import re
import string
from lifelines.utils import concordance_index


def keep_integers_commas_spaces(input_string):
    cleaned_string = re.sub(r'[^0-9\s,]', '', str(input_string))
    return cleaned_string


def normalize_answer(s):
    """Lower text and remove punctuation, articles and extra whitespace."""

    def remove_punc(text):
        exclude = set(string.punctuation)
        return "".join(ch for ch in text if ch not in exclude)

    normalized_list = keep_integers_commas_spaces(s).replace(",", " ").strip(string.punctuation).split()
    try:
        normalized_list = [int(remove_punc(x).strip()) for x in normalized_list]
    except ValueError:
        return []
    return normalized_list


def concordant_index_score(prediction, ground_truth):
    normalized_prediction = normalize_answer(prediction)
    normalized_ground_truth = normalize_answer(ground_truth)
    if sorted(normalized_ground_truth) != sorted(normalized_prediction):
        return 0.0

    pred_order = summ_id_per_location_to_pos_of_id(normalized_prediction)
    gold_order = summ_id_per_location_to_pos_of_id(normalized_ground_truth)

    return concordance_index(gold_order, pred_order)


def summ_id_per_location_to_pos_of_id(id_per_location):
    order = [-1] * len(id_per_location)
    for i, id_ in enumerate(id_per_location, 1):
        order[id_ - 1] = i
    return order


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_concordance_index(predictions, references):
    concordant_index = 0
    for prediction, ground_truths in zip(predictions, references):
        concordant_index += metric_max_over_ground_truths(concordant_index_score, prediction, ground_truths)
    return 100.0 * concordant_index / len(predictions)