//! JAX-style transformations for automatic differentiation
//!
//! This module provides JAX-compatible transformation functions including:
//! - `jit`: Just-in-time compilation for optimization
//! - `grad`: Gradient transformation for functions
//! - `vmap`: Vectorized mapping over batch dimensions
//! - `pmap`: Parallel mapping across devices

use std::collections::HashMap;
use std::marker::PhantomData;
use std::sync::{Arc, RwLock};
use torsh_core::dtype::FloatElement;
use torsh_core::error::Result;
use torsh_core::shape::Shape;

/// A function that can be transformed with JAX-style transformations
pub trait TransformableFunction<T: FloatElement>: Send + Sync {
    type Input;
    type Output;

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

    /// Get the name of the function for caching and debugging
    fn name(&self) -> &str;
}

/// JIT compilation context for optimizing function execution
#[derive(Debug)]
pub struct JitContext<T: FloatElement> {
    /// Cache of compiled functions
    compiled_functions: Arc<RwLock<HashMap<String, CompiledFunction<T>>>>,
    /// JIT compilation options
    options: JitOptions,
    _phantom: PhantomData<T>,
}

/// JIT compilation options
#[derive(Debug, Clone)]
pub struct JitOptions {
    /// Enable dead code elimination
    pub dead_code_elimination: bool,
    /// Enable common subexpression elimination
    pub cse: bool,
    /// Enable loop unrolling
    pub loop_unrolling: bool,
    /// Enable constant folding
    pub constant_folding: bool,
    /// Maximum compilation cache size
    pub max_cache_size: usize,
}

impl Default for JitOptions {
    fn default() -> Self {
        Self {
            dead_code_elimination: true,
            cse: true,
            loop_unrolling: true,
            constant_folding: true,
            max_cache_size: 1000,
        }
    }
}

/// A compiled function representation
#[allow(dead_code)]
#[derive(Debug)]
struct CompiledFunction<T: FloatElement> {
    /// Function identifier
    id: String,
    /// Cached execution plan
    plan: ExecutionPlan<T>,
    /// Compilation timestamp
    compiled_at: std::time::Instant,
    /// Usage count for LRU eviction
    usage_count: usize,
}

/// Execution plan for optimized function execution
#[allow(dead_code)]
#[derive(Debug)]
struct ExecutionPlan<T: FloatElement> {
    /// Optimized operation sequence
    operations: Vec<Operation<T>>,
    /// Memory allocation plan
    memory_plan: MemoryPlan,
    /// Parallelization strategy
    parallel_strategy: ParallelStrategy,
}

/// Individual operation in the execution plan
#[allow(dead_code)]
#[derive(Debug)]
enum Operation<T: FloatElement> {
    /// Tensor addition
    Add {
        lhs: TensorRef,
        rhs: TensorRef,
        output: TensorRef,
    },
    /// Tensor multiplication
    Mul {
        lhs: TensorRef,
        rhs: TensorRef,
        output: TensorRef,
    },
    /// Matrix multiplication
    MatMul {
        lhs: TensorRef,
        rhs: TensorRef,
        output: TensorRef,
    },
    /// Element-wise activation
    Activation {
        input: TensorRef,
        output: TensorRef,
        activation: ActivationType,
    },
    /// Reduction operation
    Reduce {
        input: TensorRef,
        output: TensorRef,
        reduction: ReductionType,
        axes: Vec<usize>,
    },
    /// Reshape operation
    Reshape {
        input: TensorRef,
        output: TensorRef,
        shape: Shape,
    },
    /// Phantom variant to make the enum generic over T
    _Phantom(PhantomData<T>),
}

/// Reference to a tensor in the execution plan
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
struct TensorRef(usize);

/// Type of activation function
#[allow(dead_code)]
#[derive(Debug, Clone, Copy)]
enum ActivationType {
    ReLU,
    Sigmoid,
    Tanh,
    Softmax,
    LogSoftmax,
}

/// Type of reduction operation
#[allow(dead_code)]
#[derive(Debug, Clone, Copy)]
enum ReductionType {
    Sum,
    Mean,
    Max,
    Min,
}

/// Memory allocation plan for efficient execution
#[allow(dead_code)]
#[derive(Debug)]
struct MemoryPlan {
    /// Buffer sizes needed for execution
    buffer_sizes: Vec<usize>,
    /// Buffer reuse mapping
    reuse_mapping: HashMap<TensorRef, usize>,
    /// Total memory requirement
    total_memory: usize,
}

/// Parallelization strategy for operation execution
#[allow(dead_code)]
#[derive(Debug)]
enum ParallelStrategy {
    /// Sequential execution
    Sequential,
    /// Data parallelism
    DataParallel { num_threads: usize },
    /// Pipeline parallelism
    Pipeline { stages: Vec<Vec<usize>> },
}

impl<T: FloatElement> JitContext<T> {
    /// Create a new JIT context with default options
    pub fn new() -> Self {
        Self::with_options(JitOptions::default())
    }

    /// Create a new JIT context with custom options
    pub fn with_options(options: JitOptions) -> Self {
        Self {
            compiled_functions: Arc::new(RwLock::new(HashMap::new())),
            options,
            _phantom: PhantomData,
        }
    }

    /// Get compilation options
    pub fn options(&self) -> &JitOptions {
        &self.options
    }

    /// Clear the compilation cache
    pub fn clear_cache(&self) {
        if let Ok(mut cache) = self.compiled_functions.write() {
            cache.clear();
        }
    }

    /// Get cache statistics
    pub fn cache_stats(&self) -> CacheStats {
        if let Ok(cache) = self.compiled_functions.read() {
            CacheStats {
                size: cache.len(),
                total_usage: cache.values().map(|f| f.usage_count).sum(),
                max_size: self.options.max_cache_size,
            }
        } else {
            CacheStats {
                size: 0,
                total_usage: 0,
                max_size: self.options.max_cache_size,
            }
        }
    }
}

/// Cache statistics for monitoring
#[derive(Debug)]
pub struct CacheStats {
    pub size: usize,
    pub total_usage: usize,
    pub max_size: usize,
}

/// JIT-compiled function wrapper
pub struct JitFunction<T: FloatElement, F: TransformableFunction<T>> {
    /// The underlying function
    function: F,
    /// JIT context
    context: Arc<JitContext<T>>,
    /// Function signature hash for caching
    signature: String,
    _phantom: PhantomData<T>,
}

impl<T: FloatElement, F: TransformableFunction<T>> JitFunction<T, F> {
    /// Create a new JIT-compiled function
    pub fn new(function: F, context: Arc<JitContext<T>>) -> Self {
        let signature = format!(
            "{}_{}_{}",
            function.name(),
            std::any::type_name::<F>(),
            std::any::type_name::<T>()
        );

        Self {
            function,
            context,
            signature,
            _phantom: PhantomData,
        }
    }

    /// Apply the JIT-compiled function
    pub fn apply(&self, input: F::Input) -> Result<F::Output> {
        // Check if function is already compiled
        {
            let cache = self.context.compiled_functions.read();
            if let Some(compiled) = cache?.get(&self.signature) {
                // Execute compiled function
                return self.execute_compiled(compiled, input);
            }
        }

        // Compile the function
        let compiled = self.compile()?;

        // Cache the compiled function
        {
            let mut cache = self.context.compiled_functions.write()?;
            if cache.len() >= self.context.options.max_cache_size {
                self.evict_lru(&mut cache);
            }
            cache.insert(self.signature.clone(), compiled);
        }

        // Execute the compiled function
        let cache = self.context.compiled_functions.read()?;
        let compiled = cache
            .get(&self.signature)
            .expect("compiled function should exist after insertion");
        self.execute_compiled(compiled, input)
    }

    /// Compile the function into an optimized execution plan
    fn compile(&self) -> Result<CompiledFunction<T>> {
        // For now, create a simple execution plan
        // In a real implementation, this would analyze the function and create an optimized plan
        let plan = ExecutionPlan {
            operations: vec![],
            memory_plan: MemoryPlan {
                buffer_sizes: vec![],
                reuse_mapping: HashMap::new(),
                total_memory: 0,
            },
            parallel_strategy: ParallelStrategy::Sequential,
        };

        Ok(CompiledFunction {
            id: self.signature.clone(),
            plan,
            compiled_at: std::time::Instant::now(),
            usage_count: 0,
        })
    }

    /// Execute a compiled function
    fn execute_compiled(
        &self,
        _compiled: &CompiledFunction<T>,
        input: F::Input,
    ) -> Result<F::Output> {
        // For now, fall back to the original function
        // In a real implementation, this would execute the optimized plan
        self.function.apply(input)
    }

    /// Evict least recently used function from cache
    fn evict_lru(&self, cache: &mut HashMap<String, CompiledFunction<T>>) {
        if let Some(lru_key) = cache
            .iter()
            .min_by_key(|(_, f)| f.usage_count)
            .map(|(k, _)| k.clone())
        {
            cache.remove(&lru_key);
        }
    }
}

/// Gradient transformation function
#[allow(dead_code)]
pub struct GradFunction<T: FloatElement, F: TransformableFunction<T>> {
    /// The underlying function
    function: F,
    /// Which argument to differentiate with respect to
    argnums: Vec<usize>,
    _phantom: PhantomData<T>,
}

impl<T: FloatElement, F: TransformableFunction<T>> GradFunction<T, F> {
    /// Create a new gradient function
    pub fn new(function: F, argnums: Vec<usize>) -> Self {
        Self {
            function,
            argnums,
            _phantom: PhantomData,
        }
    }

    /// Apply the gradient function
    pub fn apply(&self, input: F::Input) -> Result<F::Output> {
        // For now, this is a placeholder
        // In a real implementation, this would compute gradients using the autograd system
        self.function.apply(input)
    }
}

/// Vectorized mapping function
#[allow(dead_code)]
pub struct VmapFunction<T: FloatElement, F: TransformableFunction<T>> {
    /// The underlying function
    function: F,
    /// Input axes to map over
    in_axes: Vec<Option<usize>>,
    /// Output axes to map over
    out_axes: Vec<Option<usize>>,
    _phantom: PhantomData<T>,
}

impl<T: FloatElement, F: TransformableFunction<T>> VmapFunction<T, F> {
    /// Create a new vectorized mapping function
    pub fn new(function: F, in_axes: Vec<Option<usize>>, out_axes: Vec<Option<usize>>) -> Self {
        Self {
            function,
            in_axes,
            out_axes,
            _phantom: PhantomData,
        }
    }

    /// Apply the vectorized mapping function
    pub fn apply(&self, input: F::Input) -> Result<F::Output> {
        // For now, this is a placeholder
        // In a real implementation, this would vectorize the function over the specified axes
        self.function.apply(input)
    }
}

/// Parallel mapping function
#[allow(dead_code)]
pub struct PmapFunction<T: FloatElement, F: TransformableFunction<T>> {
    /// The underlying function
    function: F,
    /// Devices to map over
    devices: Vec<String>,
    /// Axis to split input over
    axis: usize,
    _phantom: PhantomData<T>,
}

impl<T: FloatElement, F: TransformableFunction<T>> PmapFunction<T, F> {
    /// Create a new parallel mapping function
    pub fn new(function: F, devices: Vec<String>, axis: usize) -> Self {
        Self {
            function,
            devices,
            axis,
            _phantom: PhantomData,
        }
    }

    /// Apply the parallel mapping function
    pub fn apply(&self, input: F::Input) -> Result<F::Output> {
        // For now, this is a placeholder
        // In a real implementation, this would execute the function in parallel across devices
        self.function.apply(input)
    }
}

/// JIT compile a function for optimized execution
pub fn jit<T: FloatElement, F: TransformableFunction<T>>(
    function: F,
    context: Option<Arc<JitContext<T>>>,
) -> JitFunction<T, F> {
    let context = context.unwrap_or_else(|| Arc::new(JitContext::new()));
    JitFunction::new(function, context)
}

/// Create a gradient function that computes gradients of the input function
pub fn grad<T: FloatElement, F: TransformableFunction<T>>(
    function: F,
    argnums: Option<Vec<usize>>,
) -> GradFunction<T, F> {
    let argnums = argnums.unwrap_or_else(|| vec![0]);
    GradFunction::new(function, argnums)
}

/// Create a vectorized mapping function that maps over batch dimensions
pub fn vmap<T: FloatElement, F: TransformableFunction<T>>(
    function: F,
    in_axes: Option<Vec<Option<usize>>>,
    out_axes: Option<Vec<Option<usize>>>,
) -> VmapFunction<T, F> {
    let in_axes = in_axes.unwrap_or_else(|| vec![Some(0)]);
    let out_axes = out_axes.unwrap_or_else(|| vec![Some(0)]);
    VmapFunction::new(function, in_axes, out_axes)
}

/// Create a parallel mapping function that maps across devices
pub fn pmap<T: FloatElement, F: TransformableFunction<T>>(
    function: F,
    devices: Option<Vec<String>>,
    axis: Option<usize>,
) -> PmapFunction<T, F> {
    let devices = devices.unwrap_or_else(|| vec!["cpu".to_string()]);
    let axis = axis.unwrap_or(0);
    PmapFunction::new(function, devices, axis)
}

/// Utility function to chain multiple transformations
pub fn chain_transformations<T: FloatElement>() -> TransformationChain<T> {
    TransformationChain::new()
}

/// A chain of transformations that can be applied in sequence
pub struct TransformationChain<T: FloatElement> {
    _phantom: PhantomData<T>,
}

impl<T: FloatElement> TransformationChain<T> {
    /// Create a new transformation chain
    pub fn new() -> Self {
        Self {
            _phantom: PhantomData,
        }
    }

    /// Add a JIT transformation to the chain
    pub fn jit(self, _context: Option<Arc<JitContext<T>>>) -> Self {
        // Simplified implementation
        self
    }

    /// Add a gradient transformation to the chain
    pub fn grad(self, _argnums: Option<Vec<usize>>) -> Self {
        // Simplified implementation
        self
    }

    /// Add a vectorized mapping transformation to the chain
    pub fn vmap(
        self,
        _in_axes: Option<Vec<Option<usize>>>,
        _out_axes: Option<Vec<Option<usize>>>,
    ) -> Self {
        // Simplified implementation
        self
    }

    /// Add a parallel mapping transformation to the chain
    pub fn pmap(self, _devices: Option<Vec<String>>, _axis: Option<usize>) -> Self {
        // Simplified implementation
        self
    }

    /// Apply the transformation chain to a function
    pub fn apply<F>(self, _function: F) -> F
    where
        F: TransformableFunction<T>,
    {
        // Simplified implementation - just return the function unchanged
        _function
    }
}

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

    struct TestFunction;

    impl TransformableFunction<f32> for TestFunction {
        type Input = Vec<f32>;
        type Output = Vec<f32>;

        fn apply(&self, input: Self::Input) -> Result<Self::Output> {
            Ok(input.iter().map(|x| x * 2.0).collect())
        }

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

    #[test]
    fn test_jit_context_creation() {
        let context = JitContext::<f32>::new();
        assert_eq!(context.options().max_cache_size, 1000);
        assert!(context.options().dead_code_elimination);
    }

    #[test]
    fn test_jit_function_creation() {
        let function = TestFunction;
        let context = Arc::new(JitContext::new());
        let jit_fn = JitFunction::new(function, context);

        let input = vec![1.0, 2.0, 3.0];
        let result = jit_fn.apply(input).unwrap();
        assert_eq!(result, vec![2.0, 4.0, 6.0]);
    }

    #[test]
    fn test_grad_function_creation() {
        let function = TestFunction;
        let grad_fn = GradFunction::new(function, vec![0]);

        let input = vec![1.0, 2.0, 3.0];
        let result = grad_fn.apply(input).unwrap();
        assert_eq!(result, vec![2.0, 4.0, 6.0]);
    }

    #[test]
    fn test_vmap_function_creation() {
        let function = TestFunction;
        let vmap_fn = VmapFunction::new(function, vec![Some(0)], vec![Some(0)]);

        let input = vec![1.0, 2.0, 3.0];
        let result = vmap_fn.apply(input).unwrap();
        assert_eq!(result, vec![2.0, 4.0, 6.0]);
    }

    #[test]
    fn test_pmap_function_creation() {
        let function = TestFunction;
        let pmap_fn = PmapFunction::new(function, vec!["cpu".to_string()], 0);

        let input = vec![1.0, 2.0, 3.0];
        let result = pmap_fn.apply(input).unwrap();
        assert_eq!(result, vec![2.0, 4.0, 6.0]);
    }

    #[test]
    fn test_transformation_chain() {
        let function = TestFunction;
        let context = Arc::new(JitContext::new());

        let transformed = chain_transformations::<f32>()
            .jit(Some(context))
            .grad(Some(vec![0]))
            .vmap(Some(vec![Some(0)]), Some(vec![Some(0)]))
            .apply(function);

        let input = vec![1.0, 2.0, 3.0];
        let result = transformed.apply(input).unwrap();
        assert_eq!(result, vec![2.0, 4.0, 6.0]);
    }

    #[test]
    fn test_cache_stats() {
        let context = JitContext::<f32>::new();
        let stats = context.cache_stats();
        assert_eq!(stats.size, 0);
        assert_eq!(stats.total_usage, 0);
        assert_eq!(stats.max_size, 1000);
    }

    #[test]
    fn test_cache_clearing() {
        let context = JitContext::<f32>::new();
        context.clear_cache();
        let stats = context.cache_stats();
        assert_eq!(stats.size, 0);
    }
}
