import re
import unicodedata
import json

def normalize_text(text: str) -> str:
    if not text:
        return ""
    text = text.lower().strip()
    # Replace German umlauts
    text = text.replace("ä", "ae").replace("ö", "oe").replace("ü", "ue").replace("ß", "ss")
    # Remove accents
    nfkd = unicodedata.normalize("NFKD", text)
    text = "".join([c for c in nfkd if not unicodedata.combining(c)])
    # Remove special punctuation like quotes, dots, dashes, commas
    text = re.sub(r"[^\w\s]", "", text)
    # Collapse whitespace
    text = re.sub(r"\s+", " ", text).strip()
    return text

def levenshtein_distance(s1: str, s2: str) -> int:
    if len(s1) < len(s2):
        return levenshtein_distance(s2, s1)
    if len(s2) == 0:
        return len(s1)
    previous_row = list(range(len(s2) + 1))
    for i, c1 in enumerate(s1):
        current_row = [i + 1]
        for j, c2 in enumerate(s2):
            insertions = previous_row[j + 1] + 1
            deletions = current_row[j] + 1
            substitutions = previous_row[j] + (c1 != c2)
            current_row.append(min(insertions, deletions, substitutions))
        previous_row = current_row
    return previous_row[-1]

def is_answer_correct(user_input: str, canonical: str, accepted_raw: str | None = None) -> bool:
    clean_user = normalize_text(user_input)
    if not clean_user:
        return False

    targets = [normalize_text(canonical)]

    if accepted_raw:
        try:
            parsed = json.loads(accepted_raw)
            if isinstance(parsed, list):
                targets.extend([normalize_text(str(x)) for x in parsed])
        except Exception:
            # Fallback comma-separated
            for part in accepted_raw.split(","):
                part_clean = normalize_text(part)
                if part_clean:
                    targets.append(part_clean)

    for target in targets:
        if not target:
            continue
        if clean_user == target:
            return True
        # For short answers (1-3 chars, e.g. numbers or abbreviations), require exact match
        if len(target) <= 3:
            continue
        # For medium words (4-6 chars), allow max 1 typo
        max_allowed = 1 if len(target) <= 6 else 2
        dist = levenshtein_distance(clean_user, target)
        if dist <= max_allowed:
            return True

    return False