//! Convolution kernels for GPU
//!
//! Implements various convolution operations for signal processing and neural networks.

use std::collections::HashMap;

use crate::gpu::kernels::{
    BaseKernel, DataType, GpuKernel, KernelMetadata, KernelParams, OperationType,
};
use crate::gpu::{GpuBackend, GpuError};

/// 1D convolution kernel
pub struct Conv1dKernel {
    base: BaseKernel,
}

impl Conv1dKernel {
    /// Create a new 1D convolution kernel
    pub fn new() -> Self {
        let metadata = KernelMetadata {
            workgroup_size: [256, 1, 1],
            local_memory_usage: 2048, // Kernel data cache
            supports_tensor_cores: false,
            operationtype: OperationType::ComputeIntensive,
            backend_metadata: HashMap::new(),
        };

        let (cuda_source, rocm_source, wgpu_source, metal_source, opencl_source) =
            Self::get_kernel_sources();

        Self {
            base: BaseKernel::new(
                "conv1d",
                &cuda_source,
                &rocm_source,
                &wgpu_source,
                &metal_source,
                &opencl_source,
                metadata,
            ),
        }
    }

    /// Get kernel sources for different backends
    fn get_kernel_sources() -> (String, String, String, String, String) {
        // CUDA kernel for 1D convolution
        let cuda_source = r#"
extern "C" __global__ void conv1d(
    const float* __restrict__ input,
    const float* __restrict__ kernel,
    float* __restrict__ output,
    int input_length,
    int kernel_length,
    int output_length,
    int stride,
    int padding
) {
    int out_idx = blockIdx.x * blockDim.x + threadIdx.x;
    
    if (out_idx >= output_length) {
        return;
    }
    
    float sum = 0.0f;
    
    for (int k = 0; k < kernel_length; k++) {
        int input_idx = out_idx * stride + k - padding;
        
        if (input_idx >= 0 && input_idx < input_length) {
            sum += input[input_idx] * kernel[k];
        }
    }
    
    output[out_idx] = sum;
}
"#
        .to_string();

        // WebGPU kernel for 1D convolution
        let wgpu_source = r#"
struct Uniforms {
    input_length: u32,
    kernel_length: u32,
    output_length: u32,
    stride: u32,
    padding: u32,
};

@group(0) @binding(0) var<uniform> uniforms: Uniforms;
@group(0) @binding(1) var<storage, read> input: array<f32>;
@group(0) @binding(2) var<storage, read> kernel_data: array<f32>;
@group(0) @binding(3) var<storage, write> output: array<f32>;

@compute @workgroup_size(256)
#[allow(dead_code)]
fn conv1d(@builtin(global_invocation_id) global_id: vec3<u32>) {
    let out_idx = global_id.x;
    
    if (out_idx >= uniforms.output_length) {
        return;
    }
    
    var sum = 0.0;
    
    for (var k = 0u; k < uniforms.kernel_length; k = k + 1u) {
        let input_idx = i32(out_idx * uniforms.stride + k) - i32(uniforms.padding);
        
        if (input_idx >= 0 && input_idx < i32(uniforms.input_length)) {
            sum += input[input_idx] * kernel_data[k];
        }
    }
    
    output[out_idx] = sum;
}
"#
        .to_string();

        // Metal kernel for 1D convolution
        let metal_source = r#"
#include <metal_stdlib>
using namespace metal;

kernel void conv1d(
    const device float* input [[buffer(0)]],
    const device float* kernel_data [[buffer(1)]],
    device float* output [[buffer(2)]],
    constant uint& input_length [[buffer(3)]],
    constant uint& kernel_length [[buffer(4)]],
    constant uint& output_length [[buffer(5)]],
    constant uint& stride [[buffer(6)]],
    constant uint& padding [[buffer(7)]],
    uint gid [[thread_position_in_grid]])
{
    if (gid >= output_length) {
        return;
    }
    
    float sum = 0.0f;
    
    for (uint k = 0; k < kernel_length; k++) {
        int input_idx = int(gid * stride + k) - int(padding);
        
        if (input_idx >= 0 && input_idx < int(input_length)) {
            sum += input[input_idx] * kernel_data[k];
        }
    }
    
    output[gid] = sum;
}
"#
        .to_string();

        // OpenCL kernel for 1D convolution
        let opencl_source = r#"
__kernel void conv1d(
    __global const float* input__global const float* kernel_data__global float* output,
    const int input_length,
    const int kernel_length,
    const int output_length,
    const int stride,
    const int padding)
{
    int out_idx = get_global_id(0);
    
    if (out_idx >= output_length) {
        return;
    }
    
    float sum = 0.0f;
    
    for (int k = 0; k < kernel_length; k++) {
        int input_idx = out_idx * stride + k - padding;
        
        if (input_idx >= 0 && input_idx < input_length) {
            sum += input[input_idx] * kernel_data[k];
        }
    }
    
    output[out_idx] = sum;
}
"#
        .to_string();

        // ROCm (HIP) kernel - similar to CUDA
        let rocm_source = cuda_source.clone();

        (
            cuda_source,
            rocm_source,
            wgpu_source,
            metal_source,
            opencl_source,
        )
    }
}

impl GpuKernel for Conv1dKernel {
    fn name(&self) -> &str {
        self.base.name()
    }

    fn source_for_backend(&self, backend: GpuBackend) -> Result<String, GpuError> {
        self.base.source_for_backend(backend)
    }

    fn metadata(&self) -> KernelMetadata {
        self.base.metadata()
    }

    fn can_specialize(&self, params: &KernelParams) -> bool {
        matches!(params.datatype, DataType::Float32 | DataType::Float64)
    }

    fn specialize(&self, params: &KernelParams) -> Result<Box<dyn GpuKernel>, GpuError> {
        if !self.can_specialize(params) {
            return Err(GpuError::SpecializationNotSupported);
        }

        Ok(Box::new(Self::new()))
    }
}

impl Default for Conv1dKernel {
    fn default() -> Self {
        Self::new()
    }
}

/// 2D convolution kernel for image processing and CNNs
pub struct Conv2dKernel {
    base: BaseKernel,
}

impl Conv2dKernel {
    /// Create a new 2D convolution kernel
    pub fn new() -> Self {
        let metadata = KernelMetadata {
            workgroup_size: [16, 16, 1],
            local_memory_usage: 4096,    // Kernel and input tile cache
            supports_tensor_cores: true, // 2D convolutions can use tensor cores
            operationtype: OperationType::ComputeIntensive,
            backend_metadata: HashMap::new(),
        };

        let (cuda_source, rocm_source, wgpu_source, metal_source, opencl_source) =
            Self::get_kernel_sources();

        Self {
            base: BaseKernel::new(
                "conv2d",
                &cuda_source,
                &rocm_source,
                &wgpu_source,
                &metal_source,
                &opencl_source,
                metadata,
            ),
        }
    }

    /// Get kernel sources for different backends
    fn get_kernel_sources() -> (String, String, String, String, String) {
        // CUDA kernel for 2D convolution
        let cuda_source = r#"
extern "C" __global__ void conv2d(
    const float* __restrict__ input,
    const float* __restrict__ kernel,
    float* __restrict__ output,
    int batch_size,
    int in_channels,
    int out_channels,
    int input_height,
    int input_width,
    int output_height,
    int output_width,
    int kernel_height,
    int kernel_width,
    int stride_y,
    int stride_x,
    int padding_y,
    int padding_x
) {
    int batch_idx = blockIdx.z;
    int out_channel = blockIdx.y;
    int out_y = blockIdx.x * blockDim.x + threadIdx.x;
    int out_x = threadIdx.y;

    if (batch_idx >= batch_size || out_channel >= out_channels || 
        out_y >= output_height || out_x >= output_width) {
        return;
    }

    float sum = 0.0f;

    // Input and output offsets
    int input_batch_offset = batch_idx * in_channels * input_height * input_width;
    int output_batch_offset = batch_idx * out_channels * output_height * output_width;
    int kernel_offset = out_channel * in_channels * kernel_height * kernel_width;

    // Convolution computation
    for (int in_ch = 0; in_ch < in_channels; in_ch++) {
        for (int ky = 0; ky < kernel_height; ky++) {
            for (int kx = 0; kx < kernel_width; kx++) {
                int input_y = out_y * stride_y + ky - padding_y;
                int input_x = out_x * stride_x + kx - padding_x;

                if (input_y >= 0 && input_y < input_height && 
                    input_x >= 0 && input_x < input_width) {
                    
                    int input_idx = input_batch_offset + 
                                   in_ch * input_height * input_width + 
                                   input_y * input_width + input_x;
                    
                    int kernel_idx = kernel_offset + 
                                    in_ch * kernel_height * kernel_width + 
                                    ky * kernel_width + kx;
                    
                    sum += input[input_idx] * kernel[kernel_idx];
                }
            }
        }
    }

    int output_idx = output_batch_offset + 
                     out_channel * output_height * output_width + 
                     out_y * output_width + out_x;
    output[output_idx] = sum;
}
"#
        .to_string();

        // WebGPU kernel for 2D convolution (simplified)
        let wgpu_source = r#"
struct Uniforms {
    batch_size: u32,
    in_channels: u32,
    out_channels: u32,
    input_height: u32,
    input_width: u32,
    output_height: u32,
    output_width: u32,
    kernel_height: u32,
    kernel_width: u32,
    stride_y: u32,
    stride_x: u32,
    padding_y: u32,
    padding_x: u32,
};

@group(0) @binding(0) var<uniform> uniforms: Uniforms;
@group(0) @binding(1) var<storage, read> input: array<f32>;
@group(0) @binding(2) var<storage, read> kernel_data: array<f32>;
@group(0) @binding(3) var<storage, write> output: array<f32>;

@compute @workgroup_size(16, 16)
#[allow(dead_code)]
fn conv2d(@builtin(global_invocation_id) global_id: vec3<u32>) {
    let batch_idx = global_id.z;
    let out_channel = global_id.y % uniforms.out_channels;
    let out_y = global_id.x;
    let out_x = global_id.y / uniforms.out_channels;

    if (batch_idx >= uniforms.batch_size || out_channel >= uniforms.out_channels || 
        out_y >= uniforms.output_height || out_x >= uniforms.output_width) {
        return;
    }

    var sum = 0.0;

    // Simplified convolution - would need optimization
    for (var in_ch = 0u; in_ch < uniforms.in_channels; in_ch = in_ch + 1u) {
        for (var ky = 0u; ky < uniforms.kernel_height; ky = ky + 1u) {
            for (var kx = 0u; kx < uniforms.kernel_width; kx = kx + 1u) {
                let input_y = i32(out_y * uniforms.stride_y + ky) - i32(uniforms.padding_y);
                let input_x = i32(out_x * uniforms.stride_x + kx) - i32(uniforms.padding_x);

                if (input_y >= 0 && input_y < i32(uniforms.input_height) && 
                    input_x >= 0 && input_x < i32(uniforms.input_width)) {
                    
                    let input_idx = batch_idx * uniforms.in_channels * uniforms.input_height * uniforms.input_width + 
                                   in_ch * uniforms.input_height * uniforms.input_width + 
                                   u32(input_y) * uniforms.input_width + u32(input_x);
                    
                    let kernel_idx = out_channel * uniforms.in_channels * uniforms.kernel_height * uniforms.kernel_width + 
                                    in_ch * uniforms.kernel_height * uniforms.kernel_width + 
                                    ky * uniforms.kernel_width + kx;
                    
                    sum += input[input_idx] * kernel_data[kernel_idx];
                }
            }
        }
    }

    let output_idx = batch_idx * uniforms.out_channels * uniforms.output_height * uniforms.output_width + 
                     out_channel * uniforms.output_height * uniforms.output_width + 
                     out_y * uniforms.output_width + out_x;
    output[output_idx] = sum;
}
"#
        .to_string();

        // Metal and OpenCL implementations would be similar but adapted for their respective syntaxes
        let metal_source = r#"
// Metal 2D convolution implementation (simplified)
#include <metal_stdlib>
using namespace metal;

kernel void conv2d(
    const device float* input [[buffer(0)]],
    const device float* kernel_data [[buffer(1)]],
    device float* output [[buffer(2)]],
    constant uint& batch_size [[buffer(3)]],
    constant uint& in_channels [[buffer(4)]],
    constant uint& out_channels [[buffer(5)]],
    constant uint& input_height [[buffer(6)]],
    constant uint& input_width [[buffer(7)]],
    constant uint& output_height [[buffer(8)]],
    constant uint& output_width [[buffer(9)]],
    constant uint& kernel_height [[buffer(10)]],
    constant uint& kernel_width [[buffer(11)]],
    constant uint& stride_y [[buffer(12)]],
    constant uint& stride_x [[buffer(13)]],
    constant uint& padding_y [[buffer(14)]],
    constant uint& padding_x [[buffer(15)]],
    uint3 global_id [[thread_position_in_grid]])
{
    // Similar implementation to CUDA kernel
}
"#
        .to_string();

        let opencl_source = r#"
// OpenCL 2D convolution implementation (simplified)
__kernel void conv2d(
    __global const float* input__global const float* kernel_data__global float* output,
    const int batch_size,
    const int in_channels,
    const int out_channels,
    const int input_height,
    const int input_width,
    const int output_height,
    const int output_width,
    const int kernel_height,
    const int kernel_width,
    const int stride_y,
    const int stride_x,
    const int padding_y,
    const int padding_x)
{
    // Similar implementation to CUDA kernel
}
"#
        .to_string();

        // ROCm (HIP) kernel - similar to CUDA
        let rocm_source = cuda_source.clone();

        (
            cuda_source,
            rocm_source,
            wgpu_source,
            metal_source,
            opencl_source,
        )
    }
}

impl GpuKernel for Conv2dKernel {
    fn name(&self) -> &str {
        self.base.name()
    }

    fn source_for_backend(&self, backend: GpuBackend) -> Result<String, GpuError> {
        self.base.source_for_backend(backend)
    }

    fn metadata(&self) -> KernelMetadata {
        self.base.metadata()
    }

    fn can_specialize(&self, params: &KernelParams) -> bool {
        matches!(
            params.datatype,
            DataType::Float32 | DataType::Float64 | DataType::Float16 | DataType::BFloat16
        )
    }

    fn specialize(&self, params: &KernelParams) -> Result<Box<dyn GpuKernel>, GpuError> {
        if !self.can_specialize(params) {
            return Err(GpuError::SpecializationNotSupported);
        }

        // In a real implementation, we would generate optimized kernels based on:
        // - Kernel size (3x3, 5x5, etc.)
        // - Number of channels
        // - Tensor core usage for appropriate data types
        // - Memory layout optimizations

        Ok(Box::new(Self::new()))
    }
}

impl Default for Conv2dKernel {
    fn default() -> Self {
        Self::new()
    }
}
