#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Train ResNet / ViT baselines (timm, ImageNet-pretrained) on one dataset.

Same data layout, augmentation, optimizer and model-selection rule as the RETFound
runs so the three models are directly comparable. After training it reloads the
best-val checkpoint, predicts the test set and saves:
    <output_dir>/<task>/checkpoint-best.pth
    <output_dir>/<task>/test_pred.npz   (y_true, y_prob)   <- consumed by evaluate.py
    <output_dir>/<task>/log.csv
Model selection score = (f1_macro + auroc + kappa)/3 on val (mirrors RETFound).
"""
import os, csv, json, math, argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import timm
from timm.data import resolve_data_config, create_transform
from torchvision import datasets, transforms
from sklearn.metrics import f1_score, roc_auc_score, cohen_kappa_score, accuracy_score

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


def build_loaders(data_path, input_size, batch_size, workers):
    train_tf = transforms.Compose([
        transforms.RandomResizedCrop(input_size, scale=(0.6, 1.0)),
        transforms.RandomHorizontalFlip(),
        transforms.ColorJitter(0.1, 0.1, 0.1),
        transforms.ToTensor(),
        transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),
    ])
    eval_tf = transforms.Compose([
        transforms.Resize(int(input_size * 1.15)),
        transforms.CenterCrop(input_size),
        transforms.ToTensor(),
        transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),
    ])
    ds = {s: datasets.ImageFolder(os.path.join(data_path, s),
                                  train_tf if s == "train" else eval_tf)
          for s in ["train", "val", "test"]}
    ld = {s: DataLoader(ds[s], batch_size=batch_size, shuffle=(s == "train"),
                        num_workers=workers, pin_memory=True, drop_last=(s == "train"))
          for s in ds}
    return ds, ld


@torch.no_grad()
def predict(model, loader, device):
    model.eval()
    probs, labels = [], []
    for x, y in loader:
        x = x.to(device, non_blocking=True)
        with torch.cuda.amp.autocast():
            out = model(x)
        probs.append(F.softmax(out.float(), 1).cpu().numpy())
        labels.append(y.numpy())
    return np.concatenate(labels), np.concatenate(probs)


def val_score(y_true, y_prob):
    C = y_prob.shape[1]
    y_pred = y_prob.argmax(1)
    f1 = f1_score(y_true, y_pred, average="macro", zero_division=0)
    kap = cohen_kappa_score(y_true, y_pred)
    try:
        if C == 2:
            auc = roc_auc_score(y_true, y_prob[:, 1])
        else:
            auc = roc_auc_score(np.eye(C)[y_true], y_prob, multi_class="ovr", average="macro")
    except Exception:
        auc = 0.0
    return (f1 + auc + kap) / 3, accuracy_score(y_true, y_pred), auc


def build_param_groups(model, weight_decay, layer_decay):
    """Split params into (weight-decay / no-decay) groups.
    If layer_decay < 1 and the model is a ViT (has .blocks), apply layer-wise lr
    decay (MAE/BeiT style): shallower layers get exponentially smaller lr via lr_scale.
    no-decay = biases, 1-D params (norms), cls_token, pos_embed."""
    blocks = getattr(model, "blocks", None)
    use_lld = (blocks is not None) and (layer_decay < 1.0)
    num_layers = (len(blocks) + 1) if blocks is not None else 1

    def layer_id(name):
        if name in ("cls_token", "pos_embed") or name.startswith("patch_embed"):
            return 0
        if name.startswith("blocks."):
            return int(name.split(".")[1]) + 1
        return num_layers

    groups = {}
    for n, p in model.named_parameters():
        if not p.requires_grad:
            continue
        no_decay = (p.ndim == 1 or n.endswith(".bias") or n in ("cls_token", "pos_embed"))
        lid = layer_id(n) if use_lld else 0
        scale = (layer_decay ** (num_layers - lid)) if use_lld else 1.0
        key = (lid, no_decay)
        if key not in groups:
            groups[key] = {"params": [], "weight_decay": 0.0 if no_decay else weight_decay,
                           "lr_scale": scale}
        groups[key]["params"].append(p)
    return list(groups.values())


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--data_path", required=True)
    ap.add_argument("--nb_classes", type=int, required=True)
    ap.add_argument("--model", required=True, help="timm name e.g. resnet50 / vit_base_patch16_224")
    ap.add_argument("--input_size", type=int, default=224)
    ap.add_argument("--batch_size", type=int, default=64)
    ap.add_argument("--epochs", type=int, default=50)
    ap.add_argument("--lr", type=float, default=5e-4)
    ap.add_argument("--weight_decay", type=float, default=0.05)
    ap.add_argument("--warmup_epochs", type=int, default=3)
    ap.add_argument("--patience", type=int, default=10)
    # ViT-friendly fine-tuning knobs (defaults OFF -> identical to old behaviour)
    ap.add_argument("--layer_decay", type=float, default=1.0, help="<1.0 enables layer-wise lr decay (ViT)")
    ap.add_argument("--drop_path", type=float, default=0.0, help="stochastic depth rate (ViT)")
    ap.add_argument("--label_smoothing", type=float, default=0.0)
    ap.add_argument("--workers", type=int, default=8)
    ap.add_argument("--output_dir", required=True)
    ap.add_argument("--task", required=True)
    args = ap.parse_args()

    out = os.path.join(args.output_dir, args.task)
    os.makedirs(out, exist_ok=True)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    torch.manual_seed(42); np.random.seed(42)

    ds, ld = build_loaders(args.data_path, args.input_size, args.batch_size, args.workers)
    print(f"[{args.task}] train={len(ds['train'])} val={len(ds['val'])} test={len(ds['test'])} "
          f"classes={ds['train'].classes}")

    model = timm.create_model(args.model, pretrained=True, num_classes=args.nb_classes,
                              drop_path_rate=args.drop_path).to(device)

    # class-weighted loss (+ optional label smoothing) from train distribution
    counts = np.bincount([y for _, y in ds["train"].samples], minlength=args.nb_classes)
    w = counts.sum() / (args.nb_classes * np.clip(counts, 1, None))
    criterion = nn.CrossEntropyLoss(weight=torch.tensor(w, dtype=torch.float32, device=device),
                                    label_smoothing=args.label_smoothing)

    groups = build_param_groups(model, args.weight_decay, args.layer_decay)
    opt = torch.optim.AdamW(groups, lr=args.lr, weight_decay=args.weight_decay)
    print(f"[{args.task}] optim groups={len(groups)} layer_decay={args.layer_decay} "
          f"drop_path={args.drop_path} ls={args.label_smoothing} lr={args.lr}")
    scaler = torch.cuda.amp.GradScaler()
    steps = len(ld["train"])

    def lr_at(ep_frac):  # warmup + cosine
        if ep_frac < args.warmup_epochs:
            return args.lr * ep_frac / max(1, args.warmup_epochs)
        p = (ep_frac - args.warmup_epochs) / max(1, args.epochs - args.warmup_epochs)
        return args.lr * 0.5 * (1 + math.cos(math.pi * min(1.0, p)))

    log_path = os.path.join(out, "log.csv")
    lf = open(log_path, "w", newline=""); lw = csv.writer(lf)
    lw.writerow(["epoch", "train_loss", "val_acc", "val_auc", "val_score", "lr"]); lf.flush()

    best_score, best_ep, since = -1, -1, 0
    ckpt = os.path.join(out, "checkpoint-best.pth")
    for ep in range(args.epochs):
        model.train(); running = 0.0
        for it, (x, y) in enumerate(ld["train"]):
            for g in opt.param_groups:
                g["lr"] = lr_at(ep + it / steps) * g.get("lr_scale", 1.0)
            x, y = x.to(device, non_blocking=True), y.to(device, non_blocking=True)
            opt.zero_grad()
            with torch.cuda.amp.autocast():
                loss = criterion(model(x), y)
            scaler.scale(loss).backward(); scaler.step(opt); scaler.update()
            running += loss.item()
        yv, pv = predict(model, ld["val"], device)
        sc, vacc, vauc = val_score(yv, pv)
        lw.writerow([ep, running / steps, vacc, vauc, sc, opt.param_groups[0]["lr"]]); lf.flush()
        print(f"[{args.task}] ep{ep} loss={running/steps:.4f} val_acc={vacc:.4f} "
              f"val_auc={vauc:.4f} score={sc:.4f}")
        if sc > best_score:
            best_score, best_ep, since = sc, ep, 0
            torch.save({"model": model.state_dict(), "epoch": ep, "val_score": sc}, ckpt)
        else:
            since += 1
            if since >= args.patience:
                print(f"[{args.task}] early stop at ep{ep} (best ep{best_ep} score={best_score:.4f})")
                break
    lf.close()

    # reload best, predict test, save raw predictions for unified evaluate.py
    state = torch.load(ckpt, map_location="cpu", weights_only=False)
    model.load_state_dict(state["model"]); model.to(device)
    yt, pt = predict(model, ld["test"], device)
    np.savez(os.path.join(out, "test_pred.npz"), y_true=yt, y_prob=pt)
    print(f"[{args.task}] DONE best_ep={best_ep} best_val_score={best_score:.4f} "
          f"-> saved test_pred.npz ({len(yt)} samples)")


if __name__ == "__main__":
    main()