//! Vulkan GPU backend for sparse matrix operations
//!
//! This module provides Vulkan-accelerated sparse matrix operations with
//! cross-platform support for various GPU vendors (NVIDIA, AMD, Intel, etc.)

use crate::csr_array::CsrArray;
use crate::error::{SparseError, SparseResult};
use crate::sparray::SparseArray;
use scirs2_core::ndarray::{Array1, ArrayView1};
use scirs2_core::numeric::{Float, SparseElement};
use std::collections::HashMap;
use std::fmt::Debug;
use std::sync::Arc;

#[cfg(feature = "gpu")]
use scirs2_core::gpu::{GpuDevice, GpuError};

/// Optimization levels for Vulkan backend
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum VulkanOptimizationLevel {
    /// Basic implementation without advanced optimizations
    Basic,
    /// Use compute shader optimizations
    ComputeShader,
    /// Use subgroup operations (requires subgroup support)
    Subgroup,
    /// Maximum performance with all optimizations
    Maximum,
}

/// Vulkan device information
#[derive(Debug, Clone)]
pub struct VulkanDeviceInfo {
    pub device_name: String,
    pub vendor_id: u32,
    pub device_type: VulkanDeviceType,
    pub max_compute_shared_memory_size: usize,
    pub max_compute_work_group_count: [u32; 3],
    pub max_compute_work_group_invocations: u32,
    pub max_compute_work_group_size: [u32; 3],
    pub subgroup_size: u32,
    pub supports_subgroups: bool,
    pub supports_int8: bool,
    pub supports_int16: bool,
    pub supports_float64: bool,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum VulkanDeviceType {
    Other,
    IntegratedGpu,
    DiscreteGpu,
    VirtualGpu,
    Cpu,
}

impl VulkanDeviceInfo {
    /// Detect Vulkan device information
    pub fn detect() -> Self {
        // In a real implementation, this would query Vulkan API
        // For now, return reasonable defaults
        Self {
            device_name: "Default Vulkan Device".to_string(),
            vendor_id: 0,
            device_type: VulkanDeviceType::DiscreteGpu,
            max_compute_shared_memory_size: 32768, // 32KB typical
            max_compute_work_group_count: [65535, 65535, 65535],
            max_compute_work_group_invocations: 1024,
            max_compute_work_group_size: [1024, 1024, 64],
            subgroup_size: 32,
            supports_subgroups: true,
            supports_int8: true,
            supports_int16: true,
            supports_float64: true,
        }
    }

    /// Check if device is NVIDIA
    pub fn is_nvidia(&self) -> bool {
        self.vendor_id == 0x10DE
    }

    /// Check if device is AMD
    pub fn is_amd(&self) -> bool {
        self.vendor_id == 0x1002
    }

    /// Check if device is Intel
    pub fn is_intel(&self) -> bool {
        self.vendor_id == 0x8086
    }

    /// Get optimal work group size for SpMV
    pub fn optimal_workgroup_size(&self) -> usize {
        if self.supports_subgroups {
            self.subgroup_size as usize
        } else {
            64 // Conservative default
        }
    }
}

/// Memory manager for Vulkan buffers
#[derive(Debug)]
pub struct VulkanMemoryManager {
    allocated_buffers: HashMap<String, usize>,
    total_allocated: usize,
    peak_usage: usize,
}

impl VulkanMemoryManager {
    pub fn new() -> Self {
        Self {
            allocated_buffers: HashMap::new(),
            total_allocated: 0,
            peak_usage: 0,
        }
    }

    pub fn allocate(&mut self, id: String, size: usize) -> SparseResult<()> {
        self.allocated_buffers.insert(id, size);
        self.total_allocated += size;
        if self.total_allocated > self.peak_usage {
            self.peak_usage = self.total_allocated;
        }
        Ok(())
    }

    pub fn deallocate(&mut self, id: &str) -> SparseResult<()> {
        if let Some(size) = self.allocated_buffers.remove(id) {
            self.total_allocated = self.total_allocated.saturating_sub(size);
        }
        Ok(())
    }

    pub fn current_usage(&self) -> usize {
        self.total_allocated
    }

    pub fn peak_usage(&self) -> usize {
        self.peak_usage
    }

    pub fn reset(&mut self) {
        self.allocated_buffers.clear();
        self.total_allocated = 0;
        self.peak_usage = 0;
    }
}

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

/// Vulkan sparse matrix-vector multiplication handler
pub struct VulkanSpMatVec {
    device_info: VulkanDeviceInfo,
    memory_manager: VulkanMemoryManager,
    shader_cache: HashMap<String, Arc<Vec<u8>>>,
}

impl VulkanSpMatVec {
    /// Create a new Vulkan SpMV handler
    pub fn new() -> SparseResult<Self> {
        let device_info = VulkanDeviceInfo::detect();

        Ok(Self {
            device_info,
            memory_manager: VulkanMemoryManager::new(),
            shader_cache: HashMap::new(),
        })
    }

    /// Get device information
    pub fn device_info(&self) -> &VulkanDeviceInfo {
        &self.device_info
    }

    /// Get memory manager
    pub fn memory_manager(&self) -> &VulkanMemoryManager {
        &self.memory_manager
    }

    /// Get mutable memory manager
    pub fn memory_manager_mut(&mut self) -> &mut VulkanMemoryManager {
        &mut self.memory_manager
    }

    /// Execute sparse matrix-vector multiplication using Vulkan
    #[cfg(feature = "gpu")]
    pub fn execute_spmv<T>(
        &self,
        matrix: &CsrArray<T>,
        vector: &ArrayView1<T>,
        device: &GpuDevice,
    ) -> SparseResult<Array1<T>>
    where
        T: Float + SparseElement + Debug + Copy + std::iter::Sum,
    {
        self.execute_optimized_spmv(
            matrix,
            vector,
            device,
            VulkanOptimizationLevel::ComputeShader,
        )
    }

    /// Execute optimized sparse matrix-vector multiplication using Vulkan
    #[cfg(feature = "gpu")]
    pub fn execute_optimized_spmv<T>(
        &self,
        matrix: &CsrArray<T>,
        vector: &ArrayView1<T>,
        device: &GpuDevice,
        optimization_level: VulkanOptimizationLevel,
    ) -> SparseResult<Array1<T>>
    where
        T: Float + SparseElement + Debug + Copy + std::iter::Sum,
    {
        // Validate inputs
        let (nrows, ncols) = matrix.shape();
        if vector.len() != ncols {
            return Err(SparseError::DimensionMismatch {
                expected: ncols,
                found: vector.len(),
            });
        }

        // In a real implementation, we would:
        // 1. Create Vulkan command buffer
        // 2. Allocate GPU buffers for matrix data (indptr, indices, data)
        // 3. Allocate GPU buffer for input vector
        // 4. Allocate GPU buffer for output vector
        // 5. Load appropriate compute shader based on optimization level
        // 6. Dispatch compute shader with appropriate workgroup size
        // 7. Read back results

        // For now, fall back to CPU implementation
        matrix.dot_vector(vector)
    }

    /// CPU fallback implementation
    pub fn execute_spmv_cpu<T>(
        &self,
        matrix: &CsrArray<T>,
        vector: &ArrayView1<T>,
    ) -> SparseResult<Array1<T>>
    where
        T: Float + SparseElement + Debug + Copy + std::iter::Sum,
    {
        matrix.dot_vector(vector)
    }

    /// Get shader source code for CSR SpMV
    fn get_spmv_shader_source(&self, optimization_level: VulkanOptimizationLevel) -> &str {
        match optimization_level {
            VulkanOptimizationLevel::Basic => {
                // Basic compute shader
                r#"
#version 450

layout (local_size_x = 64, local_size_y = 1, local_size_z = 1) in;

layout(set = 0, binding = 0) readonly buffer IndptrBuffer {
    uint indptr[];
};

layout(set = 0, binding = 1) readonly buffer IndicesBuffer {
    uint indices[];
};

layout(set = 0, binding = 2) readonly buffer DataBuffer {
    float data[];
};

layout(set = 0, binding = 3) readonly buffer VectorBuffer {
    float vector[];
};

layout(set = 0, binding = 4) writeonly buffer ResultBuffer {
    float result[];
};

layout(push_constant) uniform PushConstants {
    uint nrows;
} pc;

void main() {
    uint row = gl_GlobalInvocationID.x;

    if (row >= pc.nrows) {
        return;
    }

    uint row_start = indptr[row];
    uint row_end = indptr[row + 1];

    float sum = 0.0;
    for (uint i = row_start; i < row_end; i++) {
        uint col = indices[i];
        sum += data[i] * vector[col];
    }

    result[row] = sum;
}
"#
            }
            VulkanOptimizationLevel::ComputeShader => {
                // Optimized with shared memory
                r#"
#version 450

layout (local_size_x = 64, local_size_y = 1, local_size_z = 1) in;

layout(set = 0, binding = 0) readonly buffer IndptrBuffer {
    uint indptr[];
};

layout(set = 0, binding = 1) readonly buffer IndicesBuffer {
    uint indices[];
};

layout(set = 0, binding = 2) readonly buffer DataBuffer {
    float data[];
};

layout(set = 0, binding = 3) readonly buffer VectorBuffer {
    float vector[];
};

layout(set = 0, binding = 4) writeonly buffer ResultBuffer {
    float result[];
};

layout(push_constant) uniform PushConstants {
    uint nrows;
} pc;

shared float shared_vector[256];

void main() {
    uint row = gl_GlobalInvocationID.x;
    uint local_id = gl_LocalInvocationID.x;

    if (row >= pc.nrows) {
        return;
    }

    uint row_start = indptr[row];
    uint row_end = indptr[row + 1];

    float sum = 0.0;
    for (uint i = row_start; i < row_end; i++) {
        uint col = indices[i];

        // Cooperative loading to shared memory for better cache utilization
        if (col < 256) {
            shared_vector[col] = vector[col];
            memoryBarrierShared();
            barrier();
            sum += data[i] * shared_vector[col];
        } else {
            sum += data[i] * vector[col];
        }
    }

    result[row] = sum;
}
"#
            }
            VulkanOptimizationLevel::Subgroup => {
                // Using subgroup operations
                r#"
#version 450
#extension GL_KHR_shader_subgroup_arithmetic : enable

layout (local_size_x = 64, local_size_y = 1, local_size_z = 1) in;

layout(set = 0, binding = 0) readonly buffer IndptrBuffer {
    uint indptr[];
};

layout(set = 0, binding = 1) readonly buffer IndicesBuffer {
    uint indices[];
};

layout(set = 0, binding = 2) readonly buffer DataBuffer {
    float data[];
};

layout(set = 0, binding = 3) readonly buffer VectorBuffer {
    float vector[];
};

layout(set = 0, binding = 4) writeonly buffer ResultBuffer {
    float result[];
};

layout(push_constant) uniform PushConstants {
    uint nrows;
} pc;

void main() {
    uint row = gl_GlobalInvocationID.x;

    if (row >= pc.nrows) {
        return;
    }

    uint row_start = indptr[row];
    uint row_end = indptr[row + 1];

    float sum = 0.0;
    for (uint i = row_start; i < row_end; i++) {
        uint col = indices[i];
        sum += data[i] * vector[col];
    }

    // Use subgroup reduction for better performance
    sum = subgroupAdd(sum);

    if (subgroupElect()) {
        result[row] = sum;
    }
}
"#
            }
            VulkanOptimizationLevel::Maximum => {
                // Maximum optimization with all features
                self.get_spmv_shader_source(VulkanOptimizationLevel::Subgroup)
            }
        }
    }

    /// Compile shader (placeholder - would use shaderc in real implementation)
    fn compile_shader(&mut self, source: &str, name: &str) -> SparseResult<Arc<Vec<u8>>> {
        // In real implementation, would compile GLSL to SPIR-V
        // For now, just cache a dummy bytecode
        let bytecode = Arc::new(source.as_bytes().to_vec());
        self.shader_cache.insert(name.to_string(), bytecode.clone());
        Ok(bytecode)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_vulkan_device_info() {
        let info = VulkanDeviceInfo::detect();
        assert!(!info.device_name.is_empty());
        assert!(info.optimal_workgroup_size() > 0);
    }

    #[test]
    fn test_vulkan_memory_manager() {
        let mut manager = VulkanMemoryManager::new();

        manager
            .allocate("buffer1".to_string(), 1024)
            .expect("Failed to allocate");
        assert_eq!(manager.current_usage(), 1024);

        manager
            .allocate("buffer2".to_string(), 2048)
            .expect("Failed to allocate");
        assert_eq!(manager.current_usage(), 3072);
        assert_eq!(manager.peak_usage(), 3072);

        manager.deallocate("buffer1").expect("Failed to deallocate");
        assert_eq!(manager.current_usage(), 2048);
        assert_eq!(manager.peak_usage(), 3072);

        manager.reset();
        assert_eq!(manager.current_usage(), 0);
    }

    #[test]
    fn test_vulkan_spmv_creation() {
        let result = VulkanSpMatVec::new();
        assert!(result.is_ok());

        let spmv = result.expect("Failed to create");
        assert!(spmv.device_info().optimal_workgroup_size() > 0);
    }

    #[test]
    fn test_vulkan_cpu_fallback() {
        let spmv = VulkanSpMatVec::new().expect("Failed to create");

        // Create a simple test matrix
        let rows = vec![0, 0, 1, 2];
        let cols = vec![0, 1, 1, 2];
        let data = vec![1.0, 2.0, 3.0, 4.0];
        let matrix = CsrArray::from_triplets(&rows, &cols, &data, (3, 3), false)
            .expect("Failed to create matrix");

        let vector = Array1::from_vec(vec![1.0, 2.0, 3.0]);
        let result = spmv
            .execute_spmv_cpu(&matrix, &vector.view())
            .expect("Failed to execute");

        assert_eq!(result.len(), 3);
    }

    #[test]
    fn test_shader_source_generation() {
        let spmv = VulkanSpMatVec::new().expect("Failed to create");

        let basic_shader = spmv.get_spmv_shader_source(VulkanOptimizationLevel::Basic);
        assert!(basic_shader.contains("#version 450"));
        assert!(basic_shader.contains("layout"));

        let optimized_shader = spmv.get_spmv_shader_source(VulkanOptimizationLevel::ComputeShader);
        assert!(optimized_shader.contains("shared"));

        let subgroup_shader = spmv.get_spmv_shader_source(VulkanOptimizationLevel::Subgroup);
        assert!(subgroup_shader.contains("subgroup"));
    }
}
