"""
GPU-Native Eye Image Processor for Color Fundus Photography (CFP) Images.

This module implements a fully PyTorch-based image processor that:
1. Localizes the eye/fundus region using gradient-based radial symmetry
2. Crops to a border-minimized square centered on the eye
3. Applies CLAHE for contrast enhancement
4. Outputs tensors compatible with Hugging Face vision models

Constraints:
- PyTorch only (no OpenCV, PIL, NumPy in runtime)
- CUDA-compatible, batch-friendly, deterministic
"""

from typing import Dict, List, Optional, Union
import math

import torch
import torch.nn.functional as F
from transformers.image_processing_utils import BaseImageProcessor
from transformers.image_processing_base import BatchFeature

# Optional imports for broader input support
try:
    from PIL import Image
    PIL_AVAILABLE = True
except ImportError:
    PIL_AVAILABLE = False

try:
    import numpy as np
    NUMPY_AVAILABLE = True
except ImportError:
    NUMPY_AVAILABLE = False


# =============================================================================
# PHASE 1: Input & Tensor Standardization
# =============================================================================

def _pil_to_tensor(image: "Image.Image") -> torch.Tensor:
    """Convert PIL Image to tensor (C, H, W) in [0, 1]."""
    if not PIL_AVAILABLE:
        raise ImportError("PIL is required to process PIL Images")

    # Convert to RGB if necessary
    if image.mode != "RGB":
        image = image.convert("RGB")

    # Use numpy as intermediate if available, otherwise manual conversion
    if NUMPY_AVAILABLE:
        arr = np.array(image, dtype=np.float32) / 255.0
        # (H, W, C) -> (C, H, W)
        tensor = torch.from_numpy(arr).permute(2, 0, 1)
    else:
        # Manual conversion without numpy
        width, height = image.size
        pixels = list(image.getdata())
        tensor = torch.tensor(pixels, dtype=torch.float32).view(height, width, 3) / 255.0
        tensor = tensor.permute(2, 0, 1)

    return tensor


def _numpy_to_tensor(arr: "np.ndarray") -> torch.Tensor:
    """Convert numpy array to tensor (C, H, W) in [0, 1]."""
    if not NUMPY_AVAILABLE:
        raise ImportError("NumPy is required to process numpy arrays")

    # Handle different array shapes
    if arr.ndim == 2:
        # Grayscale (H, W) -> (1, H, W)
        arr = arr[..., None]

    if arr.ndim == 3 and arr.shape[-1] in [1, 3, 4]:
        # (H, W, C) -> (C, H, W)
        arr = arr.transpose(2, 0, 1)

    # Convert to float and normalize
    if arr.dtype == np.uint8:
        arr = arr.astype(np.float32) / 255.0
    elif arr.dtype != np.float32:
        arr = arr.astype(np.float32)

    return torch.from_numpy(arr.copy())


def standardize_input(
    images: Union[torch.Tensor, List[torch.Tensor], "Image.Image", List["Image.Image"], "np.ndarray", List["np.ndarray"]],
    device: Optional[torch.device] = None,
) -> torch.Tensor:
    """
    Convert input images to standardized tensor format.

    Args:
        images: Input as:
            - torch.Tensor (C,H,W), (B,C,H,W), or list of tensors
            - PIL.Image.Image or list of PIL Images
            - numpy.ndarray (H,W,C), (B,H,W,C), or list of arrays
        device: Target device (defaults to input device or CPU)

    Returns:
        Tensor of shape (B, C, H, W) in float32, range [0, 1]
    """
    # Handle single inputs by wrapping in list
    if PIL_AVAILABLE and isinstance(images, Image.Image):
        images = [images]
    if NUMPY_AVAILABLE and isinstance(images, np.ndarray) and images.ndim == 3:
        # Could be single (H,W,C) or batch (B,H,W) grayscale - assume single if last dim is 1-4
        if images.shape[-1] in [1, 3, 4]:
            images = [images]

    # Convert list inputs to tensors
    if isinstance(images, list):
        converted = []
        for img in images:
            if PIL_AVAILABLE and isinstance(img, Image.Image):
                converted.append(_pil_to_tensor(img))
            elif NUMPY_AVAILABLE and isinstance(img, np.ndarray):
                converted.append(_numpy_to_tensor(img))
            elif isinstance(img, torch.Tensor):
                t = img if img.dim() == 3 else img.squeeze(0)
                converted.append(t)
            else:
                raise TypeError(f"Unsupported image type: {type(img)}")
        images = torch.stack(converted)
    elif NUMPY_AVAILABLE and isinstance(images, np.ndarray):
        # Batch of numpy arrays (B, H, W, C)
        if images.ndim == 4:
            images = images.transpose(0, 3, 1, 2)  # (B, C, H, W)
        if images.dtype == np.uint8:
            images = images.astype(np.float32) / 255.0
        images = torch.from_numpy(images.copy())

    if images.dim() == 3:
        # Add batch dimension: (C, H, W) -> (B, C, H, W)
        images = images.unsqueeze(0)

    # Move to target device if specified
    if device is not None:
        images = images.to(device)

    # Convert to float32 and normalize to [0, 1]
    if images.dtype == torch.uint8:
        images = images.float() / 255.0
    elif images.dtype != torch.float32:
        images = images.float()

    # Clamp to valid range
    images = images.clamp(0.0, 1.0)

    return images


def rgb_to_grayscale(images: torch.Tensor) -> torch.Tensor:
    """
    Convert RGB images to grayscale using luminance formula.

    Y = 0.299 * R + 0.587 * G + 0.114 * B

    Args:
        images: Tensor of shape (B, 3, H, W)

    Returns:
        Tensor of shape (B, 1, H, W)
    """
    # Luminance weights
    weights = torch.tensor([0.299, 0.587, 0.114], device=images.device, dtype=images.dtype)
    weights = weights.view(1, 3, 1, 1)

    grayscale = (images * weights).sum(dim=1, keepdim=True)
    return grayscale


# =============================================================================
# PHASE 2: Eye Region Localization (GPU-Safe)
# =============================================================================

def create_sobel_kernels(device: torch.device, dtype: torch.dtype) -> tuple:
    """
    Create Sobel kernels for gradient computation.

    Returns:
        Tuple of (sobel_x, sobel_y) kernels, each of shape (1, 1, 3, 3)
    """
    sobel_x = torch.tensor([
        [-1, 0, 1],
        [-2, 0, 2],
        [-1, 0, 1]
    ], device=device, dtype=dtype).view(1, 1, 3, 3)

    sobel_y = torch.tensor([
        [-1, -2, -1],
        [ 0,  0,  0],
        [ 1,  2,  1]
    ], device=device, dtype=dtype).view(1, 1, 3, 3)

    return sobel_x, sobel_y


def compute_gradients(grayscale: torch.Tensor) -> tuple:
    """
    Compute image gradients using Sobel filters.

    Args:
        grayscale: Tensor of shape (B, 1, H, W)

    Returns:
        Tuple of (grad_x, grad_y, grad_magnitude)
    """
    sobel_x, sobel_y = create_sobel_kernels(grayscale.device, grayscale.dtype)

    # Apply Sobel filters with padding to maintain size
    grad_x = F.conv2d(grayscale, sobel_x, padding=1)
    grad_y = F.conv2d(grayscale, sobel_y, padding=1)

    # Compute gradient magnitude
    grad_magnitude = torch.sqrt(grad_x ** 2 + grad_y ** 2 + 1e-8)

    return grad_x, grad_y, grad_magnitude


def compute_radial_symmetry_response(
    grayscale: torch.Tensor,
    grad_x: torch.Tensor,
    grad_y: torch.Tensor,
    grad_magnitude: torch.Tensor,
) -> torch.Tensor:
    """
    Compute radial symmetry response for circle detection.

    This weights regions that are:
    1. Dark (low intensity - typical of pupil/iris)
    2. Have strong radial gradients pointing inward

    Args:
        grayscale: Grayscale image (B, 1, H, W)
        grad_x, grad_y: Gradient components
        grad_magnitude: Gradient magnitude

    Returns:
        Radial symmetry response map (B, 1, H, W)
    """
    B, _, H, W = grayscale.shape
    device = grayscale.device
    dtype = grayscale.dtype

    # Create coordinate grids
    y_coords = torch.arange(H, device=device, dtype=dtype).view(1, 1, H, 1).expand(B, 1, H, W)
    x_coords = torch.arange(W, device=device, dtype=dtype).view(1, 1, 1, W).expand(B, 1, H, W)

    # Compute center of mass of dark regions as initial estimate
    # Invert intensity so dark regions have high weight
    dark_weight = 1.0 - grayscale
    dark_weight = dark_weight ** 2  # Emphasize darker regions

    # Normalize weights
    weight_sum = dark_weight.sum(dim=(2, 3), keepdim=True) + 1e-8

    # Weighted center of mass
    cx_init = (dark_weight * x_coords).sum(dim=(2, 3), keepdim=True) / weight_sum
    cy_init = (dark_weight * y_coords).sum(dim=(2, 3), keepdim=True) / weight_sum

    # Compute vectors from each pixel to estimated center
    dx_to_center = cx_init - x_coords
    dy_to_center = cy_init - y_coords
    dist_to_center = torch.sqrt(dx_to_center ** 2 + dy_to_center ** 2 + 1e-8)

    # Normalize direction vectors
    dx_norm = dx_to_center / dist_to_center
    dy_norm = dy_to_center / dist_to_center

    # Normalize gradient vectors
    grad_norm = grad_magnitude + 1e-8
    gx_norm = grad_x / grad_norm
    gy_norm = grad_y / grad_norm

    # Radial symmetry: gradient should point toward center
    # Dot product between gradient and direction to center
    radial_alignment = gx_norm * dx_norm + gy_norm * dy_norm

    # Weight by gradient magnitude and darkness
    response = radial_alignment * grad_magnitude * dark_weight

    # Apply Gaussian smoothing to get robust response
    kernel_size = max(H, W) // 8
    if kernel_size % 2 == 0:
        kernel_size += 1
    kernel_size = max(kernel_size, 5)

    sigma = kernel_size / 6.0

    # Create 1D Gaussian kernel
    x = torch.arange(kernel_size, device=device, dtype=dtype) - kernel_size // 2
    gaussian_1d = torch.exp(-x ** 2 / (2 * sigma ** 2))
    gaussian_1d = gaussian_1d / gaussian_1d.sum()

    # Separable 2D convolution
    gaussian_1d_h = gaussian_1d.view(1, 1, 1, kernel_size)
    gaussian_1d_v = gaussian_1d.view(1, 1, kernel_size, 1)

    pad_h = kernel_size // 2
    pad_v = kernel_size // 2

    response = F.pad(response, (pad_h, pad_h, 0, 0), mode='reflect')
    response = F.conv2d(response, gaussian_1d_h)
    response = F.pad(response, (0, 0, pad_v, pad_v), mode='reflect')
    response = F.conv2d(response, gaussian_1d_v)

    return response


def soft_argmax_2d(response: torch.Tensor, temperature: float = 0.1) -> tuple:
    """
    Compute soft argmax to find the center coordinates.

    Args:
        response: Response map (B, 1, H, W)
        temperature: Softmax temperature (lower = sharper)

    Returns:
        Tuple of (cx, cy) each of shape (B,)
    """
    B, _, H, W = response.shape
    device = response.device
    dtype = response.dtype

    # Flatten spatial dimensions
    response_flat = response.view(B, -1)

    # Apply softmax with temperature
    weights = F.softmax(response_flat / temperature, dim=1)
    weights = weights.view(B, 1, H, W)

    # Create coordinate grids
    y_coords = torch.arange(H, device=device, dtype=dtype).view(1, 1, H, 1).expand(B, 1, H, W)
    x_coords = torch.arange(W, device=device, dtype=dtype).view(1, 1, 1, W).expand(B, 1, H, W)

    # Weighted sum of coordinates
    cx = (weights * x_coords).sum(dim=(2, 3)).squeeze(-1)  # (B,)
    cy = (weights * y_coords).sum(dim=(2, 3)).squeeze(-1)  # (B,)

    return cx, cy


def estimate_eye_center(
    images: torch.Tensor,
    softmax_temperature: float = 0.1,
) -> tuple:
    """
    Estimate the center of the eye region in each image.

    Args:
        images: RGB images of shape (B, 3, H, W)
        softmax_temperature: Temperature for soft argmax (lower = sharper peak detection,
            higher = more averaging). Typical range: 0.01-1.0. Default 0.1 works well
            for most fundus images. Use higher values (0.3-0.5) for noisy images.

    Returns:
        Tuple of (cx, cy) each of shape (B,) in pixel coordinates
    """
    grayscale = rgb_to_grayscale(images)
    grad_x, grad_y, grad_magnitude = compute_gradients(grayscale)
    response = compute_radial_symmetry_response(grayscale, grad_x, grad_y, grad_magnitude)
    cx, cy = soft_argmax_2d(response, temperature=softmax_temperature)

    return cx, cy


# =============================================================================
# PHASE 2.3: Radius Estimation
# =============================================================================

def estimate_radius(
    images: torch.Tensor,
    cx: torch.Tensor,
    cy: torch.Tensor,
    num_radii: int = 100,
    num_angles: int = 36,
    min_radius_frac: float = 0.1,
    max_radius_frac: float = 0.5,
) -> torch.Tensor:
    """
    Estimate the radius of the eye region by analyzing radial intensity profiles.

    Args:
        images: RGB images (B, 3, H, W)
        cx, cy: Center coordinates (B,)
        num_radii: Number of radius samples
        num_angles: Number of angular samples
        min_radius_frac: Minimum radius as fraction of image size
        max_radius_frac: Maximum radius as fraction of image size

    Returns:
        Estimated radius for each image (B,)
    """
    B, _, H, W = images.shape
    device = images.device
    dtype = images.dtype

    grayscale = rgb_to_grayscale(images)  # (B, 1, H, W)

    min_dim = min(H, W)
    min_radius = int(min_radius_frac * min_dim)
    max_radius = int(max_radius_frac * min_dim)

    # Create radius and angle samples
    radii = torch.linspace(min_radius, max_radius, num_radii, device=device, dtype=dtype)
    angles = torch.linspace(0, 2 * math.pi, num_angles + 1, device=device, dtype=dtype)[:-1]

    # Create sampling grid: (num_angles, num_radii)
    cos_angles = torch.cos(angles).view(-1, 1)  # (num_angles, 1)
    sin_angles = torch.sin(angles).view(-1, 1)  # (num_angles, 1)

    # Offset coordinates from center
    dx = cos_angles * radii  # (num_angles, num_radii)
    dy = sin_angles * radii  # (num_angles, num_radii)

    # Compute absolute coordinates for each batch item
    # cx, cy: (B,) -> expand to (B, num_angles, num_radii)
    cx_expanded = cx.view(B, 1, 1).expand(B, num_angles, num_radii)
    cy_expanded = cy.view(B, 1, 1).expand(B, num_angles, num_radii)

    sample_x = cx_expanded + dx.unsqueeze(0)  # (B, num_angles, num_radii)
    sample_y = cy_expanded + dy.unsqueeze(0)  # (B, num_angles, num_radii)

    # Normalize to [-1, 1] for grid_sample
    sample_x_norm = 2.0 * sample_x / (W - 1) - 1.0
    sample_y_norm = 2.0 * sample_y / (H - 1) - 1.0

    # Create sampling grid: (B, num_angles, num_radii, 2)
    grid = torch.stack([sample_x_norm, sample_y_norm], dim=-1)

    # Sample intensities
    sampled = F.grid_sample(
        grayscale, grid, mode='bilinear', padding_mode='border', align_corners=True
    )  # (B, 1, num_angles, num_radii)

    # Average over angles to get radial profile
    radial_profile = sampled.mean(dim=2).squeeze(1)  # (B, num_radii)

    # Compute gradient of radial profile (looking for strong negative gradient at iris edge)
    radial_gradient = radial_profile[:, 1:] - radial_profile[:, :-1]  # (B, num_radii-1)

    # Find the radius with strongest negative gradient (edge of iris)
    # Weight by radius to prefer larger circles (avoid pupil boundary)
    radius_weights = torch.linspace(0.5, 1.5, num_radii - 1, device=device, dtype=dtype)
    weighted_gradient = radial_gradient * radius_weights.unsqueeze(0)

    # Find minimum (strongest negative gradient)
    min_idx = weighted_gradient.argmin(dim=1)  # (B,)

    # Convert index to radius value
    estimated_radius = radii[min_idx + 1]  # +1 because gradient has one less element

    # Clamp to valid range
    estimated_radius = estimated_radius.clamp(min_radius, max_radius)

    return estimated_radius


# =============================================================================
# PHASE 3: Border-Minimized Square Crop
# =============================================================================

def compute_crop_box(
    cx: torch.Tensor,
    cy: torch.Tensor,
    radius: torch.Tensor,
    H: int,
    W: int,
    scale_factor: float = 1.1,
    allow_overflow: bool = False,
) -> tuple:
    """
    Compute square bounding box for cropping.

    Args:
        cx, cy: Center coordinates (B,)
        radius: Estimated radius (B,)
        H, W: Image dimensions
        scale_factor: Multiply radius by this factor for padding
        allow_overflow: If True, don't clamp box to image bounds (for pre-cropped images)

    Returns:
        Tuple of (x1, y1, x2, y2) each of shape (B,)
    """
    # Compute half side length
    half_side = radius * scale_factor

    # Initial box centered on detected eye
    x1 = cx - half_side
    y1 = cy - half_side
    x2 = cx + half_side
    y2 = cy + half_side

    if allow_overflow:
        # Keep the box centered on the eye, don't clamp
        # Out-of-bounds regions will be filled with black during cropping
        return x1, y1, x2, y2

    # Clamp to image bounds while maintaining square shape
    # If box exceeds bounds, shift it
    x1 = x1.clamp(min=0)
    y1 = y1.clamp(min=0)
    x2 = x2.clamp(max=W - 1)
    y2 = y2.clamp(max=H - 1)

    # Ensure square by taking minimum side
    side_x = x2 - x1
    side_y = y2 - y1
    side = torch.minimum(side_x, side_y)

    # Recenter the box
    cx_new = (x1 + x2) / 2
    cy_new = (y1 + y2) / 2

    x1 = (cx_new - side / 2).clamp(min=0)
    y1 = (cy_new - side / 2).clamp(min=0)
    x2 = x1 + side
    y2 = y1 + side

    # Final clamp
    x2 = x2.clamp(max=W - 1)
    y2 = y2.clamp(max=H - 1)

    return x1, y1, x2, y2


def batch_crop_and_resize(
    images: torch.Tensor,
    x1: torch.Tensor,
    y1: torch.Tensor,
    x2: torch.Tensor,
    y2: torch.Tensor,
    output_size: int,
    padding_mode: str = 'border',
) -> torch.Tensor:
    """
    Crop and resize images using grid_sample for GPU efficiency.

    Args:
        images: Input images (B, C, H, W)
        x1, y1, x2, y2: Crop coordinates (B,) - can extend beyond image bounds
        output_size: Output square size
        padding_mode: How to handle out-of-bounds sampling:
            - 'border': repeat edge pixels (default)
            - 'zeros': fill with black (useful for pre-cropped images)

    Returns:
        Cropped and resized images (B, C, output_size, output_size)
    """
    B, C, H, W = images.shape
    device = images.device
    dtype = images.dtype

    # Create output grid coordinates
    out_coords = torch.linspace(0, 1, output_size, device=device, dtype=dtype)
    out_y, out_x = torch.meshgrid(out_coords, out_coords, indexing='ij')
    out_grid = torch.stack([out_x, out_y], dim=-1)  # (output_size, output_size, 2)
    out_grid = out_grid.unsqueeze(0).expand(B, -1, -1, -1)  # (B, output_size, output_size, 2)

    # Scale grid to crop coordinates
    # out_grid is in [0, 1], need to map to [x1, x2] and [y1, y2]
    x1 = x1.view(B, 1, 1, 1)
    y1 = y1.view(B, 1, 1, 1)
    x2 = x2.view(B, 1, 1, 1)
    y2 = y2.view(B, 1, 1, 1)

    # Map [0, 1] to pixel coordinates
    sample_x = x1 + out_grid[..., 0:1] * (x2 - x1)
    sample_y = y1 + out_grid[..., 1:2] * (y2 - y1)

    # Normalize to [-1, 1] for grid_sample
    sample_x_norm = 2.0 * sample_x / (W - 1) - 1.0
    sample_y_norm = 2.0 * sample_y / (H - 1) - 1.0

    grid = torch.cat([sample_x_norm, sample_y_norm], dim=-1)  # (B, output_size, output_size, 2)

    # Sample with specified padding mode
    cropped = F.grid_sample(
        images, grid, mode='bilinear', padding_mode=padding_mode, align_corners=True
    )

    return cropped


# =============================================================================
# PHASE 4: CLAHE (Torch-Native)
# =============================================================================

def _srgb_to_linear(rgb: torch.Tensor) -> torch.Tensor:
    """Convert sRGB to linear RGB."""
    threshold = 0.04045
    linear = torch.where(
        rgb <= threshold,
        rgb / 12.92,
        ((rgb + 0.055) / 1.055) ** 2.4
    )
    return linear


def _linear_to_srgb(linear: torch.Tensor) -> torch.Tensor:
    """Convert linear RGB to sRGB."""
    threshold = 0.0031308
    srgb = torch.where(
        linear <= threshold,
        linear * 12.92,
        1.055 * (linear ** (1.0 / 2.4)) - 0.055
    )
    return srgb


def rgb_to_lab(images: torch.Tensor) -> tuple:
    """
    Convert sRGB images to CIE LAB color space.

    This is a proper LAB conversion that:
    1. Converts sRGB to linear RGB
    2. Converts linear RGB to XYZ
    3. Converts XYZ to LAB

    Args:
        images: RGB images (B, C, H, W) in [0, 1] sRGB

    Returns:
        Tuple of (L, a, b) where:
            - L: Luminance in [0, 1] (normalized from [0, 100])
            - a, b: Chrominance (normalized to roughly [-0.5, 0.5])
    """
    device = images.device
    dtype = images.dtype

    # Step 1: sRGB to linear RGB
    linear_rgb = _srgb_to_linear(images)

    # Step 2: Linear RGB to XYZ (D65 illuminant)
    # RGB to XYZ matrix
    r = linear_rgb[:, 0:1, :, :]
    g = linear_rgb[:, 1:2, :, :]
    b = linear_rgb[:, 2:3, :, :]

    x = 0.4124564 * r + 0.3575761 * g + 0.1804375 * b
    y = 0.2126729 * r + 0.7151522 * g + 0.0721750 * b
    z = 0.0193339 * r + 0.1191920 * g + 0.9503041 * b

    # D65 reference white
    xn, yn, zn = 0.95047, 1.0, 1.08883

    x = x / xn
    y = y / yn
    z = z / zn

    # Step 3: XYZ to LAB
    delta = 6.0 / 29.0
    delta_cube = delta ** 3

    def f(t):
        return torch.where(
            t > delta_cube,
            t ** (1.0 / 3.0),
            t / (3.0 * delta ** 2) + 4.0 / 29.0
        )

    fx = f(x)
    fy = f(y)
    fz = f(z)

    L = 116.0 * fy - 16.0  # Range [0, 100]
    a = 500.0 * (fx - fy)   # Range roughly [-128, 127]
    b_ch = 200.0 * (fy - fz)  # Range roughly [-128, 127]

    # Normalize to convenient ranges for processing
    L = L / 100.0  # [0, 1]
    a = a / 256.0 + 0.5  # Roughly [0, 1]
    b_ch = b_ch / 256.0 + 0.5  # Roughly [0, 1]

    return L, a, b_ch


def lab_to_rgb(L: torch.Tensor, a: torch.Tensor, b_ch: torch.Tensor) -> torch.Tensor:
    """
    Convert CIE LAB to sRGB.

    Args:
        L: Luminance in [0, 1] (normalized from [0, 100])
        a, b_ch: Chrominance (normalized, roughly [0, 1])

    Returns:
        RGB images (B, 3, H, W) in [0, 1] sRGB
    """
    # Denormalize
    L_lab = L * 100.0
    a_lab = (a - 0.5) * 256.0
    b_lab = (b_ch - 0.5) * 256.0

    # LAB to XYZ
    fy = (L_lab + 16.0) / 116.0
    fx = a_lab / 500.0 + fy
    fz = fy - b_lab / 200.0

    delta = 6.0 / 29.0

    def f_inv(t):
        return torch.where(
            t > delta,
            t ** 3,
            3.0 * (delta ** 2) * (t - 4.0 / 29.0)
        )

    # D65 reference white
    xn, yn, zn = 0.95047, 1.0, 1.08883

    x = xn * f_inv(fx)
    y = yn * f_inv(fy)
    z = zn * f_inv(fz)

    # XYZ to linear RGB
    r = 3.2404542 * x - 1.5371385 * y - 0.4985314 * z
    g = -0.9692660 * x + 1.8760108 * y + 0.0415560 * z
    b = 0.0556434 * x - 0.2040259 * y + 1.0572252 * z

    linear_rgb = torch.cat([r, g, b], dim=1)

    # Clamp before gamma correction to avoid NaN from negative values
    linear_rgb = linear_rgb.clamp(0.0, 1.0)

    # Linear RGB to sRGB
    srgb = _linear_to_srgb(linear_rgb)

    return srgb.clamp(0.0, 1.0)


def compute_histogram(
    tensor: torch.Tensor,
    num_bins: int = 256,
) -> torch.Tensor:
    """
    Compute histogram for a batch of single-channel images.

    Args:
        tensor: Input tensor (B, 1, H, W) with values in [0, 1]
        num_bins: Number of histogram bins

    Returns:
        Histograms (B, num_bins)
    """
    B = tensor.shape[0]
    device = tensor.device
    dtype = tensor.dtype

    # Flatten spatial dimensions
    flat = tensor.view(B, -1)  # (B, H*W)

    # Bin indices
    bin_indices = (flat * (num_bins - 1)).long().clamp(0, num_bins - 1)

    # Compute histogram using scatter_add
    histograms = torch.zeros(B, num_bins, device=device, dtype=dtype)
    ones = torch.ones_like(flat, dtype=dtype)

    for i in range(B):
        histograms[i] = histograms[i].scatter_add(0, bin_indices[i], ones[i])

    return histograms


def clahe_single_tile(
    tile: torch.Tensor,
    clip_limit: float,
    num_bins: int = 256,
) -> torch.Tensor:
    """
    Apply CLAHE to a single tile.

    Args:
        tile: Input tile (B, 1, tile_h, tile_w)
        clip_limit: Histogram clip limit
        num_bins: Number of histogram bins

    Returns:
        CDF lookup table (B, num_bins)
    """
    B, _, tile_h, tile_w = tile.shape
    device = tile.device
    dtype = tile.dtype
    num_pixels = tile_h * tile_w

    # Compute histogram
    hist = compute_histogram(tile, num_bins)  # (B, num_bins)

    # Clip histogram
    clip_value = clip_limit * num_pixels / num_bins
    excess = (hist - clip_value).clamp(min=0).sum(dim=1, keepdim=True)  # (B, 1)
    hist = hist.clamp(max=clip_value)

    # Redistribute excess uniformly
    redistribution = excess / num_bins
    hist = hist + redistribution

    # Compute CDF
    cdf = hist.cumsum(dim=1)  # (B, num_bins)

    # Normalize CDF to [0, 1]
    cdf_min = cdf[:, 0:1]
    cdf_max = cdf[:, -1:]
    cdf = (cdf - cdf_min) / (cdf_max - cdf_min + 1e-8)

    return cdf


def apply_clahe_vectorized(
    images: torch.Tensor,
    grid_size: int = 8,
    clip_limit: float = 2.0,
    num_bins: int = 256,
) -> torch.Tensor:
    """
    Vectorized CLAHE implementation (more efficient for GPU).

    Args:
        images: Input images (B, C, H, W)
        grid_size: Number of tiles in each dimension
        clip_limit: Histogram clip limit
        num_bins: Number of histogram bins

    Returns:
        CLAHE-enhanced images (B, C, H, W)
    """
    B, C, H, W = images.shape
    device = images.device
    dtype = images.dtype

    # Work on luminance only
    if C == 3:
        L, a, b_ch = rgb_to_lab(images)
    else:
        L = images.clone()
        a = b_ch = None

    # Ensure divisibility
    pad_h = (grid_size - H % grid_size) % grid_size
    pad_w = (grid_size - W % grid_size) % grid_size

    if pad_h > 0 or pad_w > 0:
        L_padded = F.pad(L, (0, pad_w, 0, pad_h), mode='reflect')
    else:
        L_padded = L

    _, _, H_pad, W_pad = L_padded.shape
    tile_h = H_pad // grid_size
    tile_w = W_pad // grid_size

    # Reshape into tiles: (B, 1, grid_size, tile_h, grid_size, tile_w)
    L_tiles = L_padded.view(B, 1, grid_size, tile_h, grid_size, tile_w)
    L_tiles = L_tiles.permute(0, 2, 4, 1, 3, 5)  # (B, grid_size, grid_size, 1, tile_h, tile_w)
    L_tiles = L_tiles.reshape(B * grid_size * grid_size, 1, tile_h, tile_w)

    # Compute histograms for all tiles at once
    num_pixels = tile_h * tile_w
    flat = L_tiles.view(B * grid_size * grid_size, -1)
    bin_indices = (flat * (num_bins - 1)).long().clamp(0, num_bins - 1)

    # Vectorized histogram computation
    histograms = torch.zeros(B * grid_size * grid_size, num_bins, device=device, dtype=dtype)
    histograms.scatter_add_(1, bin_indices, torch.ones_like(flat))

    # Clip and redistribute
    clip_value = clip_limit * num_pixels / num_bins
    excess = (histograms - clip_value).clamp(min=0).sum(dim=1, keepdim=True)
    histograms = histograms.clamp(max=clip_value)
    histograms = histograms + excess / num_bins

    # Compute CDFs
    cdfs = histograms.cumsum(dim=1)
    cdf_min = cdfs[:, 0:1]
    cdf_max = cdfs[:, -1:]
    cdfs = (cdfs - cdf_min) / (cdf_max - cdf_min + 1e-8)

    # Reshape CDFs: (B, grid_size, grid_size, num_bins)
    cdfs = cdfs.view(B, grid_size, grid_size, num_bins)

    # Create coordinate grids for interpolation
    y_coords = torch.arange(H_pad, device=device, dtype=dtype)
    x_coords = torch.arange(W_pad, device=device, dtype=dtype)

    # Map to tile coordinates (centered on tiles)
    tile_y = (y_coords + 0.5) / tile_h - 0.5
    tile_x = (x_coords + 0.5) / tile_w - 0.5

    tile_y = tile_y.clamp(0, grid_size - 1.001)
    tile_x = tile_x.clamp(0, grid_size - 1.001)

    # Integer indices and weights
    ty0 = tile_y.long().clamp(0, grid_size - 2)
    tx0 = tile_x.long().clamp(0, grid_size - 2)
    ty1 = (ty0 + 1).clamp(max=grid_size - 1)
    tx1 = (tx0 + 1).clamp(max=grid_size - 1)

    wy = (tile_y - ty0.float()).view(1, H_pad, 1, 1)
    wx = (tile_x - tx0.float()).view(1, 1, W_pad, 1)

    # Get bin indices for all pixels
    bin_idx = (L_padded * (num_bins - 1)).long().clamp(0, num_bins - 1)  # (B, 1, H_pad, W_pad)
    bin_idx = bin_idx.squeeze(1)  # (B, H_pad, W_pad)

    # Gather CDF values for each corner
    # We need cdfs[b, ty, tx, bin_idx[b, y, x]] for all combinations

    # Expand indices for gathering
    b_idx = torch.arange(B, device=device).view(B, 1, 1).expand(B, H_pad, W_pad)
    ty0_exp = ty0.view(1, H_pad, 1).expand(B, H_pad, W_pad)
    ty1_exp = ty1.view(1, H_pad, 1).expand(B, H_pad, W_pad)
    tx0_exp = tx0.view(1, 1, W_pad).expand(B, H_pad, W_pad)
    tx1_exp = tx1.view(1, 1, W_pad).expand(B, H_pad, W_pad)

    # Gather using advanced indexing
    v00 = cdfs[b_idx, ty0_exp, tx0_exp, bin_idx]  # (B, H_pad, W_pad)
    v01 = cdfs[b_idx, ty0_exp, tx1_exp, bin_idx]
    v10 = cdfs[b_idx, ty1_exp, tx0_exp, bin_idx]
    v11 = cdfs[b_idx, ty1_exp, tx1_exp, bin_idx]

    # Bilinear interpolation
    wy = wy.squeeze(-1)  # (1, H_pad, 1)
    wx = wx.squeeze(-1)  # (1, 1, W_pad)

    L_out = (1 - wy) * (1 - wx) * v00 + (1 - wy) * wx * v01 + wy * (1 - wx) * v10 + wy * wx * v11
    L_out = L_out.unsqueeze(1)  # (B, 1, H_pad, W_pad)

    # Remove padding
    if pad_h > 0 or pad_w > 0:
        L_out = L_out[:, :, :H, :W]

    # Convert back to RGB
    if C == 3:
        output = lab_to_rgb(L_out, a, b_ch)
    else:
        output = L_out

    return output


# =============================================================================
# PHASE 5: Resize & Normalization
# =============================================================================

# ImageNet normalization constants
IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]


def resize_images(
    images: torch.Tensor,
    size: int,
    mode: str = 'bilinear',
    antialias: bool = True,
) -> torch.Tensor:
    """
    Resize images to target size.

    Args:
        images: Input images (B, C, H, W)
        size: Target size (square)
        mode: Interpolation mode
        antialias: Whether to use antialiasing

    Returns:
        Resized images (B, C, size, size)
    """
    return F.interpolate(
        images,
        size=(size, size),
        mode=mode,
        align_corners=False if mode in ['bilinear', 'bicubic'] else None,
        antialias=antialias if mode in ['bilinear', 'bicubic'] else False,
    )


def normalize_images(
    images: torch.Tensor,
    mean: Optional[List[float]] = None,
    std: Optional[List[float]] = None,
    mode: str = 'imagenet',
) -> torch.Tensor:
    """
    Normalize images.

    Args:
        images: Input images (B, C, H, W) in [0, 1]
        mean: Custom mean (per channel)
        std: Custom std (per channel)
        mode: 'imagenet', 'none', or 'custom'

    Returns:
        Normalized images
    """
    if mode == 'none':
        return images

    if mode == 'imagenet':
        mean = IMAGENET_MEAN
        std = IMAGENET_STD
    elif mode == 'custom':
        if mean is None or std is None:
            raise ValueError("Custom mode requires mean and std")
    else:
        raise ValueError(f"Unknown normalization mode: {mode}")

    device = images.device
    dtype = images.dtype

    mean_tensor = torch.tensor(mean, device=device, dtype=dtype).view(1, -1, 1, 1)
    std_tensor = torch.tensor(std, device=device, dtype=dtype).view(1, -1, 1, 1)

    return (images - mean_tensor) / std_tensor


# =============================================================================
# PHASE 6: Hugging Face ImageProcessor Integration
# =============================================================================

class EyeCLAHEImageProcessor(BaseImageProcessor):
    """
    GPU-native image processor for Color Fundus Photography (CFP) images.

    This processor:
    1. Localizes the eye region using gradient-based radial symmetry
    2. Crops to a border-minimized square centered on the eye
    3. Applies CLAHE for contrast enhancement
    4. Resizes and normalizes for vision model input

    All operations are implemented in pure PyTorch and are CUDA-compatible.
    """

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        size: int = 224,
        crop_scale_factor: float = 1.1,
        clahe_grid_size: int = 8,
        clahe_clip_limit: float = 2.0,
        normalization_mode: str = "imagenet",
        custom_mean: Optional[List[float]] = None,
        custom_std: Optional[List[float]] = None,
        do_clahe: bool = True,
        do_crop: bool = True,
        min_radius_frac: float = 0.1,
        max_radius_frac: float = 0.5,
        allow_overflow: bool = False,
        softmax_temperature: float = 0.1,
        **kwargs,
    ):
        """
        Initialize the EyeCLAHEImageProcessor.

        Args:
            size: Output image size (square)
            crop_scale_factor: Scale factor for crop box (relative to detected radius)
            clahe_grid_size: Number of tiles for CLAHE
            clahe_clip_limit: Histogram clip limit for CLAHE
            normalization_mode: 'imagenet', 'none', or 'custom'
            custom_mean: Custom normalization mean (if mode='custom')
            custom_std: Custom normalization std (if mode='custom')
            do_clahe: Whether to apply CLAHE
            do_crop: Whether to perform eye-centered cropping
            min_radius_frac: Minimum radius as fraction of image size
            max_radius_frac: Maximum radius as fraction of image size
            allow_overflow: If True, allow crop box to extend beyond image bounds
                           and fill missing regions with black. Useful for pre-cropped
                           images where the fundus circle is partially cut off.
            softmax_temperature: Temperature for soft argmax in eye center detection.
                Lower values (0.01-0.1) give sharper peak detection, higher values
                (0.3-0.5) provide more averaging for noisy images. Default: 0.1.
        """
        super().__init__(**kwargs)

        self.size = size
        self.crop_scale_factor = crop_scale_factor
        self.clahe_grid_size = clahe_grid_size
        self.clahe_clip_limit = clahe_clip_limit
        self.normalization_mode = normalization_mode
        self.custom_mean = custom_mean
        self.custom_std = custom_std
        self.do_clahe = do_clahe
        self.do_crop = do_crop
        self.min_radius_frac = min_radius_frac
        self.max_radius_frac = max_radius_frac
        self.allow_overflow = allow_overflow
        self.softmax_temperature = softmax_temperature

    def preprocess(
        self,
        images,
        return_tensors: str = "pt",
        device: Optional[Union[str, torch.device]] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        Preprocess images for model input.

        Args:
            images: Input images in any of these formats:
                - torch.Tensor: (C,H,W), (B,C,H,W), or list of tensors
                - PIL.Image.Image: single image or list of images
                - numpy.ndarray: (H,W,C), (B,H,W,C), or list of arrays
            return_tensors: Return type (only "pt" supported)
            device: Target device for processing (e.g., "cuda", "cpu")

        Returns:
            BatchFeature with keys:
                - 'pixel_values': Processed images (B, C, size, size)
                - 'scale_x', 'scale_y': Scale factors for coordinate mapping (B,)
                - 'offset_x', 'offset_y': Offsets for coordinate mapping (B,)

            To map coordinates from processed image back to original:
                orig_x = offset_x + cropped_x * scale_x
                orig_y = offset_y + cropped_y * scale_y
        """
        if return_tensors != "pt":
            raise ValueError("Only 'pt' (PyTorch) tensors are supported")

        # Determine device
        if device is not None:
            device = torch.device(device)
        elif isinstance(images, torch.Tensor):
            device = images.device
        elif isinstance(images, list) and len(images) > 0 and isinstance(images[0], torch.Tensor):
            device = images[0].device
        else:
            # PIL images and numpy arrays default to CPU
            device = torch.device('cpu')

        # Standardize input
        images = standardize_input(images, device)
        B, C, H_orig, W_orig = images.shape

        if self.do_crop:
            # Estimate eye center
            cx, cy = estimate_eye_center(images, softmax_temperature=self.softmax_temperature)

            # Estimate radius
            radius = estimate_radius(
                images, cx, cy,
                min_radius_frac=self.min_radius_frac,
                max_radius_frac=self.max_radius_frac,
            )

            # Compute crop box
            x1, y1, x2, y2 = compute_crop_box(
                cx, cy, radius, H_orig, W_orig,
                scale_factor=self.crop_scale_factor,
                allow_overflow=self.allow_overflow,
            )

            # Compute coordinate mapping
            # For processed coordinates in [0, self.size-1], map back to original
            scale_x = (x2 - x1) / (self.size - 1)
            scale_y = (y2 - y1) / (self.size - 1)
            offset_x = x1
            offset_y = y1

            # Crop and resize
            # Use 'zeros' padding when allow_overflow is True to fill out-of-bounds with black
            padding_mode = 'zeros' if self.allow_overflow else 'border'
            images = batch_crop_and_resize(images, x1, y1, x2, y2, self.size, padding_mode=padding_mode)
        else:
            # Just resize - no crop
            # Compute coordinate mapping for direct resize
            scale_x = torch.full((B,), (W_orig - 1) / (self.size - 1), device=device, dtype=images.dtype)
            scale_y = torch.full((B,), (H_orig - 1) / (self.size - 1), device=device, dtype=images.dtype)
            offset_x = torch.zeros(B, device=device, dtype=images.dtype)
            offset_y = torch.zeros(B, device=device, dtype=images.dtype)
            images = resize_images(images, self.size)

        # Apply CLAHE
        if self.do_clahe:
            images = apply_clahe_vectorized(
                images,
                grid_size=self.clahe_grid_size,
                clip_limit=self.clahe_clip_limit,
            )

        # Normalize
        images = normalize_images(
            images,
            mean=self.custom_mean,
            std=self.custom_std,
            mode=self.normalization_mode,
        )

        # Return with coordinate mapping information (flattened structure)
        return BatchFeature(
            data={
                "pixel_values": images,
                "scale_x": scale_x,
                "scale_y": scale_y,
                "offset_x": offset_x,
                "offset_y": offset_y,
            },
            tensor_type="pt"
        )

    def __call__(
        self,
        images: Union[torch.Tensor, List[torch.Tensor]],
        **kwargs,
    ) -> BatchFeature:
        """
        Process images (alias for preprocess).
        """
        return self.preprocess(images, **kwargs)


# For AutoImageProcessor registration
EyeGPUImageProcessor = EyeCLAHEImageProcessor