#!/usr/bin/env python3
import argparse
import json
import os
import sys
import time
from pathlib import Path

import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from PIL import Image, ImageFile
from sklearn.metrics import roc_auc_score
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.models import create_model
from torchvision import transforms

ImageFile.LOAD_TRUNCATED_IMAGES = True

IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
BENCHMARKS = [
    ("DeepFakeDetection", "DeepFakeDetection"),
    ("CDFv2", "CDFv2"),
    ("Wild_Deepfake", "Wild_Deepfake"),
    ("ForgeryNet", "ForgeryNet"),
    ("DeepFakeFace", "DeepFakeFace"),
    ("DF40", "DF40"),
    ("ScaleDF", "ScaleDF"),
]


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--benchmark-root", required=True)
    parser.add_argument("--checkpoint", required=True)
    parser.add_argument("--output-dir", required=True)
    parser.add_argument("--manifest", default="")
    parser.add_argument("--code-root", default="/tmp/ScaleDF_repro_20260724/code/deit")
    parser.add_argument("--model", default="deit_huge_patch14_LS")
    parser.add_argument("--input-size", type=int, default=224)
    parser.add_argument("--batch-size", type=int, default=256)
    parser.add_argument("--num-workers", type=int, default=8)
    parser.add_argument("--print-freq", type=int, default=50)
    parser.add_argument("--max-samples-per-class", type=int, default=0)
    parser.add_argument("--rebuild-manifest", action="store_true")
    parser.add_argument("--skip-load-errors", action="store_true")
    return parser.parse_args()


def init_distributed():
    if "RANK" not in os.environ:
        return 0, 1, 0, torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    rank = int(os.environ["RANK"])
    world_size = int(os.environ["WORLD_SIZE"])
    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
    torch.cuda.set_device(local_rank)
    dist.init_process_group("nccl", init_method="env://")
    return rank, world_size, local_rank, torch.device(f"cuda:{local_rank}")


def is_main(rank):
    return rank == 0


def iter_images(root):
    for dirpath, dirnames, filenames in os.walk(root):
        dirnames.sort()
        for name in sorted(filenames):
            if Path(name).suffix.lower() in IMAGE_EXTS:
                yield os.path.join(dirpath, name)


def build_manifest(args, manifest_path):
    root = Path(args.benchmark_root)
    tmp_path = manifest_path.with_suffix(manifest_path.suffix + ".tmp")
    stats = []
    with tmp_path.open("w") as f:
        for bench_id, (bench_name, rel_name) in enumerate(BENCHMARKS):
            val_root = root / rel_name / "val"
            real_dir = val_root / "000real_faces"
            fake_dir = val_root / "fake"
            counts = {"real": 0, "fake": 0}
            for label, label_name, label_dir in [(0, "real", real_dir), (1, "fake", fake_dir)]:
                if not label_dir.exists():
                    raise FileNotFoundError(f"Missing {label_name} directory for {bench_name}: {label_dir}")
                for path in iter_images(label_dir):
                    f.write(f"{bench_id}\t{label}\t{path}\n")
                    counts[label_name] += 1
                    if args.max_samples_per_class and counts[label_name] >= args.max_samples_per_class:
                        break
            stats.append({
                "benchmark": bench_name,
                "real": counts["real"],
                "fake": counts["fake"],
                "total": counts["real"] + counts["fake"],
            })
    os.replace(tmp_path, manifest_path)
    with manifest_path.with_suffix(".stats.json").open("w") as f:
        json.dump({"benchmarks": stats, "total": sum(x["total"] for x in stats)}, f, indent=2)


class ManifestShardDataset(torch.utils.data.Dataset):
    def __init__(self, manifest_path, rank, world_size, transform, input_size, skip_load_errors):
        self.transform = transform
        self.input_size = input_size
        self.skip_load_errors = skip_load_errors
        self.samples = []
        with open(manifest_path, "r") as f:
            for idx, line in enumerate(f):
                if idx % world_size != rank:
                    continue
                bench_id, label, path = line.rstrip("\n").split("\t", 2)
                self.samples.append((path, int(label), int(bench_id)))

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, index):
        path, label, bench_id = self.samples[index]
        try:
            with Image.open(path) as img:
                image = img.convert("RGB")
                image = self.transform(image)
        except Exception as exc:
            print(f"LOAD_ERROR\t{path}\t{type(exc).__name__}: {exc}", file=sys.stderr, flush=True)
            if not self.skip_load_errors:
                raise
            image = torch.zeros(3, self.input_size, self.input_size)
        return image, label, bench_id


def build_transform(input_size):
    return transforms.Compose([
        transforms.Resize(input_size, interpolation=transforms.InterpolationMode.BICUBIC),
        transforms.CenterCrop(input_size),
        transforms.ToTensor(),
        transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
    ])


def load_model(args, device):
    sys.path.insert(0, args.code_root)
    import models  # noqa: F401
    import models_v2  # noqa: F401

    model = create_model(
        args.model,
        pretrained=False,
        num_classes=2,
        drop_rate=0.0,
        drop_path_rate=0.2,
        drop_block_rate=None,
        img_size=args.input_size,
    )
    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
    state = checkpoint["model"] if isinstance(checkpoint, dict) and "model" in checkpoint else checkpoint
    missing, unexpected = model.load_state_dict(state, strict=False)
    if len(missing) > 10 or len(unexpected) > 10:
        raise RuntimeError(f"Too many load_state_dict mismatches: missing={len(missing)} unexpected={len(unexpected)}")
    model.to(device).eval()
    return model, {"missing": missing, "unexpected": unexpected}


@torch.no_grad()
def run_inference(args, rank, world_size, device, manifest_path, output_dir):
    dataset = ManifestShardDataset(
        manifest_path,
        rank,
        world_size,
        build_transform(args.input_size),
        args.input_size,
        args.skip_load_errors,
    )
    loader = torch.utils.data.DataLoader(
        dataset,
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.num_workers,
        pin_memory=True,
        persistent_workers=args.num_workers > 0,
        prefetch_factor=4 if args.num_workers > 0 else None,
    )
    model, load_info = load_model(args, device)
    scores, labels, benches = [], [], []
    start = time.time()
    seen = 0
    for step, (images, y, b) in enumerate(loader):
        images = images.to(device, non_blocking=True)
        with torch.cuda.amp.autocast(enabled=device.type == "cuda"):
            logits = model(images)
            score = F.softmax(logits.float(), dim=1)[:, 1]
        scores.append(score.detach().cpu().numpy().astype(np.float32))
        labels.append(y.numpy().astype(np.uint8))
        benches.append(b.numpy().astype(np.uint8))
        seen += len(y)
        if args.print_freq and step % args.print_freq == 0:
            elapsed = max(time.time() - start, 1e-6)
            print(
                f"rank={rank} step={step} seen={seen}/{len(dataset)} "
                f"rate={seen / elapsed:.1f} img/s",
                flush=True,
            )
    shard_path = output_dir / f"shard_rank{rank:05d}.npz"
    np.savez_compressed(
        shard_path,
        scores=np.concatenate(scores) if scores else np.empty((0,), dtype=np.float32),
        labels=np.concatenate(labels) if labels else np.empty((0,), dtype=np.uint8),
        benches=np.concatenate(benches) if benches else np.empty((0,), dtype=np.uint8),
    )
    with (output_dir / f"rank{rank:05d}.done.json").open("w") as f:
        json.dump({"rank": rank, "world_size": world_size, "samples": seen, "load_info": load_info}, f)
    print(f"rank={rank} wrote={shard_path} samples={seen}", flush=True)


def aggregate(output_dir):
    shard_paths = sorted(output_dir.glob("shard_rank*.npz"))
    scores, labels, benches = [], [], []
    for path in shard_paths:
        arr = np.load(path)
        scores.append(arr["scores"])
        labels.append(arr["labels"])
        benches.append(arr["benches"])
    scores = np.concatenate(scores)
    labels = np.concatenate(labels)
    benches = np.concatenate(benches)
    rows = []
    for bench_id, (bench_name, _) in enumerate(BENCHMARKS):
        mask = benches == bench_id
        y = labels[mask]
        s = scores[mask]
        auc = float("nan") if len(np.unique(y)) < 2 else float(roc_auc_score(y, s))
        rows.append({
            "benchmark": bench_name,
            "n": int(mask.sum()),
            "real": int((y == 0).sum()),
            "fake": int((y == 1).sum()),
            "auc": auc,
        })
    mean_auc = float(np.nanmean([r["auc"] for r in rows]))
    with (output_dir / "auc_summary.tsv").open("w") as f:
        f.write("benchmark\tn\treal\tfake\tauc\n")
        for r in rows:
            f.write(f"{r['benchmark']}\t{r['n']}\t{r['real']}\t{r['fake']}\t{r['auc']:.8f}\n")
        f.write(f"MEAN_7_BENCHMARKS\t{sum(r['n'] for r in rows)}\t{sum(r['real'] for r in rows)}\t{sum(r['fake'] for r in rows)}\t{mean_auc:.8f}\n")
    with (output_dir / "auc_summary.json").open("w") as f:
        json.dump({"benchmarks": rows, "mean_auc": mean_auc, "num_shards": len(shard_paths)}, f, indent=2)
    print(f"MEAN_7_BENCHMARKS_AUC {mean_auc:.8f}", flush=True)


def main():
    args = parse_args()
    rank, world_size, local_rank, device = init_distributed()
    output_dir = Path(args.output_dir)
    if is_main(rank):
        output_dir.mkdir(parents=True, exist_ok=True)
    if dist.is_available() and dist.is_initialized():
        dist.barrier()

    manifest_path = Path(args.manifest) if args.manifest else output_dir / "benchmark_manifest.tsv"
    if is_main(rank) and (args.rebuild_manifest or not manifest_path.exists()):
        print(f"building_manifest path={manifest_path}", flush=True)
        build_manifest(args, manifest_path)
        print(f"manifest_ready path={manifest_path}", flush=True)
    if dist.is_available() and dist.is_initialized():
        dist.barrier()

    if is_main(rank):
        with (output_dir / "run_config.json").open("w") as f:
            json.dump(vars(args) | {"world_size": world_size}, f, indent=2)

    run_inference(args, rank, world_size, device, manifest_path, output_dir)
    if dist.is_available() and dist.is_initialized():
        dist.barrier()
    if is_main(rank):
        aggregate(output_dir)
    if dist.is_available() and dist.is_initialized():
        dist.barrier()


if __name__ == "__main__":
    main()