"""Construction du corpus synthetique : templates + PII generees + offsets gold.

Usage :
    # jeu d'evaluation (templates du pool EVAL, jamais vus en train)
    python -m bench.synth.build --split eval --n 1500 --out data/bench_v1.jsonl
    # corpus d'entrainement (hors git, regenerable a l'identique par seed)
    python -m bench.synth.build --split train --n 100000 \
        --out data/corpus_train_100k.jsonl

Chaque segment est un objet JSONL au format du harness (voir harness/score.py).
~30 % des segments recoivent un bruitage OCR a longueur constante (offsets
inchanges, flag "noise": true), ~8 % sont des negatifs purs sans PII, et les
valeurs longues (IBAN, NIR...) peuvent etre coupees par un saut de ligne
(piege OCR/PDF, longueur conservee).

Limite assumee : synthetique pur. L'eval reelle annotee main (Judilibre,
BODACC, documents fictifs relus) reste indispensable avant toute conclusion —
analyse §4.3 : "jamais du synthetique seul".
"""

import argparse
import json
import random
import re
from pathlib import Path

from bench.pii.generators import Person, PiiFactory
from bench.synth.noise import apply_noise
from bench.synth.templates import NEGATIVES, all_templates

_SLOT_RE = re.compile(r"\{([a-z_]+?)(\d+)(_v)?\}")

# prefixe de slot -> (methode factory, label ; None = distracteur non etiquete)
_SLOT_TYPES = {
    "person": "PERSON", "company": "COMPANY", "address": "ADDRESS",
    "city": "CITY", "email": "EMAIL", "phone": "PHONE", "date": "DATE",
    "date_birth": "DATE_BIRTH", "iban": "IBAN", "nir": "NIR",
    "siren": "SIREN", "siret": "SIRET", "tva": "TVA", "card": "CARD",
    "plate": "PLATE", "rg": "RG", "cadastre": "CADASTRE", "ip": "IP",
    "amount": "AMOUNT", "ref": None,
}


def _maybe_linebreak(surface: str, rng: random.Random) -> str:
    """Piege PDF/OCR : coupe une valeur longue par un saut de ligne (meme
    longueur, un espace interieur remplace par \\n)."""
    if len(surface) < 14 or " " not in surface[2:-2] or rng.random() > 0.04:
        return surface
    spaces = [i for i, c in enumerate(surface) if c == " " and 1 < i < len(surface) - 2]
    i = rng.choice(spaces)
    return surface[:i] + "\n" + surface[i + 1:]


def fill_template(template: str, factory: PiiFactory, rng: random.Random,
                  ref_label: str | None = None) -> dict:
    values: dict[tuple[str, str], object] = {}
    text_parts: list[str] = []
    entities: list[dict] = []
    pos = 0
    cursor = 0

    for m in _SLOT_RE.finditer(template):
        name, idx, variant = m.group(1), m.group(2), m.group(3)
        if name not in _SLOT_TYPES:
            raise ValueError(f"slot inconnu : {m.group(0)}")

        text_parts.append(template[cursor:m.start()])
        pos += m.start() - cursor
        cursor = m.end()

        key = (name, idx)
        if key not in values:
            values[key] = getattr(factory, name)()
        val = values[key]

        if isinstance(val, Person):
            surface = rng.choice(val.variants()[1:]) if variant else val.full
        else:
            surface = _maybe_linebreak(str(val), rng)

        etype = _SLOT_TYPES[name]
        if name == "ref" and ref_label:
            etype = ref_label
        if etype is not None:
            entities.append(
                {"start": pos, "end": pos + len(surface), "type": etype, "value": surface}
            )
        text_parts.append(surface)
        pos += len(surface)

    text_parts.append(template[cursor:])
    return {"text": "".join(text_parts), "entities": entities}


def build_corpus(n: int, seed: int, noise_share: float,
                 split: str = "all", negative_share: float = 0.08,
                 values: str = "faker", ref_label: str | None = None) -> list[dict]:
    rng = random.Random(seed)
    if values == "real":
        from bench.pii.real_factory import RealValuesFactory

        factory = RealValuesFactory(seed=seed)
    else:
        factory = PiiFactory(seed=seed)
    templates = all_templates(split)
    domains = list(templates)
    segments = []
    for i in range(n):
        if rng.random() < negative_share:
            domain = "negatif"
            seg = fill_template(rng.choice(NEGATIVES), factory, rng, ref_label=ref_label)
        else:
            domain = domains[i % len(domains)]
            seg = fill_template(rng.choice(templates[domain]), factory, rng, ref_label=ref_label)
        noised = rng.random() < noise_share
        if noised:
            seg["text"] = apply_noise(seg["text"], rng)
            for e in seg["entities"]:
                e["value"] = seg["text"][e["start"]:e["end"]]
        seg = {"id": f"{split}-{domain}-{i:06d}", "domain": domain,
               "noise": noised, **seg}
        segments.append(seg)
    return segments


def main():
    ap = argparse.ArgumentParser(description="Genere le corpus benchmark synthetique")
    ap.add_argument("--n", type=int, default=600)
    ap.add_argument("--seed", type=int, default=42)
    ap.add_argument("--noise-share", type=float, default=0.3)
    ap.add_argument("--negative-share", type=float, default=0.08)
    ap.add_argument("--split", choices=["train", "eval", "all"], default="all")
    ap.add_argument("--values", choices=["faker", "real"], default="faker",
                    help="real = pools BODACC (anti-contamination fine-tune)")
    ap.add_argument("--label-refs", action="store_true",
                    help="etiquette les {ref} en REF (convention 2026-07-29)")
    ap.add_argument("--out", default="data/bench_v0.jsonl")
    args = ap.parse_args()

    segments = build_corpus(args.n, args.seed, args.noise_share,
                            args.split, args.negative_share, args.values,
                            "REF" if args.label_refs else None)
    out = Path(args.out)
    out.parent.mkdir(parents=True, exist_ok=True)
    with out.open("w", encoding="utf-8") as f:
        for seg in segments:
            f.write(json.dumps(seg, ensure_ascii=False) + "\n")

    n_ent = sum(len(s["entities"]) for s in segments)
    n_noise = sum(1 for s in segments if s["noise"])
    print(f"{len(segments)} segments ({n_noise} bruites), {n_ent} entites -> {out}")


if __name__ == "__main__":
    main()