#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Split estratificado por GRUPO com val/test só do ORIGINAL e garantia de NÃO VAZAMENTO.

Lê de:
  MODELO/dataset/<WxH>/group/<grupo>/{images,masks,(masks2),(labels)}

Escreve em:
  MODELO/dataset/split/<split>/group/<grupo>/{images,masks,(masks2),(labels)}

Definições:
- Família = todas as variações da mesma base original:
    original_<base>.*
    augmented_<base>_aug_XX.*
- Val/Test: somente original_<base>.
- Train: original_<base> + todos augmented_<base>_aug_XX.

Labels:
- Ativados por config['dual_head_label'].
- Copia labels .json/.txt e .npy quando existirem.
- O pareamento é feito pelo mesmo base name da imagem.

Uso:
  python _7_split_grouped_noleak_with_labels.py
  python _7_split_grouped_noleak_with_labels.py --train 0.7 --val 0.29 --test 0.01 --seed 42
  python _7_split_grouped_noleak_with_labels.py --strict-label
  python _7_split_grouped_noleak_with_labels.py --cap-train-families "navegavel:300,naonavegavel_navegavel:800"
"""

import os
import re
import json
import shutil
import random
import argparse
from typing import Dict, List, Optional


# ===================== CONFIG =====================

with open("config.json", "r", encoding="utf-8") as f:
    config = json.load(f)

MODELO = config.get("camera")
USE_MASKS2 = bool(config.get("dual_head_mask", config.get("dual_head", False)))
USE_LABELS = bool(config.get("dual_head_label", False))
RESOLUCAO = tuple(config.get("resolucao"))

pasta_origem = os.path.join(MODELO, "dataset", f"{RESOLUCAO[0]}x{RESOLUCAO[1]}", "group")
pasta_destino = os.path.join(MODELO, "dataset", "split")

IMG_EXTS = (".jpg", ".jpeg", ".png")
MSK_EXT = ".png"
LABEL_EXTS = (".json", ".txt", ".npy")

RE_ORIGINAL_PREFIX = re.compile(r"^original_(.+)$", re.IGNORECASE)
RE_AUGMENTED_FAMILY = re.compile(r"^augmented_(.+?)(?:_aug_\d+)?$", re.IGNORECASE)
RE_AUG_SUFFIX = re.compile(r"_aug_\d+$", re.IGNORECASE)


# ===================== HELPERS =====================

def garantir(p):
    os.makedirs(p, exist_ok=True)


def lista_grupos(root):
    if not os.path.isdir(root):
        return []

    out = []
    for g in sorted(os.listdir(root)):
        gdir = os.path.join(root, g)
        if not os.path.isdir(gdir):
            continue
        if os.path.isdir(os.path.join(gdir, "images")) and os.path.isdir(os.path.join(gdir, "masks")):
            out.append(g)
    return out


def listar_imagens(img_dir):
    if not os.path.isdir(img_dir):
        return []

    fs = []
    for f in os.listdir(img_dir):
        ext = os.path.splitext(f.lower())[1]
        if ext in IMG_EXTS:
            fs.append(f)
    return sorted(fs)


def mask_from_image_name(img_name):
    base, _ = os.path.splitext(img_name)
    return base + MSK_EXT


def mask2_from_image_name(img_name):
    base, _ = os.path.splitext(img_name)
    return base + MSK_EXT


def label_candidates_from_image_name(img_name):
    base, _ = os.path.splitext(img_name)
    return [base + ext for ext in LABEL_EXTS]


def classify_source_and_family(filename_no_ext):
    """
    Retorna (source, family_key)
      source ∈ {original, augmented, unknown}
      family_key = base original sem prefixo/sufixo.
    """
    m = RE_ORIGINAL_PREFIX.match(filename_no_ext)
    if m:
        return "original", m.group(1)

    m = RE_AUGMENTED_FAMILY.match(filename_no_ext)
    if m:
        return "augmented", m.group(1)

    if RE_AUG_SUFFIX.search(filename_no_ext):
        fam = RE_AUG_SUFFIX.sub("", filename_no_ext)
        return "augmented", fam

    return "unknown", filename_no_ext


def has_any_label(label_dir: str, img_name: str) -> bool:
    if not label_dir or not os.path.isdir(label_dir):
        return False
    return any(os.path.exists(os.path.join(label_dir, cand)) for cand in label_candidates_from_image_name(img_name))


def build_family_index(img_dir, msk_dir, label_dir=None, require_label=False):
    """
    Constrói índice de famílias a partir de img_dir/msk_dir/labels.

    Retorna:
      dict family -> {
          original: str|None,
          augmented: [str],
          all: [str]
      }

    Os nomes são arquivos de imagem.
    """
    familias = {}
    imgs = listar_imagens(img_dir)

    for img_name in imgs:
        base_no_ext, _ = os.path.splitext(img_name)
        mask_name = mask_from_image_name(img_name)

        if not os.path.exists(os.path.join(msk_dir, mask_name)):
            continue

        if require_label and not has_any_label(label_dir, img_name):
            continue

        source, fam = classify_source_and_family(base_no_ext)
        d = familias.setdefault(fam, {"original": None, "augmented": [], "all": []})
        d["all"].append(img_name)

        if source == "original":
            d["original"] = img_name
        elif source == "augmented":
            d["augmented"].append(img_name)
        else:
            if d["original"] is None:
                d["original"] = img_name
            else:
                d["augmented"].append(img_name)

    return familias


def allocate_counts(n, p_train, p_val, p_test, min_train, min_val, min_test):
    n_train = int(round(n * p_train))
    n_val = int(round(n * p_val))
    n_test = n - n_train - n_val

    if n_test < 0:
        excesso = -n_test
        take_train = min(excesso, max(0, n_train))
        n_train -= take_train
        excesso -= take_train
        if excesso > 0:
            take_val = min(excesso, max(0, n_val))
            n_val -= take_val
            excesso -= take_val
        n_test = 0

    min_sum = min_train + min_val + min_test
    if n >= min_sum:
        n_train = max(n_train, min_train)
        n_val = max(n_val, min_val)
        n_test = max(n_test, min_test)

        total = n_train + n_val + n_test
        while total > n:
            if n_test > min_test:
                n_test -= 1
            elif n_val > min_val:
                n_val -= 1
            elif n_train > min_train:
                n_train -= 1
            else:
                break
            total = n_train + n_val + n_test

        while total < n:
            if n_train - min_train <= n_val - min_val:
                n_train += 1
            else:
                n_val += 1
            total = n_train + n_val + n_test
    else:
        n_train = min(n, max(1, min_train))
        resto = n - n_train
        n_val = max(0, min(resto, min_val))
        n_test = max(0, resto - n_val)

    diff = n - (n_train + n_val + n_test)
    if diff != 0:
        if diff > 0:
            take = min(diff, n - n_train)
            n_train += take
            diff -= take
            if diff > 0:
                n_val += diff
        else:
            diff = -diff
            take = min(diff, n_test)
            n_test -= take
            diff -= take
            if diff > 0:
                n_val -= diff

    return n_train, n_val, n_test


def copiar_labels_para_item(nome, src_label_dir, dst_label_dir):
    if not src_label_dir or not dst_label_dir or not os.path.isdir(src_label_dir):
        return 0

    garantir(dst_label_dir)
    copied = 0

    for cand in label_candidates_from_image_name(nome):
        src = os.path.join(src_label_dir, cand)
        if not os.path.exists(src):
            continue
        shutil.copy2(src, os.path.join(dst_label_dir, cand))
        copied += 1

    return copied


def copiar(
    nomes,
    src_img_dir,
    src_msk_dir,
    dst_img_dir,
    dst_msk_dir,
    src_msk2_dir=None,
    dst_msk2_dir=None,
    src_label_dir=None,
    dst_label_dir=None,
    strict_label=False,
):
    garantir(dst_img_dir)
    garantir(dst_msk_dir)

    use_msk2 = bool(src_msk2_dir and dst_msk2_dir and os.path.isdir(src_msk2_dir))
    use_labels = bool(src_label_dir and dst_label_dir and os.path.isdir(src_label_dir))

    if use_msk2:
        garantir(dst_msk2_dir)
    if use_labels:
        garantir(dst_label_dir)

    moved = 0
    skipped_no_label = 0

    for nome in nomes:
        mask_name = mask_from_image_name(nome)
        src_img = os.path.join(src_img_dir, nome)
        src_msk = os.path.join(src_msk_dir, mask_name)

        if not (os.path.exists(src_img) and os.path.exists(src_msk)):
            continue

        if strict_label and use_labels and not has_any_label(src_label_dir, nome):
            skipped_no_label += 1
            continue

        shutil.copy2(src_img, os.path.join(dst_img_dir, nome))
        shutil.copy2(src_msk, os.path.join(dst_msk_dir, mask_name))

        if use_msk2:
            m2_name = mask2_from_image_name(nome)
            src_m2 = os.path.join(src_msk2_dir, m2_name)
            if os.path.exists(src_m2):
                shutil.copy2(src_m2, os.path.join(dst_msk2_dir, m2_name))

        if use_labels:
            copied = copiar_labels_para_item(nome, src_label_dir, dst_label_dir)
            if strict_label and copied == 0:
                skipped_no_label += 1
                continue

        moved += 1

    if skipped_no_label > 0:
        print(f"[WARN] {skipped_no_label} itens pulados por falta de label.")

    return moved


# ===================== SPLIT =====================

def split_group(group_name, p_train, p_val, p_test, seed, mins, caps_map=None, strict_label=False):
    src_img_dir = os.path.join(pasta_origem, group_name, "images")
    src_msk_dir = os.path.join(pasta_origem, group_name, "masks")
    src_msk2_dir = os.path.join(pasta_origem, group_name, "masks2")
    src_label_dir = os.path.join(pasta_origem, group_name, "labels")

    use_msk2 = USE_MASKS2 and os.path.isdir(src_msk2_dir)
    use_labels = USE_LABELS and os.path.isdir(src_label_dir)

    if USE_LABELS and not use_labels:
        msg = f"[{group_name}] dual_head_label=true, mas labels/ não existe."
        if strict_label:
            print(f"[WARN] {msg} Pulando grupo.")
            return {"train": 0, "val": 0, "test": 0, "familias": 0}
        print(f"[WARN] {msg} Split seguirá sem copiar labels.")

    familias = build_family_index(
        src_img_dir,
        src_msk_dir,
        label_dir=src_label_dir if use_labels else None,
        require_label=bool(strict_label and use_labels),
    )

    familias_originais = [fam for fam, d in familias.items() if d["original"] is not None]
    total_familias = len(familias_originais)

    if total_familias == 0:
        print(f"[{group_name}] 0 famílias com original, pulando.")
        return {"train": 0, "val": 0, "test": 0, "familias": 0}

    rng = random.Random(seed)
    rng.shuffle(familias_originais)

    n_tr, n_va, n_te = allocate_counts(
        total_familias,
        p_train,
        p_val,
        p_test,
        mins["train"],
        mins["val"],
        mins["test"],
    )

    fam_train = set(familias_originais[:n_tr])
    fam_val = set(familias_originais[n_tr : n_tr + n_va])
    fam_test = set(familias_originais[n_tr + n_va : n_tr + n_va + n_te])

    if caps_map and group_name in caps_map:
        cap = caps_map[group_name]
        if len(fam_train) > cap:
            fam_list = list(fam_train)
            rng.shuffle(fam_list)
            kept = set(fam_list[:cap])
            dropped = set(fam_list[cap:])
            fam_train = kept
            print(
                f"[{group_name}] cap-train-families={cap} → "
                f"mantidas {len(kept)} famílias, descartadas {len(dropped)} do TRAIN"
            )

    nomes_train, nomes_val, nomes_test = [], [], []

    for fam, d in familias.items():
        if fam in fam_train:
            if d["original"]:
                nomes_train.append(d["original"])
            if d["augmented"]:
                nomes_train.extend(d["augmented"])
        elif fam in fam_val:
            if d["original"]:
                nomes_val.append(d["original"])
        elif fam in fam_test:
            if d["original"]:
                nomes_test.append(d["original"])

    dest_train_img = os.path.join(pasta_destino, "train", "group", group_name, "images")
    dest_train_msk = os.path.join(pasta_destino, "train", "group", group_name, "masks")
    dest_val_img = os.path.join(pasta_destino, "val", "group", group_name, "images")
    dest_val_msk = os.path.join(pasta_destino, "val", "group", group_name, "masks")
    dest_test_img = os.path.join(pasta_destino, "test", "group", group_name, "images")
    dest_test_msk = os.path.join(pasta_destino, "test", "group", group_name, "masks")

    dest_train_msk2 = os.path.join(pasta_destino, "train", "group", group_name, "masks2") if use_msk2 else None
    dest_val_msk2 = os.path.join(pasta_destino, "val", "group", group_name, "masks2") if use_msk2 else None
    dest_test_msk2 = os.path.join(pasta_destino, "test", "group", group_name, "masks2") if use_msk2 else None

    dest_train_label = os.path.join(pasta_destino, "train", "group", group_name, "labels") if use_labels else None
    dest_val_label = os.path.join(pasta_destino, "val", "group", group_name, "labels") if use_labels else None
    dest_test_label = os.path.join(pasta_destino, "test", "group", group_name, "labels") if use_labels else None

    m_train = copiar(
        nomes_train,
        src_img_dir,
        src_msk_dir,
        dest_train_img,
        dest_train_msk,
        src_msk2_dir,
        dest_train_msk2,
        src_label_dir,
        dest_train_label,
        strict_label=strict_label,
    )
    m_val = copiar(
        nomes_val,
        src_img_dir,
        src_msk_dir,
        dest_val_img,
        dest_val_msk,
        src_msk2_dir,
        dest_val_msk2,
        src_label_dir,
        dest_val_label,
        strict_label=strict_label,
    )
    m_test = copiar(
        nomes_test,
        src_img_dir,
        src_msk_dir,
        dest_test_img,
        dest_test_msk,
        src_msk2_dir,
        dest_test_msk2,
        src_label_dir,
        dest_test_label,
        strict_label=strict_label,
    )

    print(
        f"[{group_name}] famílias={total_familias} → "
        f"train(imgs)={m_train}, val(imgs)={m_val}, test(imgs)={m_test}"
    )
    return {"train": m_train, "val": m_val, "test": m_test, "familias": total_familias}


# ===================== MAIN =====================

def parse_cap_map(s):
    caps = {}
    if not s:
        return caps
    for item in s.split(","):
        if not item.strip():
            continue
        k, v = item.strip().split(":")
        caps[k.strip()] = int(v)
    return caps


def main():
    ap = argparse.ArgumentParser(description="Split estratificado por grupo sem vazamento, com suporte a labels.")
    ap.add_argument("--train", type=float, default=0.70, help="Proporção de treino.")
    ap.add_argument("--val", type=float, default=0.29, help="Proporção de validação.")
    ap.add_argument("--test", type=float, default=0.01, help="Proporção de teste.")
    ap.add_argument("--seed", type=int, default=42, help="Seed do embaralhamento.")

    ap.add_argument("--min-train", type=int, default=1, help="Mínimo de famílias por grupo em train.")
    ap.add_argument("--min-val", type=int, default=1, help="Mínimo de famílias por grupo em val.")
    ap.add_argument("--min-test", type=int, default=0, help="Mínimo de famílias por grupo em test.")

    ap.add_argument("--modelo", type=str, default=None, help="Sobrescreve MODELO do config.json.")
    ap.add_argument("--resolucao", type=str, default=None, help="Sobrescreve resolução no formato WxH.")

    ap.add_argument(
        "--cap-train-families",
        type=str,
        default="",
        help="Mapa grupo:cap para limitar famílias no TRAIN. Ex: 'navegavel:350'",
    )
    ap.add_argument(
        "--strict-label",
        action="store_true",
        help="Se dual_head_label=true e faltar label, pula item/grupo.",
    )

    args = ap.parse_args()

    modelo = args.modelo or MODELO
    if args.resolucao:
        try:
            w, h = args.resolucao.lower().split("x")
            resolucao = (int(w), int(h))
        except Exception:
            resolucao = RESOLUCAO
    else:
        resolucao = RESOLUCAO

    caps_map = parse_cap_map(args.cap_train_families)

    global pasta_origem, pasta_destino
    pasta_origem = os.path.join(modelo, "dataset", f"{resolucao[0]}x{resolucao[1]}", "group")
    pasta_destino = os.path.join(modelo, "dataset", "split")

    soma = args.train + args.val + args.test
    if soma <= 0:
        raise ValueError("Soma de proporções deve ser > 0.")

    p_train = args.train / soma
    p_val = args.val / soma
    p_test = args.test / soma

    mins = {
        "train": max(0, args.min_train),
        "val": max(0, args.min_val),
        "test": max(0, args.min_test),
    }

    garantir(pasta_destino)

    grupos = lista_grupos(pasta_origem)
    if not grupos:
        print(f"[WARN] Nenhum grupo encontrado em: {pasta_origem}")
        return

    random.seed(args.seed)

    total_global = {"train": 0, "val": 0, "test": 0, "familias": 0}

    print(f"[INFO] Origem: {pasta_origem}")
    print(f"[INFO] Destino: {pasta_destino}")
    print(f"[INFO] dual_head_mask/masks2: {USE_MASKS2}")
    print(f"[INFO] dual_head_label/labels: {USE_LABELS}")
    print(f"Grupos: {', '.join(grupos)}")
    print(f"Proporções normalizadas: train={p_train:.3f}, val={p_val:.3f}, test={p_test:.3f}")
    print(f"Mínimos por grupo: train={mins['train']} val={mins['val']} test={mins['test']}")

    for g in grupos:
        res = split_group(g, p_train, p_val, p_test, args.seed, mins, caps_map=caps_map, strict_label=args.strict_label)
        for k in total_global.keys():
            total_global[k] += res.get(k, 0)

    print("\nResumo global, imagens copiadas:")
    print(f"  train: {total_global['train']}")
    print(f"  val:   {total_global['val']}")
    print(f"  test:  {total_global['test']}")
    print(f"  famílias total: {total_global['familias']}")
    print("\n✅ Split sem vazamento concluído!")


if __name__ == "__main__":
    main()