from __future__ import annotations

import re


def extract_match_terms(text: str | None) -> list[str]:
    source = (text or "").lower()
    terms = [token for token in re.findall(r"[a-z0-9_]+", source) if len(token) >= 3]

    for chunk in re.findall(r"[\u4e00-\u9fff]+", text or ""):
        if len(chunk) >= 2:
            terms.append(chunk)
            if len(chunk) > 4:
                for index in range(len(chunk) - 1):
                    terms.append(chunk[index : index + 2])

    return list(dict.fromkeys(terms))


def score_text_match(query_text: str, *corpus_parts: str | None) -> tuple[float, list[str]]:
    query_terms = extract_match_terms(query_text)
    if not query_terms:
        return 0.0, []

    corpus = " ".join(part for part in corpus_parts if part).lower()
    matched_terms = [term for term in query_terms if term and term in corpus]
    if not matched_terms:
        return 0.0, []

    coverage = len(matched_terms) / max(len(query_terms), 1)
    density = min(len(matched_terms), 4) / 4
    return round(min(1.0, coverage * 0.7 + density * 0.3), 3), matched_terms