"""Experimental text normalizer.

The public :class:`Normalizer` class is still a stub that raises
``NotImplementedError``.  This module offers a small, opt-in normalizer used by
unit tests to exercise downstream components without touching the public API.
"""

from __future__ import annotations

import re
import unicodedata
from functools import lru_cache

# Map curly apostrophes to straight ASCII ones.
_APOSTROPHES_RE = re.compile("[\u2019\u2018\u02bc]")
# Leading/trailing punctuation to strip; internal apostrophes are preserved.
_STRIP_RE = re.compile(r"^\W+|\W+$", re.UNICODE)


class ExperimentalNormalizer:
    """Normalize and tokenize text.

    Steps:
    1. Unicode NFC normalization.
    2. Map curly apostrophes to ASCII ``'``.
    3. Lowercase.
    4. Map `, ; :` to ``_`` and `. ? !` to ``__``.
    5. Strip leading/trailing punctuation per token.
    6. Tokenize on whitespace.
    """

    def __init__(self) -> None:
        pass

    @lru_cache(maxsize=1024)  # noqa: B019 - deliberate cache on bound method
    def normalize(self, text: str) -> list[str]:
        """Return a list of normalized tokens."""
        if not text:
            return []
        s = unicodedata.normalize("NFC", text)
        s = _APOSTROPHES_RE.sub("'", s)
        s = s.lower()
        s = re.sub(r"[,;:]", " _ ", s)
        s = re.sub(r"[.?!]", " __ ", s)
        tokens: list[str] = []
        for raw in s.split():
            token = _STRIP_RE.sub("", raw)
            if token:
                tokens.append(token)
        return tokens


__all__ = ["ExperimentalNormalizer"]
