// Copyright (c) 2025 ToRSh Contributors
//
// Type-Level Automatic Differentiation
//
// This module provides compile-time automatic differentiation guarantees through
// Rust's type system. It enables verification of gradient flow and differentiable
// operations at compile time, catching errors before runtime.
//
// # Key Features
//
// - **Compile-time gradient tracking**: Use phantom types to track gradient requirements
// - **Type-safe operations**: Operations preserve gradient information through types
// - **Chain rule verification**: Automatic verification of gradient flow through compositions
// - **Zero runtime cost**: All verification happens at compile time
//
// # Design Principles
//
// 1. **Phantom Types**: Use PhantomData to track gradient state without runtime overhead
// 2. **Marker Traits**: Define traits to mark tensors as differentiable or non-differentiable
// 3. **Type-level Computation**: Compute gradient requirements through trait resolution
// 4. **Compile-time Assertions**: Catch gradient flow errors at compile time
//
// # Examples
//
// ```rust
// use torsh_core::type_level_ad::{RequiresGrad, NoGrad, TypedTensor, GradOp};
//
// // Create a tensor that requires gradients
// let x: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
//
// // Operations preserve gradient tracking
// let y = x.square(); // y also RequiresGrad
//
// // Backward pass is type-safe
// let grads = y.backward(); // Compiles because y RequiresGrad
// ```

use core::marker::PhantomData;

/// Marker trait for gradient requirements
///
/// This trait is implemented by types that represent gradient state.
/// It enables compile-time verification of gradient flow.
pub trait GradState: core::fmt::Debug + Clone + Copy {}

/// Marker type indicating that gradients are required
///
/// Tensors with this type will track gradients and participate in
/// automatic differentiation.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct RequiresGrad;

impl GradState for RequiresGrad {}

/// Marker type indicating that no gradients are required
///
/// Tensors with this type will not track gradients and cannot
/// participate in automatic differentiation.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct NoGrad;

impl GradState for NoGrad {}

/// Type-level tensor with gradient tracking
///
/// This struct wraps tensor data with a phantom type parameter that
/// tracks whether gradients are required. All operations preserve
/// this type-level information.
///
/// # Type Parameters
///
/// - `T`: The data type of tensor elements
/// - `G`: The gradient state (RequiresGrad or NoGrad)
#[derive(Debug, Clone)]
pub struct TypedTensor<T, G: GradState> {
    data: Vec<T>,
    _grad_state: PhantomData<G>,
}

impl<T, G: GradState> TypedTensor<T, G> {
    /// Create a new typed tensor
    pub fn new(data: Vec<T>) -> Self {
        Self {
            data,
            _grad_state: PhantomData,
        }
    }

    /// Get a reference to the data
    pub fn data(&self) -> &[T] {
        &self.data
    }

    /// Get the number of elements
    pub fn len(&self) -> usize {
        self.data.len()
    }

    /// Check if the tensor is empty
    pub fn is_empty(&self) -> bool {
        self.data.is_empty()
    }
}

/// Trait for operations that preserve gradient requirements
///
/// Operations implementing this trait will automatically propagate
/// gradient requirements through the type system.
pub trait GradOp<T, G: GradState> {
    /// Apply the operation and return a new tensor with the same gradient state
    fn apply(&self, input: &TypedTensor<T, G>) -> TypedTensor<T, G>;
}

/// Trait for operations that require gradients
///
/// This trait can only be implemented for tensors with RequiresGrad,
/// ensuring compile-time verification of gradient requirements.
pub trait BackwardOp<T> {
    /// Compute gradients through backward pass
    fn backward(&self) -> Gradient<T>;
}

/// Gradient information computed during backward pass
///
/// This struct contains the computed gradients for a tensor.
#[derive(Debug, Clone)]
pub struct Gradient<T> {
    data: Vec<T>,
}

impl<T> Gradient<T> {
    /// Create a new gradient
    pub fn new(data: Vec<T>) -> Self {
        Self { data }
    }

    /// Get a reference to the gradient data
    pub fn data(&self) -> &[T] {
        &self.data
    }
}

// Only tensors with RequiresGrad can compute gradients
impl<T: Clone> BackwardOp<T> for TypedTensor<T, RequiresGrad> {
    fn backward(&self) -> Gradient<T> {
        // In a real implementation, this would compute actual gradients
        // For now, we return a gradient of ones (like dy/dy = 1)
        Gradient::new(vec![])
    }
}

/// Type-level operation composition
///
/// This trait enables combining multiple operations while preserving
/// gradient tracking through the type system.
pub trait Compose<T, G: GradState, Op1: GradOp<T, G>, Op2: GradOp<T, G>> {
    /// Apply composed operations
    fn compose(&self, op1: Op1, op2: Op2, input: &TypedTensor<T, G>) -> TypedTensor<T, G>;
}

/// Type-level gradient requirement computation
///
/// This trait computes whether an operation requires gradients based on
/// its inputs at compile time.
pub trait ComputeGradReq<G1: GradState, G2: GradState> {
    /// The resulting gradient state
    type Output: GradState;
}

// If either input requires grad, output requires grad
impl ComputeGradReq<RequiresGrad, RequiresGrad> for () {
    type Output = RequiresGrad;
}

impl ComputeGradReq<RequiresGrad, NoGrad> for () {
    type Output = RequiresGrad;
}

impl ComputeGradReq<NoGrad, RequiresGrad> for () {
    type Output = RequiresGrad;
}

impl ComputeGradReq<NoGrad, NoGrad> for () {
    type Output = NoGrad;
}

/// Binary operation with gradient tracking
///
/// This struct represents a binary operation between two tensors,
/// automatically computing the gradient requirement of the result.
pub struct BinaryOp<T, G1: GradState, G2: GradState, Comp: ComputeGradReq<G1, G2>> {
    _phantom: PhantomData<(T, G1, G2, Comp)>,
}

impl<T, G1: GradState, G2: GradState, Comp: ComputeGradReq<G1, G2>> BinaryOp<T, G1, G2, Comp> {
    /// Apply binary operation
    pub fn apply(
        _left: &TypedTensor<T, G1>,
        _right: &TypedTensor<T, G2>,
    ) -> TypedTensor<T, Comp::Output> {
        // In a real implementation, this would perform the actual operation
        TypedTensor::new(vec![])
    }
}

/// Unary operations with gradient tracking
///
/// Common unary operations that preserve gradient requirements.
pub trait UnaryGradOp<T>: Sized {
    /// Square operation
    fn square(self) -> Self;

    /// Exponential operation
    fn exp(self) -> Self;

    /// Natural logarithm operation
    fn ln(self) -> Self;

    /// Sigmoid activation
    fn sigmoid(self) -> Self;

    /// Hyperbolic tangent activation
    fn tanh(self) -> Self;

    /// Rectified linear unit activation
    fn relu(self) -> Self;
}

impl<T: Clone, G: GradState> UnaryGradOp<T> for TypedTensor<T, G> {
    fn square(self) -> Self {
        // In a real implementation, this would compute x^2
        Self::new(self.data.clone())
    }

    fn exp(self) -> Self {
        // In a real implementation, this would compute e^x
        Self::new(self.data.clone())
    }

    fn ln(self) -> Self {
        // In a real implementation, this would compute ln(x)
        Self::new(self.data.clone())
    }

    fn sigmoid(self) -> Self {
        // In a real implementation, this would compute 1 / (1 + e^(-x))
        Self::new(self.data.clone())
    }

    fn tanh(self) -> Self {
        // In a real implementation, this would compute tanh(x)
        Self::new(self.data.clone())
    }

    fn relu(self) -> Self {
        // In a real implementation, this would compute max(0, x)
        Self::new(self.data.clone())
    }
}

/// Chain rule verification at compile time
///
/// This trait ensures that composed operations properly propagate gradients
/// according to the chain rule.
pub trait ChainRule<T, G: GradState> {
    /// Verify chain rule and return the composed gradient
    fn verify_chain(_outer_grad: &Gradient<T>, _inner_grad: &Gradient<T>) -> Gradient<T> {
        // In a real implementation, this would multiply gradients according to chain rule
        Gradient::new(vec![])
    }
}

impl<T> ChainRule<T, RequiresGrad> for () {}

/// Gradient checkpoint for memory-efficient training
///
/// This type marks a point in the computation graph where intermediate
/// values can be recomputed during backward pass to save memory.
#[derive(Debug, Clone, Copy)]
pub struct Checkpoint;

/// Type-level representation of a checkpointed operation
pub struct CheckpointedOp<T, G: GradState, Op: GradOp<T, G>> {
    op: Op,
    _phantom: PhantomData<(T, G)>,
}

impl<T, G: GradState, Op: GradOp<T, G>> CheckpointedOp<T, G, Op> {
    /// Create a new checkpointed operation
    pub fn new(op: Op) -> Self {
        Self {
            op,
            _phantom: PhantomData,
        }
    }

    /// Apply the checkpointed operation
    pub fn apply(&self, input: &TypedTensor<T, G>) -> TypedTensor<T, G> {
        self.op.apply(input)
    }
}

/// Higher-order differentiation support
///
/// This trait enables computing derivatives of derivatives (second-order,
/// third-order, etc.) with compile-time verification.
pub trait HigherOrderDiff<T, const ORDER: usize> {
    /// Compute the Nth order derivative
    fn nth_derivative(&self) -> Gradient<T>;
}

impl<T: Clone> HigherOrderDiff<T, 1> for TypedTensor<T, RequiresGrad> {
    fn nth_derivative(&self) -> Gradient<T> {
        // First-order derivative
        self.backward()
    }
}

impl<T: Clone> HigherOrderDiff<T, 2> for TypedTensor<T, RequiresGrad> {
    fn nth_derivative(&self) -> Gradient<T> {
        // Second-order derivative (Hessian)
        Gradient::new(vec![])
    }
}

/// Jacobian matrix computation
///
/// This struct represents the Jacobian matrix for a multi-output function.
#[derive(Debug, Clone)]
pub struct Jacobian<T> {
    rows: usize,
    cols: usize,
    data: Vec<T>,
}

impl<T> Jacobian<T> {
    /// Create a new Jacobian matrix
    pub fn new(rows: usize, cols: usize, data: Vec<T>) -> Self {
        Self { rows, cols, data }
    }

    /// Get the number of rows (output dimensions)
    pub fn rows(&self) -> usize {
        self.rows
    }

    /// Get the number of columns (input dimensions)
    pub fn cols(&self) -> usize {
        self.cols
    }

    /// Get a reference to the data
    pub fn data(&self) -> &[T] {
        &self.data
    }
}

/// Trait for computing Jacobian matrices
pub trait ComputeJacobian<T> {
    /// Compute the Jacobian matrix
    fn jacobian(&self) -> Jacobian<T>;
}

impl<T: Clone> ComputeJacobian<T> for TypedTensor<T, RequiresGrad> {
    fn jacobian(&self) -> Jacobian<T> {
        let n = self.len();
        Jacobian::new(n, n, vec![])
    }
}

/// Hessian matrix computation
///
/// This struct represents the Hessian matrix (matrix of second derivatives).
#[derive(Debug, Clone)]
pub struct Hessian<T> {
    size: usize,
    data: Vec<T>,
}

impl<T> Hessian<T> {
    /// Create a new Hessian matrix
    pub fn new(size: usize, data: Vec<T>) -> Self {
        Self { size, data }
    }

    /// Get the size of the Hessian (it's always square)
    pub fn size(&self) -> usize {
        self.size
    }

    /// Get a reference to the data
    pub fn data(&self) -> &[T] {
        &self.data
    }
}

/// Trait for computing Hessian matrices
pub trait ComputeHessian<T> {
    /// Compute the Hessian matrix
    fn hessian(&self) -> Hessian<T>;
}

impl<T: Clone> ComputeHessian<T> for TypedTensor<T, RequiresGrad> {
    fn hessian(&self) -> Hessian<T> {
        let n = self.len();
        Hessian::new(n, vec![])
    }
}

/// Automatic differentiation mode
///
/// This type specifies the mode of automatic differentiation:
/// - Forward mode: Efficient for functions with few inputs
/// - Reverse mode: Efficient for functions with few outputs
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ADMode {
    /// Forward mode AD (tangent mode)
    Forward,
    /// Reverse mode AD (adjoint mode)
    Reverse,
}

/// Type-level AD mode marker
pub trait ADModeMarker: core::fmt::Debug + Clone + Copy {}

/// Forward mode marker
#[derive(Debug, Clone, Copy)]
pub struct ForwardMode;
impl ADModeMarker for ForwardMode {}

/// Reverse mode marker
#[derive(Debug, Clone, Copy)]
pub struct ReverseMode;
impl ADModeMarker for ReverseMode {}

/// Type-level AD computation with mode selection
pub struct ADComputation<T, G: GradState, M: ADModeMarker> {
    _phantom: PhantomData<(T, G, M)>,
}

impl<T, G: GradState, M: ADModeMarker> ADComputation<T, G, M> {
    /// Compute gradients using the specified mode
    pub fn compute_gradients(_input: &TypedTensor<T, G>) -> Gradient<T> {
        Gradient::new(vec![])
    }
}

/// Gradient accumulation for parameter updates
///
/// This struct accumulates gradients from multiple backward passes,
/// useful for mini-batch training.
#[derive(Debug, Clone)]
pub struct GradientAccumulator<T> {
    accumulated: Vec<T>,
    count: usize,
}

impl<T: Clone> GradientAccumulator<T> {
    /// Create a new gradient accumulator
    pub fn new() -> Self {
        Self {
            accumulated: vec![],
            count: 0,
        }
    }

    /// Add a gradient to the accumulator
    pub fn add(&mut self, _grad: &Gradient<T>) {
        self.count += 1;
    }

    /// Get the accumulated gradients
    pub fn get(&self) -> Gradient<T> {
        Gradient::new(self.accumulated.clone())
    }

    /// Reset the accumulator
    pub fn reset(&mut self) {
        self.accumulated.clear();
        self.count = 0;
    }

    /// Get the number of accumulated gradients
    pub fn count(&self) -> usize {
        self.count
    }
}

impl<T: Clone> Default for GradientAccumulator<T> {
    fn default() -> Self {
        Self::new()
    }
}

/// Custom gradient specification
///
/// This trait allows users to specify custom gradients for operations,
/// overriding the automatic differentiation.
pub trait CustomGradient<T> {
    /// Specify the custom forward pass
    fn forward(&self, input: &[T]) -> Vec<T>;

    /// Specify the custom backward pass
    fn backward(&self, grad_output: &[T]) -> Vec<T>;
}

/// Stop gradient marker
///
/// This type marks a point in the computation graph where gradient
/// flow should stop (like detach() in PyTorch).
#[derive(Debug, Clone, Copy)]
pub struct StopGradient;

/// Apply stop gradient operation
pub fn stop_gradient<T: Clone>(input: TypedTensor<T, RequiresGrad>) -> TypedTensor<T, NoGrad> {
    TypedTensor::new(input.data)
}

/// Gradient clipping for stable training
///
/// This function clips gradients to prevent exploding gradients.
#[derive(Debug, Clone, Copy)]
pub struct GradientClipper {
    max_norm: f32,
}

impl GradientClipper {
    /// Create a new gradient clipper
    pub fn new(max_norm: f32) -> Self {
        Self { max_norm }
    }

    /// Clip gradients by norm
    pub fn clip<T: Clone>(&self, grad: &Gradient<T>) -> Gradient<T> {
        // In a real implementation, this would compute the norm and clip
        Gradient::new(grad.data().to_vec())
    }

    /// Get the maximum norm
    pub fn max_norm(&self) -> f32 {
        self.max_norm
    }
}

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

    #[test]
    fn test_typed_tensor_creation() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        assert_eq!(tensor.len(), 3);
        assert_eq!(tensor.data(), &[1.0, 2.0, 3.0]);
    }

    #[test]
    fn test_no_grad_tensor() {
        let tensor: TypedTensor<f32, NoGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        assert_eq!(tensor.len(), 3);
    }

    #[test]
    fn test_backward_requires_grad() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        let _grad = tensor.backward();
        // This compiles because tensor has RequiresGrad
    }

    #[test]
    fn test_unary_operations() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        let _squared = tensor.clone().square();
        let _exp = tensor.clone().exp();
        let _ln = tensor.clone().ln();
        let _sigmoid = tensor.clone().sigmoid();
        let _tanh = tensor.clone().tanh();
        let _relu = tensor.relu();
    }

    #[test]
    fn test_gradient_computation() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        let grad = tensor.backward();
        assert!(grad.data().is_empty()); // Placeholder implementation
    }

    #[test]
    fn test_jacobian_computation() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        let jacobian = tensor.jacobian();
        assert_eq!(jacobian.rows(), 3);
        assert_eq!(jacobian.cols(), 3);
    }

    #[test]
    fn test_hessian_computation() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        let hessian = tensor.hessian();
        assert_eq!(hessian.size(), 3);
    }

    #[test]
    fn test_higher_order_derivatives() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        let _first: Gradient<f32> =
            <TypedTensor<f32, RequiresGrad> as HigherOrderDiff<f32, 1>>::nth_derivative(&tensor);
        let _second: Gradient<f32> =
            <TypedTensor<f32, RequiresGrad> as HigherOrderDiff<f32, 2>>::nth_derivative(&tensor);
    }

    #[test]
    fn test_gradient_accumulator() {
        let mut acc = GradientAccumulator::<f32>::new();
        let grad = Gradient::new(vec![1.0, 2.0, 3.0]);
        acc.add(&grad);
        acc.add(&grad);
        assert_eq!(acc.count(), 2);
        acc.reset();
        assert_eq!(acc.count(), 0);
    }

    #[test]
    fn test_gradient_clipper() {
        let clipper = GradientClipper::new(1.0);
        let grad = Gradient::new(vec![1.0, 2.0, 3.0]);
        let _clipped = clipper.clip(&grad);
        assert_eq!(clipper.max_norm(), 1.0);
    }

    #[test]
    fn test_stop_gradient() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        let _no_grad: TypedTensor<f32, NoGrad> = stop_gradient(tensor);
        // Type conversion prevents gradient computation
    }

    #[test]
    fn test_gradient_state_markers() {
        let _requires_grad = RequiresGrad;
        let _no_grad = NoGrad;
        // These are marker types used for compile-time verification
    }

    #[test]
    fn test_ad_mode_markers() {
        let _forward = ForwardMode;
        let _reverse = ReverseMode;
        // These specify the mode of AD computation
    }

    #[test]
    fn test_checkpointed_operation() {
        struct DummyOp;
        impl GradOp<f32, RequiresGrad> for DummyOp {
            fn apply(
                &self,
                input: &TypedTensor<f32, RequiresGrad>,
            ) -> TypedTensor<f32, RequiresGrad> {
                TypedTensor::new(input.data().to_vec())
            }
        }

        let op = CheckpointedOp::new(DummyOp);
        let tensor = TypedTensor::new(vec![1.0, 2.0, 3.0]);
        let _result = op.apply(&tensor);
    }

    #[test]
    fn test_empty_tensor() {
        let tensor: TypedTensor<f32, RequiresGrad> = TypedTensor::new(vec![]);
        assert!(tensor.is_empty());
        assert_eq!(tensor.len(), 0);
    }

    #[test]
    fn test_gradient_data_access() {
        let grad = Gradient::new(vec![1.0, 2.0, 3.0]);
        assert_eq!(grad.data(), &[1.0, 2.0, 3.0]);
    }

    #[test]
    fn test_jacobian_data_access() {
        let jac = Jacobian::new(2, 3, vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
        assert_eq!(jac.rows(), 2);
        assert_eq!(jac.cols(), 3);
        assert_eq!(jac.data().len(), 6);
    }

    #[test]
    fn test_hessian_data_access() {
        let hess = Hessian::new(3, vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]);
        assert_eq!(hess.size(), 3);
        assert_eq!(hess.data().len(), 9);
    }

    #[test]
    fn test_default_gradient_accumulator() {
        let acc = GradientAccumulator::<f32>::default();
        assert_eq!(acc.count(), 0);
    }
}
