//! MLX (Apple Machine Learning Framework) Compatibility Layer
//!
//! This module provides MLX-compatible functions and behaviors for users
//! transitioning from MLX to ToRSh. It includes MLX-style function transformations,
//! array operations, and Metal backend integration patterns.

use crate::AutogradTensor;
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use torsh_core::dtype::TensorElement;
use torsh_core::error::{Result, TorshError};

/// MLX-compatible array type wrapper
pub trait MLXArray<T: TensorElement> {
    /// Convert to MLX-style array representation
    fn as_mlx_array(&self) -> Result<MLXArrayData<T>>;

    /// Create from MLX-style array data
    fn from_mlx_array(data: MLXArrayData<T>) -> Result<Self>
    where
        Self: Sized;
}

/// MLX array data representation
#[derive(Debug, Clone)]
pub struct MLXArrayData<T: TensorElement> {
    /// Array data
    pub data: Vec<T>,
    /// Array shape
    pub shape: Vec<usize>,
    /// Array strides
    pub strides: Vec<usize>,
    /// Device location
    pub device: MLXDevice,
}

/// MLX device representation
#[derive(Debug, Clone, PartialEq)]
pub enum MLXDevice {
    /// CPU device
    Cpu,
    /// GPU device (Metal on Apple Silicon)
    Gpu(usize),
    /// Unified memory device
    Unified,
}

impl Default for MLXDevice {
    fn default() -> Self {
        // On Apple Silicon, prefer unified memory
        #[cfg(target_arch = "aarch64")]
        {
            Self::Unified
        }
        #[cfg(not(target_arch = "aarch64"))]
        {
            Self::Cpu
        }
    }
}

// Global default device storage
lazy_static::lazy_static! {
    static ref DEFAULT_DEVICE: Arc<Mutex<Option<MLXDevice>>> = Arc::new(Mutex::new(None));
}

/// MLX-compatible function transformations
pub mod mlx {
    use super::*;

    /// MLX-style function type
    pub trait MLXFunction<T: TensorElement>: Send + Sync {
        type Input;
        type Output;

        /// Apply the function
        fn apply(&self, input: Self::Input) -> Result<Self::Output>;

        /// Get function name for tracing
        fn name(&self) -> &str;
    }

    /// MLX-style grad transformation (equivalent to mlx.grad)
    pub fn grad<F, T, I, O>(f: F) -> GradFunction<F, T>
    where
        F: MLXFunction<T, Input = I, Output = O> + Clone,
        T: TensorElement + Clone,
        I: Clone,
        O: Clone,
    {
        GradFunction::new(f)
    }

    /// MLX-style value_and_grad transformation
    pub fn value_and_grad<F, T, I, O>(f: F) -> ValueAndGradFunction<F, T>
    where
        F: MLXFunction<T, Input = I, Output = O> + Clone,
        T: TensorElement + Clone,
        I: Clone,
        O: Clone,
    {
        ValueAndGradFunction::new(f)
    }

    /// MLX-style vectorize transformation (equivalent to mlx.vmap)
    pub fn vectorize<F, T>(f: F, in_axes: Vec<Option<usize>>) -> VectorizeFunction<F, T>
    where
        F: MLXFunction<T> + Clone,
        T: TensorElement,
    {
        VectorizeFunction::new(f, in_axes)
    }

    /// MLX-style compile transformation for optimization
    pub fn compile<F, T>(f: F) -> CompiledFunction<F, T>
    where
        F: MLXFunction<T> + Clone,
        T: TensorElement,
    {
        CompiledFunction::new(f)
    }

    /// Set the default device for MLX operations
    pub fn set_default_device(device: MLXDevice) {
        DEFAULT_DEVICE
            .lock()
            .expect("lock should not be poisoned")
            .replace(device);
    }

    /// Get the current default device
    pub fn default_device() -> MLXDevice {
        DEFAULT_DEVICE
            .lock()
            .expect("lock should not be poisoned")
            .clone()
            .unwrap_or_default()
    }

    /// Enable or disable gradient computation globally
    pub fn set_grad_enabled(enabled: bool) {
        crate::push_grad_enabled(enabled);
    }

    /// Check if gradients are globally enabled
    pub fn is_grad_enabled() -> bool {
        crate::is_grad_enabled()
    }
}

/// Gradient function wrapper
#[derive(Debug, Clone)]
pub struct GradFunction<F, T>
where
    F: mlx::MLXFunction<T>,
    T: TensorElement,
{
    inner: F,
    _phantom: std::marker::PhantomData<T>,
}

impl<F, T> GradFunction<F, T>
where
    F: mlx::MLXFunction<T>,
    T: TensorElement,
{
    pub fn new(f: F) -> Self {
        Self {
            inner: f,
            _phantom: std::marker::PhantomData,
        }
    }
}

impl<F, T> mlx::MLXFunction<T> for GradFunction<F, T>
where
    F: mlx::MLXFunction<T> + Clone,
    T: TensorElement + Clone,
{
    type Input = F::Input;
    type Output = Vec<Box<dyn AutogradTensor<T>>>;

    fn apply(&self, _input: Self::Input) -> Result<Self::Output> {
        // This is a simplified implementation
        // In a real implementation, we would compute actual gradients
        tracing::info!("Computing gradients for function: {}", self.inner.name());

        // Placeholder gradient computation
        // Real implementation would use the autograd system
        Ok(vec![])
    }

    fn name(&self) -> &str {
        "grad_function"
    }
}

/// Value and gradient function wrapper
#[derive(Debug, Clone)]
pub struct ValueAndGradFunction<F, T>
where
    F: mlx::MLXFunction<T>,
    T: TensorElement,
{
    inner: F,
    _phantom: std::marker::PhantomData<T>,
}

impl<F, T> ValueAndGradFunction<F, T>
where
    F: mlx::MLXFunction<T>,
    T: TensorElement,
{
    pub fn new(f: F) -> Self {
        Self {
            inner: f,
            _phantom: std::marker::PhantomData,
        }
    }
}

impl<F, T> mlx::MLXFunction<T> for ValueAndGradFunction<F, T>
where
    F: mlx::MLXFunction<T> + Clone,
    T: TensorElement + Clone,
{
    type Input = F::Input;
    type Output = (F::Output, Vec<Box<dyn AutogradTensor<T>>>);

    fn apply(&self, input: Self::Input) -> Result<Self::Output> {
        // Compute both value and gradients
        let value = self.inner.apply(input)?;

        // Placeholder gradient computation
        let gradients = vec![];

        Ok((value, gradients))
    }

    fn name(&self) -> &str {
        "value_and_grad_function"
    }
}

/// Vectorize function wrapper
#[derive(Debug, Clone)]
pub struct VectorizeFunction<F, T>
where
    F: mlx::MLXFunction<T>,
    T: TensorElement,
{
    inner: F,
    in_axes: Vec<Option<usize>>,
    _phantom: std::marker::PhantomData<T>,
}

impl<F, T> VectorizeFunction<F, T>
where
    F: mlx::MLXFunction<T>,
    T: TensorElement,
{
    pub fn new(f: F, in_axes: Vec<Option<usize>>) -> Self {
        Self {
            inner: f,
            in_axes,
            _phantom: std::marker::PhantomData,
        }
    }
}

impl<F, T> mlx::MLXFunction<T> for VectorizeFunction<F, T>
where
    F: mlx::MLXFunction<T> + Clone,
    T: TensorElement,
{
    type Input = F::Input;
    type Output = F::Output;

    fn apply(&self, input: Self::Input) -> Result<Self::Output> {
        // Simplified vectorization
        // Real implementation would vectorize over specified axes
        tracing::info!(
            "Vectorizing function: {} over axes: {:?}",
            self.inner.name(),
            self.in_axes
        );

        self.inner.apply(input)
    }

    fn name(&self) -> &str {
        "vectorize_function"
    }
}

/// Compiled function wrapper for optimization
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct CompiledFunction<F, T>
where
    F: mlx::MLXFunction<T>,
    T: TensorElement,
{
    inner: F,
    compiled: Arc<Mutex<bool>>,
    cache: Arc<Mutex<HashMap<String, Vec<u8>>>>,
    _phantom: std::marker::PhantomData<T>,
}

impl<F, T> CompiledFunction<F, T>
where
    F: mlx::MLXFunction<T>,
    T: TensorElement,
{
    pub fn new(f: F) -> Self {
        Self {
            inner: f,
            compiled: Arc::new(Mutex::new(false)),
            cache: Arc::new(Mutex::new(HashMap::new())),
            _phantom: std::marker::PhantomData,
        }
    }

    fn compile_if_needed(&self) -> Result<()> {
        let mut compiled = self.compiled.lock().expect("lock should not be poisoned");
        if !*compiled {
            tracing::info!("Compiling function: {}", self.inner.name());
            // Placeholder compilation
            // Real implementation would compile for Metal/GPU
            *compiled = true;
        }
        Ok(())
    }
}

impl<F, T> mlx::MLXFunction<T> for CompiledFunction<F, T>
where
    F: mlx::MLXFunction<T> + Clone,
    T: TensorElement,
{
    type Input = F::Input;
    type Output = F::Output;

    fn apply(&self, input: Self::Input) -> Result<Self::Output> {
        self.compile_if_needed()?;

        // Run compiled version
        self.inner.apply(input)
    }

    fn name(&self) -> &str {
        "compiled_function"
    }
}

/// MLX-style random number generation with autograd support
pub mod random {
    use super::*;

    /// Generate random normal values with autograd support
    pub fn normal<T: TensorElement>(
        _shape: &[usize],
        _mean: T,
        _std: T,
        device: Option<MLXDevice>,
    ) -> Result<Box<dyn AutogradTensor<T>>> {
        let _device = device.unwrap_or_default();

        // Placeholder implementation
        // Real implementation would generate random values on specified device
        Err(TorshError::AutogradError(
            "MLX random normal not yet implemented".to_string(),
        ))
    }

    /// Generate random uniform values with autograd support
    pub fn uniform<T: TensorElement>(
        _shape: &[usize],
        _low: T,
        _high: T,
        device: Option<MLXDevice>,
    ) -> Result<Box<dyn AutogradTensor<T>>> {
        let _device = device.unwrap_or_default();

        // Placeholder implementation
        Err(TorshError::AutogradError(
            "MLX random uniform not yet implemented".to_string(),
        ))
    }

    /// Set random seed for reproducibility
    pub fn seed(value: u64) {
        // Placeholder implementation
        tracing::info!("Setting MLX random seed to: {}", value);
    }
}

/// MLX-style neural network utilities
pub mod nn {
    use super::*;

    /// MLX-style linear layer
    #[allow(dead_code)]
    pub struct Linear<T: TensorElement> {
        weight: Box<dyn AutogradTensor<T>>,
        bias: Option<Box<dyn AutogradTensor<T>>>,
        in_features: usize,
        out_features: usize,
    }

    impl<T: TensorElement> Linear<T> {
        pub fn new(_in_features: usize, _out_features: usize, _bias: bool) -> Result<Self> {
            // Placeholder implementation
            Err(TorshError::AutogradError(
                "MLX Linear layer not yet implemented".to_string(),
            ))
        }
    }

    impl<T: TensorElement> mlx::MLXFunction<T> for Linear<T> {
        type Input = Box<dyn AutogradTensor<T>>;
        type Output = Box<dyn AutogradTensor<T>>;

        fn apply(&self, _input: Self::Input) -> Result<Self::Output> {
            // Placeholder linear transformation
            // Real implementation would perform matrix multiplication
            Err(TorshError::AutogradError(
                "MLX Linear forward not yet implemented".to_string(),
            ))
        }

        fn name(&self) -> &str {
            "mlx_linear"
        }
    }
}

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

    #[test]
    fn test_mlx_device_default() {
        let device = MLXDevice::default();

        #[cfg(target_arch = "aarch64")]
        assert_eq!(device, MLXDevice::Unified);

        #[cfg(not(target_arch = "aarch64"))]
        assert_eq!(device, MLXDevice::Cpu);
    }

    #[test]
    fn test_default_device_management() {
        mlx::set_default_device(MLXDevice::Gpu(0));
        assert_eq!(mlx::default_device(), MLXDevice::Gpu(0));

        mlx::set_default_device(MLXDevice::Cpu);
        assert_eq!(mlx::default_device(), MLXDevice::Cpu);
    }

    #[test]
    fn test_grad_function_creation() {
        // Create a simple test function
        #[derive(Clone)]
        struct TestFunction;

        impl mlx::MLXFunction<f32> for TestFunction {
            type Input = f32;
            type Output = f32;

            fn apply(&self, input: Self::Input) -> Result<Self::Output> {
                Ok(input * input)
            }

            fn name(&self) -> &str {
                "square"
            }
        }

        let f = TestFunction;
        let grad_f = mlx::grad(f);

        assert_eq!(grad_f.name(), "grad_function");
    }

    #[test]
    fn test_compiled_function_creation() {
        #[derive(Clone)]
        struct TestFunction;

        impl mlx::MLXFunction<f32> for TestFunction {
            type Input = f32;
            type Output = f32;

            fn apply(&self, input: Self::Input) -> Result<Self::Output> {
                Ok(input + 1.0)
            }

            fn name(&self) -> &str {
                "add_one"
            }
        }

        let f = TestFunction;
        let compiled_f = mlx::compile(f);

        assert_eq!(compiled_f.name(), "compiled_function");
    }

    #[test]
    fn test_random_seed() {
        // This should not panic
        random::seed(42);
    }
}
