"""
Visualization utilities for dataset inspection and debugging.
"""

from collections import Counter

import matplotlib.pyplot as plt
import numpy as np
import torch


def denormalize_image(img_tensor, mean, std):
    """
    Denormalize a tensor image with mean and std.

    Args:
        img_tensor: normalized image tensor (C, H, W)
        mean: mean used for normalization
        std: std used for normalization

    Returns:
        numpy array: denormalized image (H, W, C) in [0, 1] range
    """
    mean = torch.tensor(mean).view(3, 1, 1)
    std = torch.tensor(std).view(3, 1, 1)

    # Denormalize
    img = img_tensor * std + mean

    # Clip to [0, 1] and convert to numpy
    img = torch.clamp(img, 0, 1)
    img = img.permute(1, 2, 0).cpu().numpy()

    return img


def show_batch(dataloader, class_names, num_images=8, denorm=True):
    """
    Display a batch of images with their labels.

    Args:
        dataloader: PyTorch DataLoader
        class_names: list of class names
        num_images: number of images to display (default: 8)
        denorm: whether to denormalize images (default: True)
    """
    batch = next(iter(dataloader))

    if isinstance(batch, dict):
        images = batch["image"][:num_images]
        labels = batch["label"][:num_images]
        modalities = batch.get("modality", ["unknown"] * num_images)[:num_images]

    elif isinstance(batch, (list, tuple)):
        images, labels = batch[:2]
        images = images[:num_images]
        labels = labels[:num_images]
        modalities = ["unknown"] * len(labels)

    else:
        raise TypeError(f"Unexpected batch type: {type(batch)}")

    # Determine grid size
    cols = 4
    rows = (num_images + cols - 1) // cols

    fig, axes = plt.subplots(rows, cols, figsize=(12, 3 * rows))
    axes = axes.flatten() if num_images > 1 else [axes]

    # ImageNet normalization (used for color images)
    IMAGENET_MEAN = [0.485, 0.456, 0.406]
    IMAGENET_STD = [0.229, 0.224, 0.225]

    for idx in range(len(axes)):
        ax = axes[idx]

        if idx < len(images):
            img = images[idx]

            # Denormalize if requested
            if denorm:
                # Try ImageNet normalization first
                img = denormalize_image(img, IMAGENET_MEAN, IMAGENET_STD)
            else:
                img = img.permute(1, 2, 0).cpu().numpy()
                img = np.clip(img, 0, 1)

            ax.imshow(img)

            label_name = class_names[labels[idx].item()]
            modality = (
                modalities[idx] if isinstance(modalities[idx], str) else modalities[idx]
            )
            ax.set_title(f"{label_name}\n({modality})", fontsize=9)
            ax.axis("off")
        else:
            ax.axis("off")

    plt.tight_layout()
    plt.show()


def plot_class_distribution(samples, class_names, title="Class Distribution"):
    """
    Plot bar chart of class distribution.

    Args:
        samples: list of (img_path, label_id, modality_name) tuples
        class_names: list of class names
        title: plot title
    """
    labels = [s[1] for s in samples]
    counts = Counter(labels)

    # Sort by class ID
    sorted_counts = [counts.get(i, 0) for i in range(len(class_names))]

    plt.figure(figsize=(15, 5))
    bars = plt.bar(range(len(class_names)), sorted_counts, color="steelblue", alpha=0.7)

    # Highlight min and max
    min_idx = np.argmin(sorted_counts)
    max_idx = np.argmax(sorted_counts)
    bars[min_idx].set_color("red")
    bars[max_idx].set_color("green")

    plt.xlabel("Class", fontsize=12)
    plt.ylabel("Number of Samples", fontsize=12)
    plt.title(title, fontsize=14, fontweight="bold")
    plt.xticks(range(len(class_names)), class_names, rotation=90, fontsize=8)
    plt.grid(axis="y", alpha=0.3)

    # Add statistics
    plt.text(
        0.02,
        0.98,
        f"Min: {min(sorted_counts)} (red)\nMax: {max(sorted_counts)} (green)\n"
        f"Mean: {np.mean(sorted_counts):.1f}\nImbalance: {max(sorted_counts)/min(sorted_counts):.2f}x",
        transform=plt.gca().transAxes,
        fontsize=10,
        verticalalignment="top",
        bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.5),
    )

    plt.tight_layout()
    plt.show()


def plot_split_distribution(train, val, test, class_names):
    """
    Plot class distribution across train, validation, and test splits.

    Args:
        train: list of training samples
        val: list of validation samples
        test: list of test samples
        class_names: list of class names
    """
    train_labels = [s[1] for s in train]
    val_labels = [s[1] for s in val]
    test_labels = [s[1] for s in test]

    train_counts = Counter(train_labels)
    val_counts = Counter(val_labels)
    test_counts = Counter(test_labels)

    # Prepare data
    num_classes = len(class_names)
    train_dist = [train_counts.get(i, 0) for i in range(num_classes)]
    val_dist = [val_counts.get(i, 0) for i in range(num_classes)]
    test_dist = [test_counts.get(i, 0) for i in range(num_classes)]

    # Plot
    x = np.arange(num_classes)
    width = 0.25

    fig, ax = plt.subplots(figsize=(15, 5))

    ax.bar(x - width, train_dist, width, label="Train", alpha=0.8)
    ax.bar(x, val_dist, width, label="Val", alpha=0.8)
    ax.bar(x + width, test_dist, width, label="Test", alpha=0.8)

    ax.set_xlabel("Class")
    ax.set_ylabel("Number of Samples")
    ax.set_title("Class Distribution Across Splits")
    ax.set_xticks(x)
    ax.set_xticklabels(class_names, rotation=90, fontsize=8)
    ax.legend()
    ax.grid(axis="y", alpha=0.3)

    plt.tight_layout()
    plt.show()


def plot_modality_distribution(samples, modalities):
    """
    Plot distribution of samples across different modalities.

    Args:
        samples: list of (img_path, label_id, modality_name) tuples
        modalities: list of modality names
    """
    modality_labels = [s[2] for s in samples]
    modality_counts = Counter(modality_labels)

    # Sort by modality order
    counts = [modality_counts.get(m, 0) for m in modalities]

    plt.figure(figsize=(8, 5))
    bars = plt.bar(
        modalities, counts, color=["#1f77b4", "#ff7f0e", "#2ca02c"], alpha=0.7
    )

    plt.xlabel("Modality", fontsize=12)
    plt.ylabel("Number of Samples", fontsize=12)
    plt.title("Sample Distribution by Modality", fontsize=14, fontweight="bold")
    plt.grid(axis="y", alpha=0.3)

    # Add count labels on bars
    for bar, count in zip(bars, counts):
        height = bar.get_height()
        plt.text(
            bar.get_x() + bar.get_width() / 2.0,
            height,
            f"{count:,}",
            ha="center",
            va="bottom",
            fontsize=11,
            fontweight="bold",
        )

    plt.tight_layout()
    plt.show()


def _extract_image_and_label(sample):
    """
    Helper to pull (image, label) from either:
    - a tuple/list: (image, label)
    - a dict: {'image': ..., 'label': ...} or similar
    """
    # Tuple / list: (image, label, ...) is how MultiModalityDataset works
    if isinstance(sample, (list, tuple)):
        if len(sample) < 2:
            raise ValueError(
                f"Expected at least (image, label) in sample, got length {len(sample)}"
            )
        img, label = sample[0], sample[1]
        return img, int(label) if hasattr(label, "item") else label

    # Dict-based sample (for other Hugging Face style datasets)
    if isinstance(sample, dict):
        # Try common key patterns for image
        img_key = None
        for key in ["image", "img", "pixel_values"]:
            if key in sample:
                img_key = key
                break
        if img_key is None:
            raise KeyError(
                f"Could not find image key in sample dict. Keys: {list(sample.keys())}"
            )

        # Try common key patterns for label
        label_key = None
        for key in ["label", "labels", "target", "y", "class"]:
            if key in sample:
                label_key = key
                break
        if label_key is None:
            raise KeyError(
                f"Could not find label key in sample dict. Keys: {list(sample.keys())}"
            )

        img = sample[img_key]
        label = sample[label_key]
        return img, int(label) if hasattr(label, "item") else label

    raise TypeError(f"Unsupported sample type in compare_augmentations: {type(sample)}")


def compare_augmentations(
    dataset_original, dataset_augmented, class_names, idx=0, num_versions=5
):
    """
    Compare original and augmented versions of the same image.

    Args:
        dataset_original: dataset WITHOUT augmentation
        dataset_augmented: dataset WITH augmentation (same underlying data, but transforms include augmentations)
        class_names: list of class names (index -> name)
        idx: index of the sample to visualize
        num_versions: how many augmented variants to show
    """
    fig, axes = plt.subplots(2, num_versions, figsize=(3 * num_versions, 6))

    # Make axes indexable even when num_versions == 1
    if num_versions == 1:
        axes = np.array([[axes[0]], [axes[1]]])

    # Original sample (no augmentation)
    orig_sample = dataset_original[idx]
    orig_img, label = _extract_image_and_label(orig_sample)

    # ImageNet normalization (matches data.transforms for color images)
    IMAGENET_MEAN = [0.485, 0.456, 0.406]
    IMAGENET_STD = [0.229, 0.224, 0.225]

    # Show original multiple times (top row)
    for col in range(num_versions):
        img_denorm = denormalize_image(orig_img, IMAGENET_MEAN, IMAGENET_STD)
        ax = axes[0, col]
        ax.imshow(img_denorm)
        if col == 0:
            ax.set_title("Original", fontsize=10)
        ax.axis("off")

    # Show augmented versions (bottom row)
    for col in range(num_versions):
        aug_sample = dataset_augmented[idx]  # same index, new random augmentation
        aug_img, _ = _extract_image_and_label(aug_sample)
        img_denorm = denormalize_image(aug_img, IMAGENET_MEAN, IMAGENET_STD)
        ax = axes[1, col]
        ax.imshow(img_denorm)
        if col == 0:
            ax.set_title("Augmented", fontsize=10)
        ax.axis("off")

    class_name = (
        class_names[label] if 0 <= label < len(class_names) else f"class {label}"
    )
    fig.suptitle(
        f"Augmentation Comparison: {class_name}", fontsize=14, fontweight="bold"
    )
    plt.tight_layout()
    plt.show()


def visualize_sample_images(
    samples, class_names, num_classes_to_show=5, samples_per_class=3
):
    """
    Show sample images from multiple classes.

    Args:
        samples: list of (img_path, label_id, modality_name) tuples
        class_names: list of class names
        num_classes_to_show: number of classes to visualize
        samples_per_class: number of samples per class
    """
    from PIL import Image

    # Group samples by class
    class_samples = {}
    for sample in samples:
        label = sample[1]
        if label not in class_samples:
            class_samples[label] = []
        class_samples[label].append(sample)

    # Select classes to show
    classes_to_show = sorted(class_samples.keys())[:num_classes_to_show]

    fig, axes = plt.subplots(
        num_classes_to_show,
        samples_per_class,
        figsize=(samples_per_class * 3, num_classes_to_show * 3),
    )

    for row, class_id in enumerate(classes_to_show):
        samples_for_class = class_samples[class_id][:samples_per_class]

        for col, sample in enumerate(samples_for_class):
            img_path = sample[0]
            img = Image.open(img_path).convert("RGB")

            if num_classes_to_show == 1:
                ax = axes[col]
            else:
                ax = axes[row, col]

            ax.imshow(img)
            if col == 0:
                ax.set_ylabel(class_names[class_id], fontsize=10, fontweight="bold")
            ax.axis("off")

    plt.suptitle("Sample Images from Dataset", fontsize=14, fontweight="bold")
    plt.tight_layout()
    plt.show()