"""Evaluate DOFA on masked pixels with explicit PSNR data ranges."""

import json
from pathlib import Path

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import yaml

ROOT = Path(__file__).resolve().parents[1]


def scalar(archive, key):
    if key not in archive or archive[key].ndim != 0:
        raise ValueError(f"Inference metadata {key} must be present as a scalar")
    return archive[key].item()


def display(array):
    selected = array[:3] if len(array) >= 3 else np.repeat(array[:1], 3, 0)
    selected = selected.transpose(1, 2, 0)
    return np.clip((selected - selected.min()) / max(np.ptp(selected), 1e-6), 0, 1)


def pixel_mask(patch_mask, image_size, patch_size):
    side = image_size // patch_size
    return np.repeat(np.repeat(patch_mask.reshape(side, side), patch_size, 0), patch_size, 1)


def main():
    config = yaml.safe_load((ROOT / "conf/config.yaml").read_text(encoding="utf-8"))
    input_dir = ROOT / config["paths"]["inference_dir"]
    output_dir = ROOT / config["paths"]["evaluation_dir"]
    output_dir.mkdir(parents=True, exist_ok=True)
    metrics, sources, protocols = {}, set(), set()
    for modality, modality_config in config["data"]["modalities"].items():
        path = input_dir / f"{modality}_reconstruction.npz"
        if not path.is_file():
            raise FileNotFoundError("Run inference before result evaluation")
        archive = np.load(path)
        inputs, reconstructions, masks = archive["inputs"], archive["reconstructions"], archive["masks"]
        protocol = str(scalar(archive, "protocol"))
        source = str(scalar(archive, "data_source"))
        output_modality = str(scalar(archive, "modality"))
        data_range = float(scalar(archive, "data_range"))
        expected_shape = (modality_config["channels"], config["data"]["image_size"], config["data"]["image_size"])
        if inputs.ndim != 4 or inputs.shape[1:] != expected_shape or reconstructions.shape != inputs.shape:
            raise ValueError(f"{path}: invalid reconstruction NCHW shape")
        if protocol != config["data"]["protocol"] or output_modality != modality:
            raise ValueError(f"{path}: protocol/modality metadata does not match config")
        if not np.isfinite(data_range) or data_range <= 0:
            raise ValueError(f"{path}: PSNR requires a positive data_range")
        sources.add(source)
        protocols.add(protocol)
        expanded = np.stack([pixel_mask(mask, config["data"]["image_size"],
                            config["model"]["patch_size"]) for mask in masks])[:, None]
        errors = (inputs - reconstructions)[np.broadcast_to(expanded, inputs.shape)]
        mse, mae = float(np.mean(errors**2)), float(np.mean(np.abs(errors)))
        metrics[modality] = {"masked_mse": mse, "masked_mae": mae,
                             "masked_psnr_db": float(10 * np.log10(data_range**2 / max(mse, 1e-12))),
                             "psnr_data_range": data_range, "channels": int(inputs.shape[1]),
                             "masked_fraction": float(expanded.mean()), "data_source": source,
                             "protocol": protocol}
        masked_input = inputs[0].copy()
        masked_input[:, expanded[0, 0].astype(bool)] = 0
        figure, axes = plt.subplots(1, 3, figsize=(10, 3))
        for axis, image, title in zip(axes, (inputs[0], masked_input, reconstructions[0]),
                                      ("target", "masked input", "reconstruction")):
            axis.imshow(display(image)); axis.set_title(f"{modality}: {title}"); axis.axis("off")
        figure.tight_layout()
        figure.savefig(output_dir / f"{modality}_comparison.png", dpi=120)
        plt.close(figure)
    payload = {"modalities": metrics, "data_sources": sorted(sources),
               "protocols": sorted(protocols), "protocol": config["data"]["protocol"],
               "metric_scope": "masked_pixels_only"}
    (output_dir / "metrics.json").write_text(json.dumps(payload, indent=2) + "\n")
    print(json.dumps(payload, indent=2)); print(f"evaluation={output_dir}")


if __name__ == "__main__":
    main()