# pyright: basic

from __future__ import annotations

from pathlib import Path
from typing import Any

import numpy as np
import pandas as pd
import torch
from bayesian_torch.utils.util import predictive_entropy

from model.cnn import CNN3D

from .data_pipeline import build_holdout_loader
from .metrics import calibration_stats, performance_at_threshold
from .model_utils import configure_bayesian_sampling_mode
from .plotting import (
    plots_dir,
    save_clean_scan_image,
    save_noise_example_grid,
    save_metric_pair_plot,
    save_noise_metrics_plot,
)
from .runtime import write_json


def _apply_scaled_noise(
    volume: torch.Tensor, sigma: float, intensity_range: float
) -> torch.Tensor:
    # Scale by global MRI intensity range measured from holdout set.
    return volume + (torch.randn_like(volume) * sigma * intensity_range)


def _uniform_sigma_schedule(noise_sigmas: list[float]) -> list[float]:
    if not noise_sigmas:
        raise ValueError("noise_sigmas must contain at least one value")

    ordered = np.array(sorted(float(s) for s in noise_sigmas), dtype=float)
    if len(ordered) == 1:
        return [float(ordered[0])]

    uniform = np.linspace(
        float(ordered[0]), float(ordered[-1]), num=len(ordered), dtype=float
    )
    return [float(s) for s in uniform]


def _load_ensemble_models(config: dict[str, Any]) -> list[torch.nn.Module]:
    model_dir = Path(config["output"]["ensemble_path"])
    model_files = sorted(model_dir.glob("model_run_*.pt"))
    if not model_files:
        raise FileNotFoundError(f"No ensemble model files found in {model_dir}")

    models: list[torch.nn.Module] = []
    for model_file in model_files:
        model = (
            CNN3D(
                image_channels=int(config["data"]["image_channels"]),
                clin_data_channels=int(config["data"]["clin_data_channels"]),
                num_classes=int(config["data"]["num_classes"]),
                droprate=float(config["training"]["droprate"]),
            )
            .float()
            .to(config["training"]["device"])
        )
        model.load_state_dict(
            torch.load(model_file, map_location=config["training"]["device"]),
            strict=False,
        )
        model.eval()
        models.append(model)

    return models


def _load_bayesian_model(config: dict[str, Any]) -> torch.nn.Module:
    device = str(config["training"]["device"])
    try:
        from bayesian_torch.models.dnn_to_bnn import dnn_to_bnn  # type: ignore[import-untyped]
    except ImportError as e:
        raise ImportError(
            "bayesian_torch is required for bayesian noise analysis"
        ) from e

    model_path = Path(config["output"]["bayesian_path"]) / "model_bayesian.pt"
    if not model_path.exists():
        raise FileNotFoundError(f"Bayesian model checkpoint not found: {model_path}")

    model = (
        CNN3D(
            image_channels=int(config["data"]["image_channels"]),
            clin_data_channels=int(config["data"]["clin_data_channels"]),
            num_classes=int(config["data"]["num_classes"]),
            droprate=float(config["training"]["droprate"]),
        )
        .float()
        .to(config["training"]["device"])
    )

    prior_params: dict[str, float | bool | str] = {
        "prior_mu": 0.0,
        "prior_sigma": 1.0,
        "posterior_mu_init": 0.0,
        "posterior_rho_init": -3.0,
        "type": "Reparameterization",
        "moped_enable": False,
        "moped_delta": 0.5,
    }
    dnn_to_bnn(model, prior_params)
    model.to(device)
    model.load_state_dict(torch.load(model_path, map_location=device), strict=False)
    model.to(device)
    configure_bayesian_sampling_mode(model, stochastic=False)
    return model


def _infer_with_noise_ensemble(
    test_loader: torch.utils.data.DataLoader,
    models: list[torch.nn.Module],
    sigma: float,
    intensity_range: float,
    class_index: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    if not models:
        raise ValueError("No ensemble models were provided for noise inference")

    device = next(models[0].parameters()).device
    all_probs: list[float] = []
    all_confidence: list[float] = []
    all_stds: list[float] = []
    all_true: list[int] = []

    with torch.no_grad():
        for mri, xls, labels, _ in test_loader:
            mri_device = mri.float().to(device)
            xls_device = xls.float().to(device)
            labels_device = labels.to(device)
            noisy = _apply_scaled_noise(mri_device, sigma, intensity_range)
            preds = []
            for model in models:
                out = model((noisy, xls_device))
                preds.append(out[:, class_index].detach().cpu().numpy())

            pred_mat = np.stack(preds, axis=0)
            mean = pred_mat.mean(axis=0)
            confidence = np.abs(pred_mat - 0.5).mean(axis=0)
            std = pred_mat.std(axis=0)
            true = labels_device[:, class_index].detach().cpu().numpy().astype(int)

            all_probs.extend(mean.tolist())
            all_confidence.extend(confidence.tolist())
            all_stds.extend(std.tolist())
            all_true.extend(true.tolist())

    return (
        np.asarray(all_true),
        np.asarray(all_probs),
        np.asarray(all_confidence),
        np.asarray(all_stds),
    )


def _infer_with_noise_bayesian(
    test_loader: torch.utils.data.DataLoader,
    model: torch.nn.Module,
    sigma: float,
    intensity_range: float,
    class_index: int,
    mc_passes: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    device = next(model.parameters()).device
    all_probs: list[float] = []
    all_confidence: list[float] = []
    all_stds: list[float] = []
    all_true: list[int] = []

    with torch.no_grad():
        for mri, xls, labels, _ in test_loader:
            mri_device = mri.float().to(device)
            xls_device = xls.float().to(device)
            labels_device = labels.to(device)
            noisy = _apply_scaled_noise(mri_device, sigma, intensity_range)
            draws = []
            for _ in range(mc_passes):
                out = model((noisy, xls_device))
                draws.append(out.detach().cpu().numpy())

            draw_mat = np.stack(draws, axis=0)  # (mc_passes, batch, classes)
            mean = draw_mat.mean(axis=0)[:, class_index]
            confidence = np.abs(draw_mat[:, :, class_index] - 0.5).mean(axis=0)
            entropy_uncertainty = predictive_entropy(draw_mat)
            true = labels_device[:, class_index].detach().cpu().numpy().astype(int)

            all_probs.extend(mean.tolist())
            all_confidence.extend(np.asarray(confidence, dtype=float).tolist())
            all_stds.extend(np.asarray(entropy_uncertainty, dtype=float).tolist())
            all_true.extend(true.tolist())

    return (
        np.asarray(all_true),
        np.asarray(all_probs),
        np.asarray(all_confidence),
        np.asarray(all_stds),
    )


def run_noise_analysis(
    config: dict[str, Any],
    root_dir: Path,
    backend: str,
    output_dir: Path,
    class_index: int,
    noise_sigmas: list[float],
    threshold: float,
    calibration_bins: int,
    bayesian_mc_passes: int,
) -> dict[str, Any]:
    noise_sigmas = _uniform_sigma_schedule(noise_sigmas)
    test_loader = build_holdout_loader(
        config=config,
        root_dir=root_dir,
        seed=int(config["data"]["seed"]),
    )
    # intensity_min, intensity_max, intensity_range = _compute_mri_intensity_range(
    #     dataset
    # )

    # Just use a fixed intensity range for noise scaling since all that matters is that it's consistent
    intensity_range = 10_000.0

    out_plots_dir = plots_dir(output_dir)
    examples_dir = out_plots_dir / "noise_examples"
    examples_dir.mkdir(parents=True, exist_ok=True)

    rows: list[dict[str, Any]] = []
    if backend == "ensemble":
        models = _load_ensemble_models(config)
        example_rows: list[tuple[float, torch.Tensor]] = []
        for sigma in noise_sigmas:
            y_true, y_prob, y_confidence, y_std = _infer_with_noise_ensemble(
                test_loader,
                models,
                sigma,
                intensity_range,
                class_index=class_index,
            )
            perf = performance_at_threshold(y_true, y_prob, threshold)
            cal, _ = calibration_stats(y_true, y_prob, bins=calibration_bins)
            rows.append(
                {
                    "uncertainty_metric": "std",
                    "noise_factor": float(sigma),
                    "accuracy": float(perf["accuracy"]),
                    "f1": float(perf["f1"]),
                    "mce": float(cal["mce"]),
                    "mean_confidence": float(np.nanmean(y_confidence)),
                    "mean_model_output_probability": float(np.nanmean(y_prob)),
                    "mean_std": float(np.nanmean(y_std)),
                    "mean_predictive_entropy": float("nan"),
                    "mri_intensity_range": float(intensity_range),
                }
            )

        with torch.no_grad():
            sample = next(iter(test_loader))
            original_mri = sample[0]
            device = next(models[0].parameters()).device
            original_device = original_mri.float().to(device)
            for sigma in noise_sigmas:
                noisy_mri = _apply_scaled_noise(original_device, sigma, intensity_range)
                example_rows.append((float(sigma), noisy_mri.detach().cpu()))

        save_noise_example_grid(
            original_mri=original_mri,
            noisy_by_sigma=example_rows,
            output_path=examples_dir / f"{backend}_noise_examples.png",
            title=f"{backend.title()} Noise Examples",
            max_images=9,
            n_rows=2,
        )
        save_clean_scan_image(
            original_mri=original_mri,
            output_path=examples_dir / f"{backend}_clean_scan_example.png",
        )
    elif backend == "bayesian":
        model = _load_bayesian_model(config)
        example_rows = []
        for sigma in noise_sigmas:
            y_true, y_prob, y_confidence, y_std = _infer_with_noise_bayesian(
                test_loader,
                model,
                sigma,
                intensity_range,
                class_index=class_index,
                mc_passes=bayesian_mc_passes,
            )
            perf = performance_at_threshold(y_true, y_prob, threshold)
            cal, _ = calibration_stats(y_true, y_prob, bins=calibration_bins)
            rows.append(
                {
                    "uncertainty_metric": "predictive_entropy",
                    "noise_factor": float(sigma),
                    "accuracy": float(perf["accuracy"]),
                    "f1": float(perf["f1"]),
                    "mce": float(cal["mce"]),
                    "mean_confidence": float(np.nanmean(y_confidence)),
                    "mean_model_output_probability": float(np.nanmean(y_prob)),
                    # Compatibility field name retained for downstream code.
                    "mean_std": float(np.nanmean(y_std)),
                    "mean_predictive_entropy": float(np.nanmean(y_std)),
                    "mri_intensity_range": float(intensity_range),
                }
            )

        with torch.no_grad():
            sample = next(iter(test_loader))
            original_mri = sample[0]
            device = next(model.parameters()).device
            original_device = original_mri.float().to(device)
            for sigma in noise_sigmas:
                noisy_mri = _apply_scaled_noise(original_device, sigma, intensity_range)
                example_rows.append((float(sigma), noisy_mri.detach().cpu()))

        save_noise_example_grid(
            original_mri=original_mri,
            noisy_by_sigma=example_rows,
            output_path=examples_dir / f"{backend}_noise_examples.png",
            title=f"{backend.title()} Noise Examples",
            max_images=9,
            n_rows=2,
        )
        save_clean_scan_image(
            original_mri=original_mri,
            output_path=examples_dir / f"{backend}_clean_scan_example.png",
        )
    else:
        raise ValueError(f"Unsupported backend for noise analysis: {backend}")

    df = pd.DataFrame(rows).sort_values("noise_factor")
    csv_path = output_dir / "noise_sensitivity.csv"
    df.to_csv(csv_path, index=False)

    accuracy_plot_path = out_plots_dir / "noise_sensitivity_accuracy.png"
    f1_plot_path = out_plots_dir / "noise_sensitivity_f1.png"
    pair_plot_path = out_plots_dir / "noise_sensitivity_accuracy_f1.png"
    confidence_plot_path = out_plots_dir / "noise_confidence.png"
    confidence_uncertainty_pair_path = (
        out_plots_dir / "noise_confidence_predictive_uncertainty.png"
        if backend == "bayesian"
        else out_plots_dir / "noise_confidence_standard_deviation.png"
    )
    secondary_plot_name = (
        "noise_predictive_uncertainty.png"
        if backend == "bayesian"
        else "noise_standard_deviation.png"
    )
    secondary_plot_path = out_plots_dir / secondary_plot_name

    save_noise_metrics_plot(
        x=df["noise_factor"],
        y=df["accuracy"],
        legend_label="Accuracy",
        marker="o",
        x_label="Gaussian Noise Factor",
        y_label="Accuracy",
        title=f"Accuracy vs Noise ({backend})",
        output_path=accuracy_plot_path,
        plot_key="noise_sensitivity_accuracy",
    )
    save_noise_metrics_plot(
        x=df["noise_factor"],
        y=df["f1"],
        legend_label="F1",
        marker="s",
        x_label="Gaussian Noise Factor",
        y_label="F1",
        title=f"F1 vs Noise ({backend})",
        output_path=f1_plot_path,
        plot_key="noise_sensitivity_f1",
    )
    save_metric_pair_plot(
        x=df["noise_factor"],
        left_y=df["accuracy"],
        right_y=df["f1"],
        left_label="Accuracy",
        right_label="F1",
        x_label="Gaussian Noise Factor",
        y_label="Accuracy/F1",
        title=f"Accuracy and F1 vs Noise ({backend})",
        output_path=pair_plot_path,
        plot_key="noise_sensitivity_accuracy_f1",
    )

    secondary_label = (
        "Predictive Uncertainty" if backend == "bayesian" else "Standard Deviation"
    )

    save_noise_metrics_plot(
        x=df["noise_factor"],
        y=df["mean_confidence"],
        legend_label="Confidence",
        marker="o",
        x_label="Gaussian Noise Factor",
        y_label="Confidence",
        title=f"Confidence vs Noise ({backend})",
        output_path=confidence_plot_path,
        plot_key="noise_confidence",
    )
    save_noise_metrics_plot(
        x=df["noise_factor"],
        y=df["mean_std"],
        legend_label=secondary_label,
        marker="^",
        x_label="Gaussian Noise Factor",
        y_label=secondary_label,
        title=f"{secondary_label} vs Noise ({backend})",
        output_path=secondary_plot_path,
        plot_key=(
            "noise_predictive_uncertainty"
            if backend == "bayesian"
            else "noise_standard_deviation"
        ),
    )
    save_metric_pair_plot(
        x=df["noise_factor"],
        left_y=df["mean_confidence"],
        right_y=df["mean_std"],
        left_label="Confidence",
        right_label=secondary_label,
        x_label="Gaussian Noise Factor",
        y_label="Confidence/Uncertainty",
        title=f"Confidence and {secondary_label} vs Noise ({backend})",
        output_path=confidence_uncertainty_pair_path,
        plot_key=(
            "noise_confidence_predictive_uncertainty"
            if backend == "bayesian"
            else "noise_confidence_standard_deviation"
        ),
    )

    out = {
        "table": str(csv_path),
        "plots": {
            "accuracy": str(accuracy_plot_path),
            "f1": str(f1_plot_path),
            "accuracy_f1": str(pair_plot_path),
            "confidence": str(confidence_plot_path),
            (
                "confidence_predictive_uncertainty"
                if backend == "bayesian"
                else "confidence_standard_deviation"
            ): str(confidence_uncertainty_pair_path),
            (
                "predictive_uncertainty"
                if backend == "bayesian"
                else "standard_deviation"
            ): str(secondary_plot_path),
        },
        "noise_factors": noise_sigmas,
        "noise_sigmas": noise_sigmas,
        # "mri_intensity_min": float(intensity_min),
        # "mri_intensity_max": float(intensity_max),
        "mri_intensity_range": float(intensity_range),
    }
    write_json(output_dir / "noise_summary.json", out)
    return out