//! Custom CUDA kernels integration for torsh-nn via scirs2
//!
//! This module provides integration between torsh-nn and custom CUDA kernels
//! through the scirs2 backend system for high-performance GPU acceleration.

use parking_lot::RwLock;
use torsh_core::error::{Result, TorshError};
use torsh_tensor::Tensor;

// Conditional imports for std/no_std compatibility
#[cfg(feature = "std")]
use std::{collections::HashMap, string::String, sync::Arc, vec::Vec};

#[cfg(not(feature = "std"))]
use alloc::{boxed::Box, string::String, sync::Arc, vec::Vec};

#[cfg(not(feature = "std"))]
use hashbrown::HashMap;

/// Custom CUDA kernel function signature
pub type CudaKernelFn = dyn Fn(&[&Tensor], &mut [&mut Tensor]) -> Result<()> + Send + Sync;

/// Registry for custom CUDA kernels
pub struct CudaKernelRegistry {
    kernels: RwLock<HashMap<String, Arc<CudaKernelFn>>>,
}

impl CudaKernelRegistry {
    /// Create a new kernel registry
    pub fn new() -> Self {
        Self {
            kernels: RwLock::new(HashMap::new()),
        }
    }

    /// Register a custom CUDA kernel
    pub fn register_kernel<F>(&self, name: String, kernel: F) -> Result<()>
    where
        F: Fn(&[&Tensor], &mut [&mut Tensor]) -> Result<()> + Send + Sync + 'static,
    {
        let mut kernels = self.kernels.write();
        kernels.insert(name, Arc::new(kernel));
        Ok(())
    }

    /// Execute a registered kernel
    pub fn execute_kernel(
        &self,
        name: &str,
        inputs: &[&Tensor],
        outputs: &mut [&mut Tensor],
    ) -> Result<()> {
        let kernels = self.kernels.read();
        if let Some(kernel) = kernels.get(name) {
            kernel(inputs, outputs)
        } else {
            Err(TorshError::Other(format!("Kernel '{}' not found", name)))
        }
    }

    /// List all registered kernels
    pub fn list_kernels(&self) -> Vec<String> {
        self.kernels.read().keys().cloned().collect()
    }

    /// Check if a kernel is registered
    pub fn has_kernel(&self, name: &str) -> bool {
        self.kernels.read().contains_key(name)
    }

    /// Unregister a kernel
    pub fn unregister_kernel(&self, name: &str) -> bool {
        self.kernels.write().remove(name).is_some()
    }
}

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

/// Global kernel registry instance
static GLOBAL_KERNEL_REGISTRY: std::sync::OnceLock<CudaKernelRegistry> = std::sync::OnceLock::new();

/// Get the global kernel registry
pub fn global_kernel_registry() -> &'static CudaKernelRegistry {
    GLOBAL_KERNEL_REGISTRY.get_or_init(CudaKernelRegistry::new)
}

/// Custom neural network operations using CUDA kernels
pub struct CudaNeuralOps;

impl CudaNeuralOps {
    /// Fused convolution + batch norm + ReLU operation
    pub fn fused_conv_bn_relu(
        input: &Tensor,
        weight: &Tensor,
        bias: Option<&Tensor>,
        _bn_weight: &Tensor,
        _bn_bias: &Tensor,
        _bn_mean: &Tensor,
        _bn_var: &Tensor,
        _eps: f32,
        stride: (usize, usize),
        padding: (usize, usize),
    ) -> Result<Tensor> {
        // This would implement a fused kernel that combines convolution, batch norm, and ReLU
        // For now, we'll fall back to sequential operations through scirs2

        // Placeholder implementation - would use actual CUDA kernels
        let conv_output = input.conv2d(weight, bias, stride, padding, (1, 1), 1)?;
        // Batch norm and ReLU would be custom CUDA kernels
        conv_output.relu()
    }

    /// Flash Attention kernel implementation
    pub fn flash_attention(
        query: &Tensor,
        key: &Tensor,
        value: &Tensor,
        mask: Option<&Tensor>,
        scale: f32,
        _block_size: usize,
    ) -> Result<Tensor> {
        // Flash attention with memory-efficient block-wise computation
        // This would use a custom CUDA kernel for optimal performance

        let _batch_size = query.shape().dims()[0];
        let _seq_len = query.shape().dims()[1];
        let _head_dim = query.shape().dims()[2];

        // For now, implement a simplified version
        // In practice, this would use block-wise computation
        let scores = query.matmul(&key.transpose(0, 2)?)?; // Transpose key
        let scaled_scores = scores.mul_scalar(scale)?;

        let attention_weights = if let Some(mask) = mask {
            let masked_scores = scaled_scores.add_op(mask)?;
            masked_scores.softmax(-1)?
        } else {
            scaled_scores.softmax(-1)?
        };

        attention_weights.matmul(value)
    }

    /// Custom activation functions using CUDA kernels
    pub fn custom_activations() -> CustomActivations {
        CustomActivations::new()
    }

    /// Optimized matrix multiplication with custom kernels
    pub fn optimized_matmul(
        a: &Tensor,
        b: &Tensor,
        transpose_a: bool,
        transpose_b: bool,
        use_tensor_cores: bool,
    ) -> Result<Tensor> {
        // This would use optimized CUDA kernels, potentially with Tensor Cores
        // For now, delegate to standard implementation

        let (a_trans, b_trans) = if transpose_a || transpose_b {
            let a_work = if transpose_a {
                a.transpose(-2, -1)?
            } else {
                a.clone()
            };
            let b_work = if transpose_b {
                b.transpose(-2, -1)?
            } else {
                b.clone()
            };
            (a_work, b_work)
        } else {
            (a.clone(), b.clone())
        };

        // Use tensor cores hint if available
        if use_tensor_cores {
            // This would set hints for the CUDA backend to use tensor cores
            a_trans.matmul(&b_trans)
        } else {
            a_trans.matmul(&b_trans)
        }
    }

    /// Memory-efficient layer normalization
    pub fn memory_efficient_layer_norm(
        input: &Tensor,
        weight: &Tensor,
        bias: Option<&Tensor>,
        eps: f32,
        normalized_shape: &[usize],
    ) -> Result<Tensor> {
        // Custom kernel that reduces memory usage for layer norm
        crate::functional::layer_norm(input, normalized_shape, Some(weight), bias, eps)
    }

    /// Grouped convolution with custom kernel
    pub fn grouped_conv2d(
        input: &Tensor,
        weight: &Tensor,
        bias: Option<&Tensor>,
        stride: (usize, usize),
        padding: (usize, usize),
        dilation: (usize, usize),
        groups: usize,
    ) -> Result<Tensor> {
        // Custom grouped convolution implementation
        if groups == 1 {
            // Standard convolution
            input.conv2d(weight, bias, stride, padding, dilation, 1)
        } else {
            // Split into groups and process separately
            let in_channels = input.shape().dims()[1];
            let out_channels = weight.shape().dims()[0];
            let group_in_channels = in_channels / groups;
            let group_out_channels = out_channels / groups;

            let mut group_outputs = Vec::new();

            for g in 0..groups {
                let input_start = g * group_in_channels;
                let input_end = (g + 1) * group_in_channels;
                let output_start = g * group_out_channels;
                let output_end = (g + 1) * group_out_channels;

                let input_slice = input.slice(1, input_start, input_end)?;
                let weight_slice = weight.slice(0, output_start, output_end)?;

                let group_bias = if let Some(bias) = bias {
                    Some(bias.slice(0, output_start, output_end)?.to_tensor()?)
                } else {
                    None
                };

                let input_tensor = input_slice.to_tensor()?;
                let weight_tensor = weight_slice.to_tensor()?;
                let group_output = input_tensor.conv2d(
                    &weight_tensor,
                    group_bias.as_ref(),
                    stride,
                    padding,
                    dilation,
                    1,
                )?;
                group_outputs.push(group_output);
            }

            // Concatenate along channel dimension
            Tensor::cat(&group_outputs.iter().collect::<Vec<_>>(), 1)
        }
    }
}

/// Custom activation functions with CUDA kernels
pub struct CustomActivations {
    registry: Arc<CudaKernelRegistry>,
}

impl CustomActivations {
    pub fn new() -> Self {
        let registry = Arc::new(CudaKernelRegistry::new());

        // Register built-in custom activations
        Self::register_builtin_activations(&registry);

        Self { registry }
    }

    fn register_builtin_activations(registry: &CudaKernelRegistry) {
        // Register Swish (SiLU) activation
        let _ = registry.register_kernel(
            "swish".to_string(),
            |inputs: &[&Tensor], outputs: &mut [&mut Tensor]| {
                if inputs.len() != 1 || outputs.len() != 1 {
                    return Err(TorshError::Other(
                        "Swish requires 1 input and 1 output".to_string(),
                    ));
                }

                let input = inputs[0];
                let sigmoid = input.sigmoid()?;
                let result = input.mul_op(&sigmoid)?;
                *outputs[0] = result;
                Ok(())
            },
        );

        // Register GELU activation
        let _ = registry.register_kernel(
            "gelu".to_string(),
            |inputs: &[&Tensor], outputs: &mut [&mut Tensor]| {
                if inputs.len() != 1 || outputs.len() != 1 {
                    return Err(TorshError::Other(
                        "GELU requires 1 input and 1 output".to_string(),
                    ));
                }

                let input = inputs[0];
                // GELU(x) = 0.5 * x * (1 + tanh(sqrt(2/π) * (x + 0.044715 * x³)))
                let x_cubed = input.pow(3.0)?;
                let term = input.add_op(&x_cubed.mul_scalar(0.044715)?)?;
                let sqrt_2_pi = (2.0 / std::f32::consts::PI).sqrt();
                let tanh_input = term.mul_scalar(sqrt_2_pi)?;
                let tanh_result = tanh_input.tanh()?;
                let one_plus_tanh = tanh_result.add_scalar(1.0)?;
                let result = input.mul_op(&one_plus_tanh)?.mul_scalar(0.5)?;
                *outputs[0] = result;
                Ok(())
            },
        );

        // Register Mish activation
        let _ = registry.register_kernel(
            "mish".to_string(),
            |inputs: &[&Tensor], outputs: &mut [&mut Tensor]| {
                if inputs.len() != 1 || outputs.len() != 1 {
                    return Err(TorshError::Other(
                        "Mish requires 1 input and 1 output".to_string(),
                    ));
                }

                let input = inputs[0];
                // Mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + e^x))
                let softplus = input.exp()?.add_scalar(1.0)?.log()?;
                let tanh_softplus = softplus.tanh()?;
                let result = input.mul_op(&tanh_softplus)?;
                *outputs[0] = result;
                Ok(())
            },
        );
    }

    /// Apply custom activation function
    pub fn apply(&self, name: &str, input: &Tensor) -> Result<Tensor> {
        let mut output = input.clone();
        self.registry
            .execute_kernel(name, &[input], &mut [&mut output])?;
        Ok(output)
    }

    /// Register a new custom activation
    pub fn register<F>(&self, name: String, activation_fn: F) -> Result<()>
    where
        F: Fn(&[&Tensor], &mut [&mut Tensor]) -> Result<()> + Send + Sync + 'static,
    {
        self.registry.register_kernel(name, activation_fn)
    }

    /// List available activations
    pub fn list_activations(&self) -> Vec<String> {
        self.registry.list_kernels()
    }
}

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

/// Performance optimization utilities for CUDA kernels
pub struct CudaOptimizations;

impl CudaOptimizations {
    /// Auto-tune kernel parameters for optimal performance
    pub fn auto_tune_kernel(
        kernel_name: &str,
        _input_shapes: &[Vec<usize>],
        iterations: usize,
    ) -> Result<HashMap<String, String>> {
        // This would run the kernel with different parameters and measure performance
        // Return the best configuration as a JSON object

        let mut best_config = HashMap::new();
        best_config.insert("block_size".to_string(), "256".to_string());
        best_config.insert("shared_memory".to_string(), "0".to_string());
        best_config.insert("iterations_tested".to_string(), iterations.to_string());
        best_config.insert("kernel_name".to_string(), kernel_name.to_string());

        Ok(best_config)
    }

    /// Benchmark a kernel's performance
    pub fn benchmark_kernel(
        kernel_name: &str,
        inputs: &[&Tensor],
        iterations: usize,
    ) -> Result<KernelBenchmarkResult> {
        let start_time = std::time::Instant::now();

        // Run kernel multiple times for accurate measurement
        for _ in 0..iterations {
            let registry = global_kernel_registry();
            if registry.has_kernel(kernel_name) {
                // Would actually run the kernel here
                // For now, just add a small delay to simulate execution
                std::thread::sleep(std::time::Duration::from_micros(1));
            } else {
                return Err(TorshError::Other(format!(
                    "Kernel '{}' not found",
                    kernel_name
                )));
            }
        }

        let total_time = start_time.elapsed();
        let avg_time = total_time / iterations as u32;

        // Calculate theoretical FLOPS (this would be kernel-specific)
        let total_elements: usize = inputs.iter().map(|t| t.shape().numel()).sum();
        let flops_per_element = 1.0; // Simplified
        let total_flops = total_elements as f64 * flops_per_element * iterations as f64;
        let gflops = total_flops / 1e9 / total_time.as_secs_f64();

        Ok(KernelBenchmarkResult {
            kernel_name: kernel_name.to_string(),
            iterations,
            total_time,
            avg_time,
            gflops,
            memory_bandwidth_gb_s: 0.0, // Would calculate actual memory bandwidth
        })
    }

    /// Profile memory usage of a kernel
    pub fn profile_memory_usage(kernel_name: &str, inputs: &[&Tensor]) -> Result<MemoryProfile> {
        let total_input_memory: usize = inputs
            .iter()
            .map(|t| t.shape().numel() * std::mem::size_of::<f32>()) // Assuming f32
            .sum();

        Ok(MemoryProfile {
            kernel_name: kernel_name.to_string(),
            input_memory_bytes: total_input_memory,
            output_memory_bytes: total_input_memory, // Simplified
            peak_memory_bytes: total_input_memory * 2, // Simplified
            memory_efficiency: 0.8,                  // Would calculate actual efficiency
        })
    }
}

/// Kernel benchmark results
#[derive(Debug, Clone)]
pub struct KernelBenchmarkResult {
    pub kernel_name: String,
    pub iterations: usize,
    pub total_time: std::time::Duration,
    pub avg_time: std::time::Duration,
    pub gflops: f64,
    pub memory_bandwidth_gb_s: f64,
}

/// Memory usage profile for a kernel
#[derive(Debug, Clone)]
pub struct MemoryProfile {
    pub kernel_name: String,
    pub input_memory_bytes: usize,
    pub output_memory_bytes: usize,
    pub peak_memory_bytes: usize,
    pub memory_efficiency: f64,
}

/// Utilities for kernel development and testing
pub mod utils {
    use super::*;

    /// Validate kernel inputs and outputs
    pub fn validate_kernel_args(inputs: &[&Tensor], outputs: &[&Tensor]) -> Result<()> {
        // Check that all tensors are on the same device
        if let Some(first_input) = inputs.first() {
            let device = first_input.device();

            for input in inputs.iter().skip(1) {
                if input.device() != device {
                    return Err(TorshError::Other(
                        "All input tensors must be on the same device".to_string(),
                    ));
                }
            }

            for output in outputs {
                if output.device() != device {
                    return Err(TorshError::Other(
                        "All output tensors must be on the same device as inputs".to_string(),
                    ));
                }
            }
        }

        Ok(())
    }

    /// Generate test data for kernel development
    pub fn generate_test_tensors(shapes: &[Vec<usize>]) -> Result<Vec<Tensor>> {
        shapes
            .iter()
            .map(|shape| torsh_tensor::creation::randn(shape))
            .collect()
    }

    /// Compare kernel output with reference implementation
    pub fn compare_with_reference<F>(
        kernel_output: &Tensor,
        reference_fn: F,
        inputs: &[&Tensor],
        _tolerance: f32,
    ) -> Result<bool>
    where
        F: Fn(&[&Tensor]) -> Result<Tensor>,
    {
        let reference_output = reference_fn(inputs)?;

        // Check shape compatibility
        if kernel_output.shape() != reference_output.shape() {
            return Ok(false);
        }

        // Compute element-wise difference
        let diff = kernel_output.sub(&reference_output)?;
        let abs_diff = diff.abs()?;
        let _max_diff = abs_diff.max(None, false)?; // Get maximum absolute difference

        // Check if maximum difference is within tolerance
        // This is a simplified check - would need to extract the actual scalar value
        Ok(true) // Placeholder - would do actual comparison
    }
}

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

    #[test]
    fn test_kernel_registry() {
        let registry = CudaKernelRegistry::new();

        // Register a simple kernel
        let result = registry.register_kernel(
            "test_kernel".to_string(),
            |inputs: &[&Tensor], outputs: &mut [&mut Tensor]| {
                if inputs.len() == 1 && outputs.len() == 1 {
                    *outputs[0] = inputs[0].clone();
                    Ok(())
                } else {
                    Err(TorshError::Other("Invalid arguments".to_string()))
                }
            },
        );

        assert!(result.is_ok());
        assert!(registry.has_kernel("test_kernel"));
        assert_eq!(registry.list_kernels(), vec!["test_kernel"]);
    }

    #[test]
    fn test_custom_activations() {
        let activations = CustomActivations::new();
        let input = randn(&[2, 3, 4]).unwrap();

        // Test built-in activations
        let swish_result = activations.apply("swish", &input);
        assert!(swish_result.is_ok());

        let gelu_result = activations.apply("gelu", &input);
        assert!(gelu_result.is_ok());

        let mish_result = activations.apply("mish", &input);
        assert!(mish_result.is_ok());
    }

    #[test]
    fn test_utils_validation() {
        let tensor1 = randn(&[2, 3]).unwrap();
        let tensor2 = randn(&[2, 3]).unwrap();

        let inputs = vec![&tensor1, &tensor2];
        let outputs = vec![&tensor1];

        let result = utils::validate_kernel_args(&inputs, &outputs);
        assert!(result.is_ok());
    }

    #[test]
    fn test_generate_test_tensors() {
        let shapes = vec![vec![2, 3], vec![4, 5, 6]];
        let tensors = utils::generate_test_tensors(&shapes).unwrap();

        assert_eq!(tensors.len(), 2);
        assert_eq!(tensors[0].shape().dims(), &[2, 3]);
        assert_eq!(tensors[1].shape().dims(), &[4, 5, 6]);
    }

    #[test]
    fn test_benchmark_result() {
        let result = KernelBenchmarkResult {
            kernel_name: "test".to_string(),
            iterations: 100,
            total_time: std::time::Duration::from_millis(100),
            avg_time: std::time::Duration::from_millis(1),
            gflops: 10.0,
            memory_bandwidth_gb_s: 500.0,
        };

        assert_eq!(result.kernel_name, "test");
        assert_eq!(result.iterations, 100);
        assert!((result.gflops - 10.0).abs() < 1e-6);
    }
}
