"""Bruitage OCR/typo a longueur constante.

Contrainte forte : les substitutions preservent la longueur du texte, pour que
les offsets gold restent valides sans re-mapping. C'est ce qui permet de scorer
les memes segments propres ET bruites (analyse §5.1, metrique robustesse OCR).
"""

import random
import unicodedata

# substitutions OCR classiques, 1 caractere -> 1 caractere
_OCR_SUBS = {
    "0": "O", "O": "0", "o": "0",
    "1": "l", "l": "1", "I": "1",
    "5": "S", "S": "5",
    "8": "B", "B": "8",
    "é": "e", "è": "e", "ê": "e", "à": "a", "ç": "c", "û": "u", "ô": "o",
    "É": "E", "È": "E", "À": "A", "Ç": "C",
    "m": "n", "u": "v",
}


def _strip_accent(ch: str) -> str:
    d = unicodedata.normalize("NFD", ch)
    base = d[0]
    return base if len(base) == 1 else ch


def apply_noise(text: str, rng: random.Random, rate: float = 0.03) -> str:
    """Bruite ~rate des caracteres eligibles. Longueur strictement conservee."""
    chars = list(text)
    for i, ch in enumerate(chars):
        if rng.random() >= rate:
            continue
        r = rng.random()
        if ch in _OCR_SUBS and r < 0.6:
            chars[i] = _OCR_SUBS[ch]
        elif ch.isalpha() and r < 0.8:
            chars[i] = ch.upper() if ch.islower() else ch.lower()
        elif ch.isalpha():
            chars[i] = _strip_accent(ch)
    out = "".join(chars)
    assert len(out) == len(text)
    return out