"""
Utilities used by app.py.

This is a Space-local subset of the project's `utils.py` — only the helpers
needed for Stage 2 fusion (image I/O, decoder loading, PSNR).
"""

import torch
import torch.nn.functional as F
from PIL import Image
from torchvision import transforms

from decoder import Decoder


def load_image(image_path: str, size: int = 224) -> torch.Tensor:
    """Load an image as a (1, 3, H, W) tensor in [0, 1]."""
    img = Image.open(image_path).convert("RGB")
    transform = transforms.Compose([
        transforms.Resize((size, size)),
        transforms.ToTensor(),
    ])
    return transform(img).unsqueeze(0)


def load_decoder(path: str, embed_dim: int = 512, device: torch.device = None) -> Decoder:
    """Load AnyAttack Decoder weights with state dict key remapping."""
    decoder = Decoder(embed_dim=embed_dim).to(device).eval()
    ckpt = torch.load(path, map_location="cpu", weights_only=False)
    state = ckpt.get("decoder_state_dict", ckpt)
    remapped = {}
    for k, v in state.items():
        k = k.removeprefix("module.")
        k = k.replace("upsample_blocks.", "blocks.")
        k = k.replace("final_conv.", "head.")
        remapped[k] = v
    decoder.load_state_dict(remapped)
    return decoder


def compute_psnr(img1: torch.Tensor, img2: torch.Tensor) -> float:
    """Compute PSNR between two image tensors in [0, 1]."""
    mse = torch.mean((img1 - img2) ** 2).item()
    if mse == 0:
        return float("inf")
    return -10 * torch.log10(torch.tensor(mse)).item()