//! GPU kernel implementations for ndimage operations
//!
//! This module contains the actual GPU kernel code and interfaces
//! for various image processing operations. The kernels are written
//! in a way that can be translated to CUDA, OpenCL, or Metal.

use scirs2_core::ndarray::{Array, ArrayView, ArrayView2, Dimension};
use scirs2_core::numeric::{Float, FromPrimitive};
use std::fmt::Debug;

use crate::error::{NdimageError, NdimageResult};
use crate::utils::safe_f64_to_float;

/// Helper function for safe usize conversion
#[allow(dead_code)]
fn safe_usize_to_float<T: Float + FromPrimitive>(value: usize) -> NdimageResult<T> {
    T::from_usize(value).ok_or_else(|| {
        NdimageError::ComputationError(format!("Failed to convert usize {} to float type", value))
    })
}

/// Helper function for safe array to slice conversion
#[allow(dead_code)]
fn safe_as_slice<'a, T, D: Dimension>(array: &'a ArrayView<T, D>) -> NdimageResult<&'a [T]> {
    array.as_slice().ok_or_else(|| {
        NdimageError::ComputationError("Failed to convert _array to contiguous slice".to_string())
    })
}

// Kernel source code constants
const GAUSSIAN_BLUR_KERNEL: &str = include_str!("kernels/gaussian_blur.kernel");
const CONVOLUTION_KERNEL: &str = include_str!("kernels/convolution.kernel");
const MEDIAN_FILTER_KERNEL: &str = include_str!("kernels/median_filter.kernel");
const MORPHOLOGY_KERNEL: &str = include_str!("kernels/morphology.kernel");

// Advanced kernel source code constants
const ADVANCED_MORPHOLOGY_KERNEL: &str = include_str!("kernels/advanced_morphology.kernel");
const ADVANCED_EDGE_DETECTION_KERNEL: &str = include_str!("kernels/advanced_edge_detection.kernel");
const TEXTURE_ANALYSIS_KERNEL: &str = include_str!("kernels/texture_analysis.kernel");
const ADVANCED_SEGMENTATION_KERNEL: &str = include_str!("kernels/advanced_segmentation.kernel");

// Advanced GPU kernels - inline definitions for enhanced operations
const SEPARABLE_GAUSSIAN_KERNEL: &str = r#"
__kernel void separable_gaussian_1d(
    __global const float* input__global float* output__global const float* weights,
    const int size,
    const int radius,
    const int direction, // 0 for horizontal, 1 for vertical
    const int width,
    const int height
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    if (x >= width || y >= height) return;
    
    float sum = 0.0f;
    
    if (direction == 0) {
        // Horizontal pass
        for (int i = -radius; i <= radius; i++) {
            int px = clamp(x + i, 0, width - 1);
            sum += input[y * width + px] * weights[i + radius];
        }
    } else {
        // Vertical pass
        for (int i = -radius; i <= radius; i++) {
            int py = clamp(y + i, 0, height - 1);
            sum += input[py * width + x] * weights[i + radius];
        }
    }
    
    output[y * width + x] = sum;
}
"#;

const BILATERAL_FILTER_KERNEL: &str = r#"
__kernel void bilateral_filter_2d(
    __global const float* input__global float* output,
    const float sigma_spatial,
    const float sigma_intensity,
    const int radius,
    const int width,
    const int height
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    if (x >= width || y >= height) return;
    
    float center_val = input[y * width + x];
    float sum = 0.0f;
    float weight_sum = 0.0f;
    
    float spatial_coeff = -0.5f / (sigma_spatial * sigma_spatial);
    float intensity_coeff = -0.5f / (sigma_intensity * sigma_intensity);
    
    for (int dy = -radius; dy <= radius; dy++) {
        for (int dx = -radius; dx <= radius; dx++) {
            int px = clamp(x + dx, 0, width - 1);
            int py = clamp(y + dy, 0, height - 1);
            
            float pixel_val = input[py * width + px];
            
            // Spatial weight
            float spatial_dist = dx * dx + dy * dy;
            float spatial_weight = exp(spatial_dist * spatial_coeff);
            
            // Intensity weight
            float intensity_dist = (pixel_val - center_val) * (pixel_val - center_val);
            float intensity_weight = exp(intensity_dist * intensity_coeff);
            
            float total_weight = spatial_weight * intensity_weight;
            
            sum += pixel_val * total_weight;
            weight_sum += total_weight;
        }
    }
    
    output[y * width + x] = sum / weight_sum;
}
"#;

const SOBEL_FILTER_KERNEL: &str = r#"
__kernel void sobel_filter_2d(
    __global const float* input__global float* output_x__global float* output_y__global float* magnitude,
    const int width,
    const int height
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    if (x >= width || y >= height) return;
    
    // Sobel X kernel: [-1, 0, 1; -2, 0, 2; -1, 0, 1]
    float gx = 0.0f;
    gx += -1.0f * input[clamp(y-1, 0, height-1) * width + clamp(x-1, 0, width-1)];
    gx += -2.0f * input[y * width + clamp(x-1, 0, width-1)];
    gx += -1.0f * input[clamp(y+1, 0, height-1) * width + clamp(x-1, 0, width-1)];
    gx += 1.0f * input[clamp(y-1, 0, height-1) * width + clamp(x+1, 0, width-1)];
    gx += 2.0f * input[y * width + clamp(x+1, 0, width-1)];
    gx += 1.0f * input[clamp(y+1, 0, height-1) * width + clamp(x+1, 0, width-1)];
    
    // Sobel Y kernel: [-1, -2, -1; 0, 0, 0; 1, 2, 1]
    float gy = 0.0f;
    gy += -1.0f * input[clamp(y-1, 0, height-1) * width + clamp(x-1, 0, width-1)];
    gy += -2.0f * input[clamp(y-1, 0, height-1) * width + x];
    gy += -1.0f * input[clamp(y-1, 0, height-1) * width + clamp(x+1, 0, width-1)];
    gy += 1.0f * input[clamp(y+1, 0, height-1) * width + clamp(x-1, 0, width-1)];
    gy += 2.0f * input[clamp(y+1, 0, height-1) * width + x];
    gy += 1.0f * input[clamp(y+1, 0, height-1) * width + clamp(x+1, 0, width-1)];
    
    output_x[y * width + x] = gx;
    output_y[y * width + x] = gy;
    magnitude[y * width + x] = sqrt(gx * gx + gy * gy);
}
"#;

const HISTOGRAM_KERNEL: &str = r#"
__kernel void compute_histogram(
    __global const float* input__global int* histogram,
    const float min_val,
    const float max_val,
    const int num_bins,
    const int total_pixels
) {
    int idx = get_global_id(0);
    
    if (idx >= total_pixels) return;
    
    float val = input[idx];
    if (val >= min_val && val <= max_val) {
        float normalized = (val - min_val) / (max_val - min_val);
        int bin = (int)(normalized * (num_bins - 1));
        bin = clamp(bin, 0, num_bins - 1);
        atomic_inc(&histogram[bin]);
    }
}
"#;

const MORPHOLOGY_EROSION_KERNEL: &str = r#"
__kernel void morphology_erosion_2d(
    __global const float* input__global float* output__global const int* structure,
    const int struct_width,
    const int struct_height,
    const int width,
    const int height
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    if (x >= width || y >= height) return;
    
    float min_val = INFINITY;
    int struct_center_x = struct_width / 2;
    int struct_center_y = struct_height / 2;
    
    for (int sy = 0; sy < struct_height; sy++) {
        for (int sx = 0; sx < struct_width; sx++) {
            if (structure[sy * struct_width + sx]) {
                int px = x + sx - struct_center_x;
                int py = y + sy - struct_center_y;
                px = clamp(px, 0, width - 1);
                py = clamp(py, 0, height - 1);
                
                float val = input[py * width + px];
                min_val = fmin(min_val, val);
            }
        }
    }
    
    output[y * width + x] = min_val;
}
"#;

const WAVELET_TRANSFORM_KERNEL: &str = r#"
__kernel void wavelet_transform_2d(
    __global const float* input__global float* ll_output__global float* lh_output__global float* hl_output__global float* hh_output__global const float* low_filter__global const float* high_filter,
    const int filter_length,
    const int width,
    const int height
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    if (x >= width/2 || y >= height/2) return;
    
    int filter_radius = filter_length / 2;
    
    // Apply row filters first
    float ll_row = 0.0f, lh_row = 0.0f;
    for (int i = 0; i < filter_length; i++) {
        int px = clamp(x * 2 + i - filter_radius, 0, width - 1);
        ll_row += input[y * 2 * width + px] * low_filter[i];
        lh_row += input[y * 2 * width + px] * high_filter[i];
    }
    
    // Apply column filters to row results
    float ll = 0.0f, lh = 0.0f, hl = 0.0f, hh = 0.0f;
    for (int i = 0; i < filter_length; i++) {
        int py = clamp(y * 2 + i - filter_radius, 0, height - 1);
        // This is simplified - full implementation would need temporary storage
        ll += ll_row * low_filter[i];
        lh += lh_row * low_filter[i];
        hl += ll_row * high_filter[i];
        hh += lh_row * high_filter[i];
    }
    
    ll_output[y * (width/2) + x] = ll;
    lh_output[y * (width/2) + x] = lh;
    hl_output[y * (width/2) + x] = hl;
    hh_output[y * (width/2) + x] = hh;
}
"#;

const TEMPLATE_MATCHING_KERNEL: &str = r#"
__kernel void template_matching_ncc(
    __global const float* image__global const float* template__global float* output,
    const int image_width,
    const int image_height,
    const int template_width,
    const int template_height,
    const float template_mean,
    const float template_norm
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    int output_width = image_width - template_width + 1;
    int output_height = image_height - template_height + 1;
    
    if (x >= output_width || y >= output_height) return;
    
    // Compute image patch mean
    float image_sum = 0.0f;
    for (int ty = 0; ty < template_height; ty++) {
        for (int tx = 0; tx < template_width; tx++) {
            image_sum += image[(y + ty) * image_width + (x + tx)];
        }
    }
    float image_mean = image_sum / (template_width * template_height);
    
    // Compute normalized cross-correlation
    float correlation = 0.0f;
    float image_variance = 0.0f;
    
    for (int ty = 0; ty < template_height; ty++) {
        for (int tx = 0; tx < template_width; tx++) {
            float image_val = image[(y + ty) * image_width + (x + tx)];
            float template_val = template[ty * template_width + tx];
            
            float image_centered = image_val - image_mean;
            float template_centered = template_val - template_mean;
            
            correlation += image_centered * template_centered;
            image_variance += image_centered * image_centered;
        }
    }
    
    float image_norm = sqrt(image_variance);
    float ncc = (image_norm > 0.0f) ? correlation / (image_norm * template_norm) : 0.0f;
    
    output[y * output_width + x] = ncc;
}
"#;

const DISTANCE_TRANSFORM_KERNEL: &str = r#"
__kernel void distance_transform_edt(
    __global const int* binary_image__global float* distance,
    const int width,
    const int height
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    if (x >= width || y >= height) return;
    
    int idx = y * width + x;
    
    if (binary_image[idx] == 0) {
        distance[idx] = 0.0f;
        return;
    }
    
    float min_dist = INFINITY;
    
    // Brute force approach - can be optimized with separable algorithm
    for (int py = 0; py < height; py++) {
        for (int px = 0; px < width; px++) {
            if (binary_image[py * width + px] == 0) {
                float dx = px - x;
                float dy = py - y;
                float dist = sqrt(dx * dx + dy * dy);
                min_dist = fmin(min_dist, dist);
            }
        }
    }
    
    distance[idx] = min_dist;
}
"#;

const GABOR_FILTER_KERNEL: &str = r#"
__kernel void gabor_filter_2d(
    __global const float* input__global float* output,
    const float sigma_x,
    const float sigma_y,
    const float theta,
    const float lambda,
    const float gamma,
    const float psi,
    const int kernel_size,
    const int width,
    const int height
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    if (x >= width || y >= height) return;
    
    float sum = 0.0f;
    int half_size = kernel_size / 2;
    
    float cos_theta = cos(theta);
    float sin_theta = sin(theta);
    
    for (int ky = -half_size; ky <= half_size; ky++) {
        for (int kx = -half_size; kx <= half_size; kx++) {
            // Rotate coordinates
            float x_rot = kx * cos_theta + ky * sin_theta;
            float y_rot = -kx * sin_theta + ky * cos_theta;
            
            // Gabor function
            float envelope = exp(-0.5f * (x_rot * x_rot / (sigma_x * sigma_x) + 
                                         gamma * gamma * y_rot * y_rot / (sigma_y * sigma_y)));
            float wave = cos(2.0f * M_PI * x_rot / lambda + psi);
            float gabor_val = envelope * wave;
            
            // Apply to image
            int px = clamp(x + kx, 0, width - 1);
            int py = clamp(y + ky, 0, height - 1);
            sum += input[py * width + px] * gabor_val;
        }
    }
    
    output[y * width + x] = sum;
}
"#;

const LAPLACIAN_KERNEL: &str = r#"
__kernel void laplacian_filter_2d(
    __global const float* input__global float* output,
    const int width,
    const int height,
    const int connectivity // 4 or 8
) {
    int x = get_global_id(0);
    int y = get_global_id(1);
    
    if (x >= width || y >= height) return;
    
    float center = input[y * width + x];
    float sum = 0.0f;
    
    if (connectivity == 4) {
        // 4-connected Laplacian: [0, -1, 0; -1, 4, -1; 0, -1, 0]
        sum += 4.0f * center;
        sum += -1.0f * input[clamp(y-1, 0, height-1) * width + x]; // top
        sum += -1.0f * input[clamp(y+1, 0, height-1) * width + x]; // bottom
        sum += -1.0f * input[y * width + clamp(x-1, 0, width-1)]; // left
        sum += -1.0f * input[y * width + clamp(x+1, 0, width-1)]; // right
    } else {
        // 8-connected Laplacian: [-1, -1, -1; -1, 8, -1; -1, -1, -1]
        sum += 8.0f * center;
        for (int dy = -1; dy <= 1; dy++) {
            for (int dx = -1; dx <= 1; dx++) {
                if (dx == 0 && dy == 0) continue;
                int px = clamp(x + dx, 0, width - 1);
                int py = clamp(y + dy, 0, height - 1);
                sum += -1.0f * input[py * width + px];
            }
        }
    }
    
    output[y * width + x] = sum;
}
"#;

/// GPU kernel registry for managing kernel implementations
pub struct KernelRegistry {
    kernels: std::collections::HashMap<String, KernelInfo>,
}

/// Information about a GPU kernel
pub struct KernelInfo {
    pub name: String,
    pub source: String,
    pub entry_point: String,
    pub work_dimensions: usize,
}

impl KernelRegistry {
    pub fn new() -> Self {
        let mut registry = Self {
            kernels: std::collections::HashMap::new(),
        };

        // Register built-in kernels
        registry.register_builtin_kernels();
        registry
    }

    fn register_builtin_kernels(&mut self) {
        // Gaussian blur kernel
        self.register_kernel(
            "gaussian_blur_2d",
            GAUSSIAN_BLUR_KERNEL,
            "gaussian_blur_2d",
            2,
        );

        // Convolution kernel
        self.register_kernel("convolution_2d", CONVOLUTION_KERNEL, "convolution_2d", 2);

        // Median filter kernel
        self.register_kernel(
            "median_filter_2d",
            MEDIAN_FILTER_KERNEL,
            "median_filter_2d",
            2,
        );

        // Morphological operations
        self.register_kernel("morphology_erosion", MORPHOLOGY_KERNEL, "erosion_2d", 2);
        self.register_kernel("morphology_dilation", MORPHOLOGY_KERNEL, "dilation_2d", 2);

        // Advanced filter kernels
        self.register_kernel(
            "separable_gaussian_1d",
            SEPARABLE_GAUSSIAN_KERNEL,
            "separable_gaussian_1d",
            2,
        );
        self.register_kernel(
            "bilateral_filter_2d",
            BILATERAL_FILTER_KERNEL,
            "bilateral_filter_2d",
            2,
        );
        self.register_kernel("sobel_filter_2d", SOBEL_FILTER_KERNEL, "sobel_filter_2d", 2);
        self.register_kernel(
            "laplacian_filter_2d",
            LAPLACIAN_KERNEL,
            "laplacian_filter_2d",
            2,
        );

        // Advanced morphological operations
        self.register_kernel(
            "hit_or_miss_2d",
            ADVANCED_MORPHOLOGY_KERNEL,
            "hit_or_miss_2d",
            2,
        );
        self.register_kernel(
            "morphological_gradient_2d",
            ADVANCED_MORPHOLOGY_KERNEL,
            "morphological_gradient_2d",
            2,
        );
        self.register_kernel(
            "tophat_transform_2d",
            ADVANCED_MORPHOLOGY_KERNEL,
            "tophat_transform_2d",
            2,
        );
        self.register_kernel("skeleton_2d", ADVANCED_MORPHOLOGY_KERNEL, "skeleton_2d", 2);
        self.register_kernel(
            "distance_transform_chamfer_2d",
            ADVANCED_MORPHOLOGY_KERNEL,
            "distance_transform_chamfer_2d",
            2,
        );

        // Advanced edge detection
        self.register_kernel(
            "canny_gradient_2d",
            ADVANCED_EDGE_DETECTION_KERNEL,
            "canny_gradient_2d",
            2,
        );
        self.register_kernel(
            "canny_non_maximum_suppression_2d",
            ADVANCED_EDGE_DETECTION_KERNEL,
            "canny_non_maximum_suppression_2d",
            2,
        );
        self.register_kernel(
            "canny_double_threshold_2d",
            ADVANCED_EDGE_DETECTION_KERNEL,
            "canny_double_threshold_2d",
            2,
        );
        self.register_kernel(
            "canny_edge_tracking_2d",
            ADVANCED_EDGE_DETECTION_KERNEL,
            "canny_edge_tracking_2d",
            2,
        );
        self.register_kernel(
            "laplacian_of_gaussian_2d",
            ADVANCED_EDGE_DETECTION_KERNEL,
            "laplacian_of_gaussian_2d",
            2,
        );
        self.register_kernel(
            "zero_crossing_2d",
            ADVANCED_EDGE_DETECTION_KERNEL,
            "zero_crossing_2d",
            2,
        );
        self.register_kernel(
            "harris_corner_response_2d",
            ADVANCED_EDGE_DETECTION_KERNEL,
            "harris_corner_response_2d",
            2,
        );
        self.register_kernel(
            "oriented_fast_keypoints_2d",
            ADVANCED_EDGE_DETECTION_KERNEL,
            "oriented_fast_keypoints_2d",
            2,
        );

        // Texture analysis
        self.register_kernel(
            "local_binary_pattern_2d",
            TEXTURE_ANALYSIS_KERNEL,
            "local_binary_pattern_2d",
            2,
        );
        self.register_kernel(
            "uniform_local_binary_pattern_2d",
            TEXTURE_ANALYSIS_KERNEL,
            "uniform_local_binary_pattern_2d",
            2,
        );
        self.register_kernel(
            "glcm_cooccurrence_2d",
            TEXTURE_ANALYSIS_KERNEL,
            "glcm_cooccurrence_2d",
            2,
        );
        self.register_kernel(
            "glcmfeatures_2d",
            TEXTURE_ANALYSIS_KERNEL,
            "glcmfeatures_2d",
            1,
        );
        self.register_kernel(
            "gabor_filter_2d",
            TEXTURE_ANALYSIS_KERNEL,
            "gabor_filter_2d",
            2,
        );
        self.register_kernel(
            "lawstexture_energy_2d",
            TEXTURE_ANALYSIS_KERNEL,
            "lawstexture_energy_2d",
            2,
        );
        self.register_kernel(
            "fractal_dimension_2d",
            TEXTURE_ANALYSIS_KERNEL,
            "fractal_dimension_2d",
            2,
        );

        // Advanced segmentation
        self.register_kernel(
            "watershed_labels_init_2d",
            ADVANCED_SEGMENTATION_KERNEL,
            "watershed_labels_init_2d",
            2,
        );
        self.register_kernel(
            "watershed_propagation_2d",
            ADVANCED_SEGMENTATION_KERNEL,
            "watershed_propagation_2d",
            2,
        );
        self.register_kernel(
            "region_growing_2d",
            ADVANCED_SEGMENTATION_KERNEL,
            "region_growing_2d",
            2,
        );
        self.register_kernel(
            "mean_shift_2d",
            ADVANCED_SEGMENTATION_KERNEL,
            "mean_shift_2d",
            2,
        );
        self.register_kernel(
            "level_set_evolution_2d",
            ADVANCED_SEGMENTATION_KERNEL,
            "level_set_evolution_2d",
            2,
        );
        self.register_kernel(
            "chan_vese_energy_2d",
            ADVANCED_SEGMENTATION_KERNEL,
            "chan_vese_energy_2d",
            2,
        );
        self.register_kernel(
            "active_contour_evolution_2d",
            ADVANCED_SEGMENTATION_KERNEL,
            "active_contour_evolution_2d",
            1,
        );
        self.register_kernel(
            "superpixel_slic_2d",
            ADVANCED_SEGMENTATION_KERNEL,
            "superpixel_slic_2d",
            2,
        );
    }

    pub fn register_kernel(&mut self, name: &str, source: &str, entrypoint: &str, dims: usize) {
        self.kernels.insert(
            name.to_string(),
            KernelInfo {
                name: name.to_string(),
                source: source.to_string(),
                entry_point: entrypoint.to_string(),
                work_dimensions: dims,
            },
        );
    }

    pub fn get_kernel(&self, name: &str) -> Option<&KernelInfo> {
        self.kernels.get(name)
    }
}

/// Abstract GPU buffer that can be used across different GPU backends
pub trait GpuBuffer<T>: Send + Sync {
    fn size(&self) -> usize;
    fn copy_from_host(&mut self, data: &[T]) -> NdimageResult<()>;
    fn copy_to_host(&self, data: &mut [T]) -> NdimageResult<()>;
    fn as_any(&self) -> &dyn std::any::Any;
    fn as_any_mut(&mut self) -> &mut dyn std::any::Any;
}

/// Abstract GPU kernel executor
pub trait GpuKernelExecutor<T>: Send + Sync {
    fn execute_kernel(
        &self,
        kernel: &KernelInfo,
        inputs: &[&dyn GpuBuffer<T>],
        outputs: &[&mut dyn GpuBuffer<T>],
        work_size: &[usize],
        params: &[T],
    ) -> NdimageResult<()>;
}

/// CPU fallback buffer that implements the GpuBuffer trait
/// This allows the GPU functions to work even when CUDA is not available
pub struct CpuFallbackBuffer<T> {
    data: Vec<T>,
}

impl<T> CpuFallbackBuffer<T>
where
    T: Clone + Default,
{
    /// Create a buffer from existing data
    pub fn from_slice(data: &[T]) -> NdimageResult<Self> {
        Ok(Self {
            data: data.to_vec(),
        })
    }

    /// Create an empty buffer of given size
    pub fn empty(size: usize) -> NdimageResult<Self> {
        Ok(Self {
            data: vec![T::default(); size],
        })
    }
}

impl<T> GpuBuffer<T> for CpuFallbackBuffer<T>
where
    T: Clone + Default + Send + Sync + Copy + 'static,
{
    fn size(&self) -> usize {
        self.data.len()
    }

    fn copy_from_host(&mut self, data: &[T]) -> NdimageResult<()> {
        if data.len() != self.data.len() {
            return Err(NdimageError::InvalidInput(format!(
                "Data size mismatch: expected {}, got {}",
                self.data.len(),
                data.len()
            )));
        }
        self.data.copy_from_slice(data);
        Ok(())
    }

    fn copy_to_host(&self, data: &mut [T]) -> NdimageResult<()> {
        if data.len() != self.data.len() {
            return Err(NdimageError::InvalidInput(format!(
                "Data size mismatch: expected {}, got {}",
                self.data.len(),
                data.len()
            )));
        }
        data.copy_from_slice(&self.data);
        Ok(())
    }

    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

    fn as_any_mut(&mut self) -> &mut dyn std::any::Any {
        self
    }
}

/// CPU fallback kernel executor that implements GPU operations on CPU
/// This provides a way to run "GPU" kernels on CPU when CUDA is not available
pub struct CpuFallbackExecutor;

impl CpuFallbackExecutor {
    pub fn new() -> Self {
        Self
    }
}

impl<T> GpuKernelExecutor<T> for CpuFallbackExecutor
where
    T: Float + FromPrimitive + Clone + Send + Sync + 'static,
{
    fn execute_kernel(
        &self,
        kernel: &KernelInfo,
        inputs: &[&dyn GpuBuffer<T>],
        outputs: &[&mut dyn GpuBuffer<T>],
        work_size: &[usize],
        _params: &[T],
    ) -> NdimageResult<()> {
        // This is a basic CPU fallback that returns an error indicating
        // that GPU kernel execution on CPU is not fully implemented.
        // A full implementation would need to emulate each GPU kernel
        // with equivalent CPU operations.

        Err(NdimageError::NotImplementedError(format!(
            "CPU fallback execution for GPU kernel '{}' is not fully implemented. Work _size: {:?}, {} inputs, {} outputs",
            kernel.name,
            work_size,
            inputs.len(),
            outputs.len()
        )))
    }
}

/// GPU-accelerated Gaussian filter implementation
#[allow(dead_code)]
pub fn gpu_gaussian_filter_2d<T>(
    input: &ArrayView2<T>,
    sigma: [T; 2],
    executor: &dyn GpuKernelExecutor<T>,
) -> NdimageResult<Array<T, scirs2_core::ndarray::Ix2>>
where
    T: Float + FromPrimitive + Debug + Clone + Default + Send + Sync + 'static,
{
    let (h, w) = input.dim();

    // Allocate GPU buffers
    // This is pseudo-code - actual implementation would use backend-specific allocations
    let input_buffer = allocate_gpu_buffer(safe_as_slice(input)?)?;
    let mut output_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;

    // Prepare kernel parameters
    let params = vec![
        sigma[0],
        sigma[1],
        safe_usize_to_float::<T>(h)?,
        safe_usize_to_float::<T>(w)?,
    ];

    // Get kernel from registry
    let registry = KernelRegistry::new();
    let kernel = registry
        .get_kernel("gaussian_blur_2d")
        .ok_or_else(|| NdimageError::ComputationError("Kernel not found".into()))?;

    // Execute kernel
    executor.execute_kernel(
        kernel,
        &[input_buffer.as_ref()],
        &[output_buffer.as_mut()],
        &[h, w],
        &params,
    )?;

    // Copy result back to host
    let mut output_data = vec![T::zero(); h * w];
    output_buffer.copy_to_host(&mut output_data)?;

    Ok(Array::from_shape_vec((h, w), output_data)?)
}

/// GPU-accelerated convolution implementation
#[allow(dead_code)]
pub fn gpu_convolve_2d<T>(
    input: &ArrayView2<T>,
    kernel: &ArrayView2<T>,
    executor: &dyn GpuKernelExecutor<T>,
) -> NdimageResult<Array<T, scirs2_core::ndarray::Ix2>>
where
    T: Float + FromPrimitive + Debug + Clone + Default + Send + Sync + 'static,
{
    let (ih, iw) = input.dim();
    let (kh, kw) = kernel.dim();

    // Allocate GPU buffers
    let input_buffer = allocate_gpu_buffer(safe_as_slice(input)?)?;
    let kernel_buffer = allocate_gpu_buffer(safe_as_slice(kernel)?)?;
    let mut output_buffer = allocate_gpu_buffer_empty::<T>(ih * iw)?;

    // Prepare kernel parameters
    let params = vec![
        safe_usize_to_float::<T>(ih)?,
        safe_usize_to_float::<T>(iw)?,
        safe_usize_to_float::<T>(kh)?,
        safe_usize_to_float::<T>(kw)?,
    ];

    // Get kernel from registry
    let registry = KernelRegistry::new();
    let gpu_kernel = registry
        .get_kernel("convolution_2d")
        .ok_or_else(|| NdimageError::ComputationError("Kernel not found".into()))?;

    // Execute kernel
    executor.execute_kernel(
        gpu_kernel,
        &[input_buffer.as_ref(), kernel_buffer.as_ref()],
        &[output_buffer.as_mut()],
        &[ih, iw],
        &params,
    )?;

    // Copy result back to host
    let mut output_data = vec![T::zero(); ih * iw];
    output_buffer.copy_to_host(&mut output_data)?;

    Ok(Array::from_shape_vec((ih, iw), output_data)?)
}

/// GPU-accelerated median filter implementation
#[allow(dead_code)]
pub fn gpu_median_filter_2d<T>(
    input: &ArrayView2<T>,
    size: [usize; 2],
    executor: &dyn GpuKernelExecutor<T>,
) -> NdimageResult<Array<T, scirs2_core::ndarray::Ix2>>
where
    T: Float + FromPrimitive + Debug + Clone + Default + Send + Sync + 'static,
{
    let (h, w) = input.dim();

    // Allocate GPU buffers
    let input_buffer = allocate_gpu_buffer(safe_as_slice(input)?)?;
    let mut output_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;

    // Prepare kernel parameters
    let params = vec![
        safe_usize_to_float::<T>(h)?,
        safe_usize_to_float::<T>(w)?,
        safe_usize_to_float::<T>(size[0])?,
        safe_usize_to_float::<T>(size[1])?,
    ];

    // Get kernel from registry
    let registry = KernelRegistry::new();
    let kernel = registry
        .get_kernel("median_filter_2d")
        .ok_or_else(|| NdimageError::ComputationError("Kernel not found".into()))?;

    // Execute kernel
    executor.execute_kernel(
        kernel,
        &[input_buffer.as_ref()],
        &[output_buffer.as_mut()],
        &[h, w],
        &params,
    )?;

    // Copy result back to host
    let mut output_data = vec![T::zero(); h * w];
    output_buffer.copy_to_host(&mut output_data)?;

    Ok(Array::from_shape_vec((h, w), output_data)?)
}

/// GPU-accelerated morphological erosion
#[allow(dead_code)]
pub fn gpu_erosion_2d<T>(
    input: &ArrayView2<T>,
    structure: &ArrayView2<bool>,
    executor: &dyn GpuKernelExecutor<T>,
) -> NdimageResult<Array<T, scirs2_core::ndarray::Ix2>>
where
    T: Float + FromPrimitive + Debug + Clone + Default + Send + Sync + 'static,
{
    let (h, w) = input.dim();
    let (sh, sw) = structure.dim();

    // Convert structure to T type
    let structure_t: Vec<T> = structure
        .iter()
        .map(|&b| if b { T::one() } else { T::zero() })
        .collect();

    // Allocate GPU buffers
    let input_buffer = allocate_gpu_buffer(safe_as_slice(input)?)?;
    let structure_buffer = allocate_gpu_buffer(&structure_t)?;
    let mut output_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;

    // Prepare kernel parameters
    let params = vec![
        safe_usize_to_float::<T>(h)?,
        safe_usize_to_float::<T>(w)?,
        safe_usize_to_float::<T>(sh)?,
        safe_usize_to_float::<T>(sw)?,
    ];

    // Get kernel from registry
    let registry = KernelRegistry::new();
    let kernel = registry
        .get_kernel("morphology_erosion")
        .ok_or_else(|| NdimageError::ComputationError("Kernel not found".into()))?;

    // Execute kernel
    executor.execute_kernel(
        kernel,
        &[input_buffer.as_ref(), structure_buffer.as_ref()],
        &[output_buffer.as_mut()],
        &[h, w],
        &params,
    )?;

    // Copy result back to host
    let mut output_data = vec![T::zero(); h * w];
    output_buffer.copy_to_host(&mut output_data)?;

    Ok(Array::from_shape_vec((h, w), output_data)?)
}

/// GPU-accelerated separable Gaussian filter implementation
#[allow(dead_code)]
pub fn gpu_separable_gaussian_filter_2d<T>(
    input: &ArrayView2<T>,
    sigma: [T; 2],
    executor: &dyn GpuKernelExecutor<T>,
) -> NdimageResult<Array<T, scirs2_core::ndarray::Ix2>>
where
    T: Float + FromPrimitive + Debug + Clone + Default + Send + Sync + 'static,
{
    let (h, w) = input.dim();

    // Calculate Gaussian weights for separable filter
    let radius_x = (safe_f64_to_float::<T>(3.0)? * sigma[0])
        .to_usize()
        .ok_or_else(|| {
            NdimageError::ComputationError("Failed to convert radius_x to usize".to_string())
        })?;
    let radius_y = (safe_f64_to_float::<T>(3.0)? * sigma[1])
        .to_usize()
        .ok_or_else(|| {
            NdimageError::ComputationError("Failed to convert radius_y to usize".to_string())
        })?;

    let max_radius = radius_x.max(radius_y);
    let weights_size = 2 * max_radius + 1;

    // Gaussian weights for horizontal pass
    let weights_x: Result<Vec<T>, NdimageError> = (0..weights_size)
        .map(|i| -> NdimageResult<T> {
            let offset = safe_usize_to_float::<T>(i)? - safe_usize_to_float::<T>(max_radius)?;
            let exp_arg = -safe_f64_to_float::<T>(0.5)? * offset * offset / (sigma[0] * sigma[0]);
            Ok(exp_arg.exp())
        })
        .collect();
    let weights_x = weights_x?;

    // Gaussian weights for vertical pass
    let weights_y: Result<Vec<T>, NdimageError> = (0..weights_size)
        .map(|i| -> NdimageResult<T> {
            let offset = safe_usize_to_float::<T>(i)? - safe_usize_to_float::<T>(max_radius)?;
            let exp_arg = -safe_f64_to_float::<T>(0.5)? * offset * offset / (sigma[1] * sigma[1]);
            Ok(exp_arg.exp())
        })
        .collect();
    let weights_y = weights_y?;

    // Allocate GPU buffers
    let input_buffer = allocate_gpu_buffer(safe_as_slice(input)?)?;
    let weights_x_buffer = allocate_gpu_buffer(&weights_x)?;
    let weights_y_buffer = allocate_gpu_buffer(&weights_y)?;
    let mut temp_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;
    let mut output_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;

    let registry = KernelRegistry::new();
    let kernel = registry
        .get_kernel("separable_gaussian_1d")
        .ok_or_else(|| NdimageError::ComputationError("Kernel not found".into()))?;

    // Horizontal pass (direction = 0)
    let params_h = vec![
        safe_usize_to_float::<T>(h * w)?,
        safe_usize_to_float::<T>(radius_x)?,
        T::zero(), // direction = 0 for horizontal
        safe_usize_to_float::<T>(w)?,
        safe_usize_to_float::<T>(h)?,
    ];

    executor.execute_kernel(
        kernel,
        &[input_buffer.as_ref(), weights_x_buffer.as_ref()],
        &[temp_buffer.as_mut()],
        &[h, w],
        &params_h,
    )?;

    // Vertical pass (direction = 1)
    let params_v = vec![
        safe_usize_to_float::<T>(h * w)?,
        safe_usize_to_float::<T>(radius_y)?,
        T::one(), // direction = 1 for vertical
        safe_usize_to_float::<T>(w)?,
        safe_usize_to_float::<T>(h)?,
    ];

    executor.execute_kernel(
        kernel,
        &[temp_buffer.as_ref(), weights_y_buffer.as_ref()],
        &[output_buffer.as_mut()],
        &[h, w],
        &params_v,
    )?;

    // Copy result back to host
    let mut output_data = vec![T::zero(); h * w];
    output_buffer.copy_to_host(&mut output_data)?;

    Ok(Array::from_shape_vec((h, w), output_data)?)
}

/// GPU-accelerated bilateral filter implementation
#[allow(dead_code)]
pub fn gpu_bilateral_filter_2d<T>(
    input: &ArrayView2<T>,
    sigma_spatial: T,
    sigma_intensity: T,
    radius: usize,
    executor: &dyn GpuKernelExecutor<T>,
) -> NdimageResult<Array<T, scirs2_core::ndarray::Ix2>>
where
    T: Float + FromPrimitive + Debug + Clone + Default + Send + Sync + 'static,
{
    let (h, w) = input.dim();

    // Allocate GPU buffers
    let input_buffer = allocate_gpu_buffer(safe_as_slice(input)?)?;
    let mut output_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;

    // Prepare kernel parameters
    let params = vec![
        sigma_spatial,
        sigma_intensity,
        safe_usize_to_float::<T>(radius)?,
        safe_usize_to_float::<T>(w)?,
        safe_usize_to_float::<T>(h)?,
    ];

    // Get kernel from registry
    let registry = KernelRegistry::new();
    let kernel = registry
        .get_kernel("bilateral_filter_2d")
        .ok_or_else(|| NdimageError::ComputationError("Kernel not found".into()))?;

    // Execute kernel
    executor.execute_kernel(
        kernel,
        &[input_buffer.as_ref()],
        &[output_buffer.as_mut()],
        &[h, w],
        &params,
    )?;

    // Copy result back to host
    let mut output_data = vec![T::zero(); h * w];
    output_buffer.copy_to_host(&mut output_data)?;

    Ok(Array::from_shape_vec((h, w), output_data)?)
}

/// GPU-accelerated Sobel filter implementation
#[allow(dead_code)]
pub fn gpu_sobel_filter_2d<T>(
    input: &ArrayView2<T>,
    executor: &dyn GpuKernelExecutor<T>,
) -> NdimageResult<(
    Array<T, scirs2_core::ndarray::Ix2>,
    Array<T, scirs2_core::ndarray::Ix2>,
    Array<T, scirs2_core::ndarray::Ix2>,
)>
where
    T: Float + FromPrimitive + Debug + Clone + Default + Send + Sync + 'static,
{
    let (h, w) = input.dim();

    // Allocate GPU buffers
    let input_buffer = allocate_gpu_buffer(safe_as_slice(input)?)?;
    let mut output_x_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;
    let mut output_y_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;
    let mut magnitude_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;

    // Prepare kernel parameters
    let params = vec![safe_usize_to_float::<T>(w)?, safe_usize_to_float::<T>(h)?];

    // Get kernel from registry
    let registry = KernelRegistry::new();
    let kernel = registry
        .get_kernel("sobel_filter_2d")
        .ok_or_else(|| NdimageError::ComputationError("Kernel not found".into()))?;

    // Execute kernel
    executor.execute_kernel(
        kernel,
        &[input_buffer.as_ref()],
        &[
            output_x_buffer.as_mut(),
            output_y_buffer.as_mut(),
            magnitude_buffer.as_mut(),
        ],
        &[h, w],
        &params,
    )?;

    // Copy results back to host
    let mut output_x_data = vec![T::zero(); h * w];
    let mut output_y_data = vec![T::zero(); h * w];
    let mut magnitude_data = vec![T::zero(); h * w];

    output_x_buffer.copy_to_host(&mut output_x_data)?;
    output_y_buffer.copy_to_host(&mut output_y_data)?;
    magnitude_buffer.copy_to_host(&mut magnitude_data)?;

    Ok((
        Array::from_shape_vec((h, w), output_x_data)?,
        Array::from_shape_vec((h, w), output_y_data)?,
        Array::from_shape_vec((h, w), magnitude_data)?,
    ))
}

/// GPU-accelerated Laplacian filter implementation
#[allow(dead_code)]
pub fn gpu_laplacian_filter_2d<T>(
    input: &ArrayView2<T>,
    connectivity: usize,
    executor: &dyn GpuKernelExecutor<T>,
) -> NdimageResult<Array<T, scirs2_core::ndarray::Ix2>>
where
    T: Float + FromPrimitive + Debug + Clone + Default + Send + Sync + 'static,
{
    let (h, w) = input.dim();

    // Allocate GPU buffers
    let input_buffer = allocate_gpu_buffer(safe_as_slice(input)?)?;
    let mut output_buffer = allocate_gpu_buffer_empty::<T>(h * w)?;

    // Prepare kernel parameters
    let params = vec![
        safe_usize_to_float::<T>(w)?,
        safe_usize_to_float::<T>(h)?,
        safe_usize_to_float::<T>(connectivity)?,
    ];

    // Get kernel from registry
    let registry = KernelRegistry::new();
    let kernel = registry
        .get_kernel("laplacian_filter_2d")
        .ok_or_else(|| NdimageError::ComputationError("Kernel not found".into()))?;

    // Execute kernel
    executor.execute_kernel(
        kernel,
        &[input_buffer.as_ref()],
        &[output_buffer.as_mut()],
        &[h, w],
        &params,
    )?;

    // Copy result back to host
    let mut output_data = vec![T::zero(); h * w];
    output_buffer.copy_to_host(&mut output_data)?;

    Ok(Array::from_shape_vec((h, w), output_data)?)
}

// GPU buffer allocation functions that delegate to backend-specific implementations

#[allow(dead_code)]
fn allocate_gpu_buffer<T>(data: &[T]) -> NdimageResult<Box<dyn GpuBuffer<T>>>
where
    T: Clone + Default + Send + Sync + Copy + 'static,
{
    #[cfg(feature = "cuda")]
    {
        return crate::backend::cuda::allocate_gpu_buffer(data);
    }

    #[cfg(not(feature = "cuda"))]
    {
        // CPU fallback: create a CPU buffer that implements the GpuBuffer trait
        Ok(Box::new(CpuFallbackBuffer::from_slice(data)?))
    }
}

#[allow(dead_code)]
fn allocate_gpu_buffer_empty<T>(size: usize) -> NdimageResult<Box<dyn GpuBuffer<T>>>
where
    T: Clone + Default + Send + Sync + Copy + 'static,
{
    #[cfg(feature = "cuda")]
    {
        return crate::backend::cuda::allocate_gpu_buffer_empty(size);
    }

    #[cfg(not(feature = "cuda"))]
    {
        // CPU fallback: create an empty CPU buffer that implements the GpuBuffer trait
        Ok(Box::new(CpuFallbackBuffer::empty(size)?))
    }
}

// Kernel source code would normally be in separate files
// Here we embed them as module documentation for demonstration

// Gaussian blur kernel (pseudo-CUDA/OpenCL code)
// ```cuda
// __kernel void gaussian_blur_2d(
//     __global const float* input,
//     __global float* output,
//     const float sigma_x,
//     const float sigma_y,
//     const int height,
//     const int width
// ) {
//     int x = get_global_id(0);
//     int y = get_global_id(1);
//
//     if (x >= width || y >= height) return;
//
//     // Gaussian kernel computation
//     float sum = 0.0f;
//     float weight_sum = 0.0f;
//
//     int radius_x = (int)(3.0f * sigma_x);
//     int radius_y = (int)(3.0f * sigma_y);
//
//     for (int dy = -radius_y; dy <= radius_y; dy++) {
//         for (int dx = -radius_x; dx <= radius_x; dx++) {
//             int px = clamp(x + dx, 0, width - 1);
//             int py = clamp(y + dy, 0, height - 1);
//
//             float weight = exp(-0.5f * (dx*dx/(sigma_x*sigma_x) + dy*dy/(sigma_y*sigma_y)));
//             sum += input[py * width + px] * weight;
//             weight_sum += weight;
//         }
//     }
//
//     output[y * width + x] = sum / weight_sum;
// }
// ```
