import java.io.ObjectOutputStream;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.TreeMap;

import static org.apache.commons.lang.StringUtils.getLevenshteinDistance;

public class SimpleClassMetaDataRepository<T extends Attachable<T>> implements ClassMetaDataRepository<T> {
    private final Map<String, T> classes = new TreeMap<>();

    @SuppressWarnings("unchecked")

            @Override
            public Suggestion apply(ModelPath available) {
                int distance = StringUtils.getLevenshteinDistance(unavailable.toString(), available.toString());
                boolean suggest = distance <= Math.min(3, unavailable.toString().length() / 2);
                if (suggest) {

            if (kebabCasePrefixPattern.matcher(candidate).matches()) {
                kebabCasePrefixMatches.add(candidate);
                found = true;
            }
            if (!found && StringUtils.getLevenshteinDistance(normalisedPattern, candidate.toUpperCase()) <= Math.min(3, pattern.length() / 2)) {
                candidates.add(candidate);
            }
        }

        if (!caseInsensitiveMatches.isEmpty()) {

            if (kebabCasePrefixPattern.matcher(candidate).matches()) {
                kebabCasePrefixMatches.add(candidate);
                found = true;
            }
            if (!found && StringUtils.getLevenshteinDistance(normalisedPattern, candidate.toUpperCase()) <= Math.min(3, pattern.length() / 2)) {
                candidates.add(candidate);
            }
        }

        if (!caseInsensitiveMatches.isEmpty()) {

                Arrays.stream(actualText.split("\n"))
                    // Measure Levenshtein distance for each line
                    .map(line -> new LineWithDistance(line, StringUtils.getLevenshteinDistance(line, expectedText)))
                    // Filter out lines that need more edits than half the length of the line
                    .filter(pair -> pair.getDistance() < pair.getLine().length() / 2)