rustorch 0.6.29

use crate::dtype::DType;
/// CoreML unified trait implementations
/// CoreML統一trait実装
use crate::error::{RusTorchError, RusTorchResult};
use crate::gpu::hybrid_executor::{HybridExecution, TensorInfo};
use crate::gpu::{DeviceType, OpType};
use crate::tensor::Tensor;
use ndarray::ScalarOperand;
use num_traits::{Float, FromPrimitive};

// CoreML bindings - conditional compilation for macOS
#[cfg(all(feature = "coreml", target_os = "macos"))]
use objc2_core_ml::*;
#[cfg(all(feature = "coreml", target_os = "macos"))]
use objc2_foundation::*;

// Standard library imports for CoreML integration
#[cfg(all(feature = "coreml", target_os = "macos"))]
use std::collections::HashMap;
#[cfg(all(feature = "coreml", target_os = "macos"))]
use std::sync::{Arc, Mutex, OnceLock};

/// CoreML-specific operations
/// CoreML専用演算
#[cfg(feature = "coreml")]
pub mod coreml_backend {
    use super::*;

    /// CoreML device manager for operations
    /// 演算用CoreMLデバイスマネージャー
    pub struct CoreMLDevice {
        device_id: usize,
        is_available: bool,
    }

    impl CoreMLDevice {
        pub fn new(device_id: usize) -> RusTorchResult<Self> {
            Ok(Self {
                device_id,
                is_available: Self::check_availability(),
            })
        }

        fn check_availability() -> bool {
            // Check if CoreML is available on the system
            #[cfg(target_os = "macos")]
            {
                // Use objc2-core-ml to check availability
                true // Simplified for now
            }
            #[cfg(not(target_os = "macos"))]
            {
                false
            }
        }

        pub fn is_available(&self) -> bool {
            self.is_available
        }
    }

    /// CoreML computation graph for operations
    /// 演算用CoreML計算グラフ
    pub struct CoreMLGraph {
        device: CoreMLDevice,
    }

    impl CoreMLGraph {
        pub fn new(device_id: usize) -> RusTorchResult<Self> {
            Ok(Self {
                device: CoreMLDevice::new(device_id)?,
            })
        }

        /// Execute matrix multiplication using CoreML
        /// CoreMLを使用した行列乗算の実行
        pub fn matmul<T>(&self, a: &Tensor<T>, b: &Tensor<T>) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            if !self.device.is_available() {
                return Err(RusTorchError::UnsupportedDevice(
                    "CoreML not available".to_string(),
                ));
            }

            // For now, delegate to CPU implementation
            // TODO: Implement actual CoreML computation using objc2-core-ml
            self.cpu_fallback_matmul(a, b)
        }

        /// Execute 2D convolution using CoreML
        /// CoreMLを使用した2D畳み込みの実行
        pub fn conv2d<T>(
            &self,
            input: &Tensor<T>,
            kernel: &Tensor<T>,
            stride: &[usize],
            padding: &[usize],
        ) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            if !self.device.is_available() {
                return Err(RusTorchError::UnsupportedDevice(
                    "CoreML not available".to_string(),
                ));
            }

            // For now, delegate to CPU implementation
            // TODO: Implement actual CoreML convolution using objc2-core-ml
            self.cpu_fallback_conv2d(input, kernel, stride, padding)
        }

        /// Execute activation function using CoreML
        /// CoreMLを使用した活性化関数の実行
        pub fn activation<T>(
            &self,
            input: &Tensor<T>,
            activation_type: ActivationType,
        ) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            if !self.device.is_available() {
                return Err(RusTorchError::UnsupportedDevice(
                    "CoreML not available".to_string(),
                ));
            }

            match activation_type {
                ActivationType::ReLU => self.cpu_fallback_relu(input),
                ActivationType::Sigmoid => self.cpu_fallback_sigmoid(input),
                ActivationType::Tanh => self.cpu_fallback_tanh(input),
                ActivationType::Softmax => self.cpu_fallback_softmax(input),
                ActivationType::GELU => self.cpu_fallback_gelu(input),
            }
        }

        // CPU fallback implementations
        fn cpu_fallback_matmul<T>(&self, a: &Tensor<T>, b: &Tensor<T>) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            // Use existing CPU implementation
            a.matmul(b)
        }

        fn cpu_fallback_conv2d<T>(
            &self,
            input: &Tensor<T>,
            kernel: &Tensor<T>,
            _stride: &[usize],
            _padding: &[usize],
        ) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            // Simplified CPU convolution fallback
            // TODO: Implement proper CPU convolution
            let output_shape = input.shape().to_vec();
            Ok(Tensor::zeros(&output_shape))
        }

        fn cpu_fallback_relu<T>(&self, input: &Tensor<T>) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            Err(RusTorchError::TensorOp {
                message: "ReLU activation not yet implemented".to_string(),
                source: None,
            })
        }

        fn cpu_fallback_sigmoid<T>(&self, input: &Tensor<T>) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            Err(RusTorchError::TensorOp {
                message: "Sigmoid fallback not yet implemented".to_string(),
                source: None,
            })
        }

        fn cpu_fallback_tanh<T>(&self, input: &Tensor<T>) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            Ok(input.tanh())
        }

        fn cpu_fallback_softmax<T>(&self, input: &Tensor<T>) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            Err(RusTorchError::TensorOp {
                message: "Softmax fallback not yet implemented".to_string(),
                source: None,
            })
        }

        fn cpu_fallback_gelu<T>(&self, input: &Tensor<T>) -> RusTorchResult<Tensor<T>>
        where
            T: Float + FromPrimitive + ScalarOperand + 'static,
        {
            Err(RusTorchError::TensorOp {
                message: "GELU fallback not yet implemented".to_string(),
                source: None,
            })
        }
    }

    /// Activation function types supported by CoreML
    /// CoreMLでサポートされる活性化関数タイプ
    #[derive(Debug, Clone, Copy)]
    pub enum ActivationType {
        ReLU,
        Sigmoid,
        Tanh,
        Softmax,
        GELU,
    }
}

/// CoreML Linear Algebra operations
/// CoreML線形代数演算
pub trait CoreMLLinearAlgebra<T: Float + FromPrimitive + ScalarOperand + 'static> {
    /// CoreML matrix multiplication
    /// CoreML行列乗算
    fn coreml_matmul(&self, other: &Self) -> RusTorchResult<Tensor<T>>;

    /// CoreML batch matrix multiplication
    /// CoreMLバッチ行列乗算
    fn coreml_batch_matmul(&self, other: &Self) -> RusTorchResult<Tensor<T>>;

    /// CoreML matrix-vector multiplication
    /// CoreML行列ベクトル乗算
    fn coreml_matvec(&self, vector: &Self) -> RusTorchResult<Tensor<T>>;
}

/// CoreML Convolution operations
/// CoreML畳み込み演算
pub trait CoreMLConvolution<T: Float + FromPrimitive + ScalarOperand + 'static> {
    /// CoreML 2D convolution
    /// CoreML 2D畳み込み
    fn coreml_conv2d(
        &self,
        kernel: &Self,
        stride: &[usize],
        padding: &[usize],
    ) -> RusTorchResult<Tensor<T>>;

    /// CoreML batch convolution
    /// CoreMLバッチ畳み込み
    fn coreml_batch_conv2d(
        &self,
        kernel: &Self,
        stride: &[usize],
        padding: &[usize],
    ) -> RusTorchResult<Tensor<T>>;

    /// CoreML transposed convolution
    /// CoreML転置畳み込み
    fn coreml_conv2d_transpose(
        &self,
        kernel: &Self,
        stride: &[usize],
        padding: &[usize],
    ) -> RusTorchResult<Tensor<T>>;
}

/// CoreML Activation operations
/// CoreML活性化演算
pub trait CoreMLActivation<T: Float + FromPrimitive + ScalarOperand + 'static> {
    /// CoreML ReLU activation
    /// CoreML ReLU活性化
    fn coreml_relu(&self) -> RusTorchResult<Tensor<T>>;

    /// CoreML Sigmoid activation
    /// CoreML Sigmoid活性化
    fn coreml_sigmoid(&self) -> RusTorchResult<Tensor<T>>;

    /// CoreML Tanh activation
    /// CoreML Tanh活性化
    fn coreml_tanh(&self) -> RusTorchResult<Tensor<T>>;

    /// CoreML Softmax activation
    /// CoreML Softmax活性化
    fn coreml_softmax(&self, dim: isize) -> RusTorchResult<Tensor<T>>;

    /// CoreML GELU activation
    /// CoreML GELU活性化
    fn coreml_gelu(&self) -> RusTorchResult<Tensor<T>>;

    /// CoreML Leaky ReLU activation
    /// CoreML Leaky ReLU活性化
    fn coreml_leaky_relu(&self, negative_slope: f64) -> RusTorchResult<Tensor<T>>;

    /// CoreML ELU activation
    /// CoreML ELU活性化
    fn coreml_elu(&self, alpha: f64) -> RusTorchResult<Tensor<T>>;

    /// CoreML Swish activation
    /// CoreML Swish活性化
    fn coreml_swish(&self) -> RusTorchResult<Tensor<T>>;
}

/// Implement CoreML traits for Tensor
/// TensorにCoreML traitを実装
impl<T> CoreMLLinearAlgebra<T> for Tensor<T>
where
    T: Float + FromPrimitive + ScalarOperand + 'static,
{
    fn coreml_matmul(&self, other: &Self) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            let graph = coreml_backend::CoreMLGraph::new(0)?;
            graph.matmul(self, other)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_batch_matmul(&self, other: &Self) -> RusTorchResult<Tensor<T>> {
        // For now, delegate to regular matmul
        self.coreml_matmul(other)
    }

    fn coreml_matvec(&self, vector: &Self) -> RusTorchResult<Tensor<T>> {
        // For now, delegate to regular matmul
        self.coreml_matmul(vector)
    }
}

impl<T> CoreMLConvolution<T> for Tensor<T>
where
    T: Float + FromPrimitive + ScalarOperand + 'static,
{
    fn coreml_conv2d(
        &self,
        kernel: &Self,
        stride: &[usize],
        padding: &[usize],
    ) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            let graph = coreml_backend::CoreMLGraph::new(0)?;
            graph.conv2d(self, kernel, stride, padding)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_batch_conv2d(
        &self,
        kernel: &Self,
        stride: &[usize],
        padding: &[usize],
    ) -> RusTorchResult<Tensor<T>> {
        // For now, delegate to regular conv2d
        self.coreml_conv2d(kernel, stride, padding)
    }

    fn coreml_conv2d_transpose(
        &self,
        kernel: &Self,
        stride: &[usize],
        padding: &[usize],
    ) -> RusTorchResult<Tensor<T>> {
        // TODO: Implement transposed convolution
        self.coreml_conv2d(kernel, stride, padding)
    }
}

impl<T> CoreMLActivation<T> for Tensor<T>
where
    T: Float + FromPrimitive + ScalarOperand + Send + Sync + 'static,
{
    fn coreml_relu(&self) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            let graph = coreml_backend::CoreMLGraph::new(0)?;
            graph.activation(self, coreml_backend::ActivationType::ReLU)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_sigmoid(&self) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            let graph = coreml_backend::CoreMLGraph::new(0)?;
            graph.activation(self, coreml_backend::ActivationType::Sigmoid)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_tanh(&self) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            let graph = coreml_backend::CoreMLGraph::new(0)?;
            graph.activation(self, coreml_backend::ActivationType::Tanh)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_softmax(&self, _dim: isize) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            let graph = coreml_backend::CoreMLGraph::new(0)?;
            graph.activation(self, coreml_backend::ActivationType::Softmax)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_gelu(&self) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            let graph = coreml_backend::CoreMLGraph::new(0)?;
            graph.activation(self, coreml_backend::ActivationType::GELU)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_leaky_relu(&self, _negative_slope: f64) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            // For now, fallback to CPU implementation
            // TODO: Implement actual CoreML leaky ReLU when supported
            self.leaky_relu_fallback(_negative_slope)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_elu(&self, _alpha: f64) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            // For now, fallback to CPU implementation
            // TODO: Implement actual CoreML ELU when supported
            self.elu_fallback(_alpha)
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }

    fn coreml_swish(&self) -> RusTorchResult<Tensor<T>> {
        #[cfg(feature = "coreml")]
        {
            // For now, fallback to CPU implementation
            // TODO: Implement actual CoreML Swish when supported
            self.swish_fallback()
        }
        #[cfg(not(feature = "coreml"))]
        {
            Err(RusTorchError::UnsupportedOperation(
                "CoreML feature not enabled".to_string(),
            ))
        }
    }
}

/// Implement HybridExecution for Tensor with CoreML support
/// CoreMLサポート付きTensorにHybridExecutionを実装
impl<T> HybridExecution<T> for Tensor<T>
where
    T: Float + FromPrimitive + ScalarOperand + 'static,
{
    fn hybrid_operation<F, R>(&self, op_type: OpType, operation: F) -> RusTorchResult<R>
    where
        F: Fn(DeviceType) -> RusTorchResult<R>,
    {
        use crate::gpu::hybrid_executor::HybridExecutor;

        let executor = HybridExecutor::new();
        let tensor_info = self.tensor_info();
        executor.hybrid_operation(op_type, tensor_info, operation)
    }

    fn tensor_info(&self) -> TensorInfo {
        let element_size = std::mem::size_of::<T>();
        let total_elements = self.shape().iter().product::<usize>();

        let dtype = if std::any::TypeId::of::<T>() == std::any::TypeId::of::<f32>() {
            DType::Float32
        } else if std::any::TypeId::of::<T>() == std::any::TypeId::of::<f64>() {
            DType::Float64
        } else {
            DType::Float32 // default fallback
        };

        TensorInfo {
            dtype,
            shape: self.shape().to_vec(),
            requires_custom_kernel: false,
            memory_size_bytes: total_elements * element_size,
        }
    }
}

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

    #[test]
    fn test_coreml_availability() {
        #[cfg(feature = "coreml")]
        {
            let device = coreml_backend::CoreMLDevice::new(0);
            assert!(device.is_ok());
        }
    }

    #[test]
    fn test_coreml_matmul_fallback() {
        let a = Tensor::<f32>::randn(&[4, 4]);
        let b = Tensor::<f32>::randn(&[4, 4]);

        // This should fallback to CPU implementation
        let result = a.coreml_matmul(&b);

        #[cfg(feature = "coreml")]
        {
            assert!(result.is_ok());
            let output = result.unwrap();
            assert_eq!(output.shape(), &[4, 4]);
        }
        #[cfg(not(feature = "coreml"))]
        {
            assert!(result.is_err());
        }
    }

    #[test]
    fn test_hybrid_execution() {
        let tensor = Tensor::<f32>::randn(&[100, 100]);

        let result = tensor.hybrid_operation(OpType::LinearAlgebra, |device| match device {
            DeviceType::CoreML(_) => tensor.coreml_matmul(&tensor),
            DeviceType::Cpu => tensor.matmul(&tensor),
            _ => Err(RusTorchError::UnsupportedDevice(
                "Unsupported device type".to_string(),
            )),
        });

        assert!(result.is_ok());
    }
}