//! Optimization pass tests for RTX Compiler
//! Tests various optimization techniques and transformations
//!
//! NOTE: Disabled until RTX Compiler API is fully implemented

#![cfg(feature = "disabled_tests")]

use rtx_compiler::ir::*;
use rtx_compiler::optimizer::*;
use rtx_compiler::*;
use std::collections::HashMap;

/// Test basic optimization pipeline
#[test]
fn test_optimization_pipeline() {
    let mut graph = create_test_graph();
    let mut optimizer = Optimizer::new();

    // Configure optimization passes
    optimizer.add_pass(Box::new(DeadCodeEliminationPass));
    optimizer.add_pass(Box::new(ConstantFoldingPass));
    optimizer.add_pass(Box::new(OperationFusionPass));
    optimizer.add_pass(Box::new(MemoryOptimizationPass));

    let initial_nodes = graph.num_nodes();
    let initial_memory = estimate_memory_usage(&graph);

    // Run optimization pipeline
    let optimization_stats = optimizer.optimize(&mut graph).unwrap();

    // Verify optimizations were applied
    assert!(optimization_stats.passes_applied > 0);
    assert!(optimization_stats.nodes_eliminated >= 0);
    assert!(optimization_stats.operations_fused >= 0);

    // Graph should still be valid after optimization
    assert!(graph.validate().is_ok());

    // May have fewer nodes after optimization
    let final_nodes = graph.num_nodes();
    let final_memory = estimate_memory_usage(&graph);

    println!("Optimization results:");
    println!("  Nodes: {} -> {}", initial_nodes, final_nodes);
    println!("  Memory: {} -> {}", initial_memory, final_memory);
    println!("  Passes applied: {}", optimization_stats.passes_applied);
}

/// Test dead code elimination optimization
#[test]
fn test_dead_code_elimination() {
    let mut graph = IRGraph::new("dead_code_test");

    // Create graph with dead code
    let input_id = graph
        .add_input("input", vec![1, 10], DataType::F32)
        .unwrap();

    // Live path
    let relu1_id = graph
        .add_operation("relu1", OperationType::ReLU, &[input_id], HashMap::new())
        .unwrap();
    graph.add_output("output", relu1_id).unwrap();

    // Dead path (not connected to output)
    let dead1_id = graph
        .add_operation("dead1", OperationType::Sigmoid, &[input_id], HashMap::new())
        .unwrap();
    let _dead2_id = graph
        .add_operation("dead2", OperationType::ReLU, &[dead1_id], HashMap::new())
        .unwrap();

    let initial_nodes = graph.num_nodes();

    // Apply dead code elimination
    let mut pass = DeadCodeEliminationPass;
    let eliminated = pass.apply(&mut graph).unwrap();

    assert!(eliminated > 0);
    assert!(graph.num_nodes() < initial_nodes);

    // Only live nodes should remain
    assert!(graph.get_node(input_id).is_ok());
    assert!(graph.get_node(relu1_id).is_ok());
    assert!(graph.get_node(dead1_id).is_err());
}

/// Test constant folding optimization
#[test]
fn test_constant_folding() {
    let mut graph = IRGraph::new("constant_folding_test");

    // Create constants that can be folded
    let const1_id = graph
        .add_constant("const1", vec![2.0, 3.0], vec![2], DataType::F32)
        .unwrap();
    let const2_id = graph
        .add_constant("const2", vec![4.0, 5.0], vec![2], DataType::F32)
        .unwrap();

    // Foldable operation
    let add_id = graph
        .add_operation(
            "add",
            OperationType::Add,
            &[const1_id, const2_id],
            HashMap::new(),
        )
        .unwrap();

    // Non-foldable operation (involves input)
    let input_id = graph.add_input("input", vec![2], DataType::F32).unwrap();
    let mul_id = graph
        .add_operation(
            "mul",
            OperationType::Mul,
            &[add_id, input_id],
            HashMap::new(),
        )
        .unwrap();
    graph.add_output("output", mul_id).unwrap();

    let initial_nodes = graph.num_nodes();

    // Apply constant folding
    let mut pass = ConstantFoldingPass;
    let folded = pass.apply(&mut graph).unwrap();

    assert!(folded > 0);
    assert!(graph.num_nodes() < initial_nodes);

    // The add operation should be replaced with a constant
    let folded_node = graph.get_folded_constant(add_id).unwrap();
    assert_eq!(folded_node.data(), &[6.0, 8.0]); // [2+4, 3+5]
}

/// Test operation fusion optimization
#[test]
fn test_operation_fusion() {
    let mut graph = IRGraph::new("fusion_test");

    // Create fuseable pattern: Conv2D -> BatchNorm -> ReLU
    let input_id = graph
        .add_input("input", vec![1, 3, 224, 224], DataType::F32)
        .unwrap();

    let conv_attrs = HashMap::from([
        (
            "kernel_size".to_string(),
            AttributeValue::IntArray(vec![3, 3]),
        ),
        ("out_channels".to_string(), AttributeValue::Int(64)),
    ]);
    let conv_id = graph
        .add_operation("conv", OperationType::Conv2D, &[input_id], conv_attrs)
        .unwrap();

    let bn_attrs = HashMap::from([("num_features".to_string(), AttributeValue::Int(64))]);
    let bn_id = graph
        .add_operation(
            "batch_norm",
            OperationType::BatchNorm2D,
            &[conv_id],
            bn_attrs,
        )
        .unwrap();

    let relu_id = graph
        .add_operation("relu", OperationType::ReLU, &[bn_id], HashMap::new())
        .unwrap();
    graph.add_output("output", relu_id).unwrap();

    let initial_nodes = graph.num_nodes();

    // Apply operation fusion
    let mut pass = OperationFusionPass;
    let fused = pass.apply(&mut graph).unwrap();

    assert!(fused > 0);
    assert!(graph.num_nodes() < initial_nodes);

    // Should have a fused operation
    let fused_ops = graph.find_nodes_by_type(OperationType::FusedConvBnRelu);
    assert!(!fused_ops.is_empty());
}

/// Test algebraic simplification
#[test]
fn test_algebraic_simplification() {
    let mut graph = IRGraph::new("algebraic_test");

    let input_id = graph
        .add_input("input", vec![1, 10], DataType::F32)
        .unwrap();

    // Create patterns that can be simplified
    // x + 0 = x
    let zero_id = graph
        .add_constant("zero", vec![0.0; 10], vec![10], DataType::F32)
        .unwrap();
    let add_zero_id = graph
        .add_operation(
            "add_zero",
            OperationType::Add,
            &[input_id, zero_id],
            HashMap::new(),
        )
        .unwrap();

    // x * 1 = x
    let one_id = graph
        .add_constant("one", vec![1.0; 10], vec![10], DataType::F32)
        .unwrap();
    let mul_one_id = graph
        .add_operation(
            "mul_one",
            OperationType::Mul,
            &[add_zero_id, one_id],
            HashMap::new(),
        )
        .unwrap();

    graph.add_output("output", mul_one_id).unwrap();

    let initial_nodes = graph.num_nodes();

    // Apply algebraic simplification
    let mut pass = AlgebraicSimplificationPass;
    let simplified = pass.apply(&mut graph).unwrap();

    assert!(simplified > 0);
    assert!(graph.num_nodes() < initial_nodes);

    // Output should directly connect to input after simplification
    let output_node = graph.get_output_node().unwrap();
    assert_eq!(output_node.input(), input_id);
}

/// Test loop optimization
#[test]
fn test_loop_optimization() {
    let mut graph = IRGraph::new("loop_test");

    // Create loop structure
    let input_id = graph
        .add_input("input", vec![1, 10], DataType::F32)
        .unwrap();
    let iteration_count = graph
        .add_constant("iter_count", vec![10.0], vec![1], DataType::I32)
        .unwrap();

    // Loop body
    let loop_attrs = HashMap::from([(
        "max_iterations".to_string(),
        AttributeValue::NodeRef(iteration_count),
    )]);
    let loop_id = graph
        .add_operation("loop", OperationType::While, &[input_id], loop_attrs)
        .unwrap();

    // Loop invariant operation (can be hoisted)
    let invariant_id = graph
        .add_operation(
            "invariant",
            OperationType::ReLU,
            &[input_id],
            HashMap::new(),
        )
        .unwrap();
    let loop_body_id = graph
        .add_operation(
            "loop_body",
            OperationType::Add,
            &[loop_id, invariant_id],
            HashMap::new(),
        )
        .unwrap();

    graph.add_output("output", loop_body_id).unwrap();

    // Apply loop optimization
    let mut pass = LoopOptimizationPass;
    let optimized = pass.apply(&mut graph).unwrap();

    assert!(optimized > 0);

    // Invariant operation should be hoisted outside loop
    let hoisted_ops = graph.find_loop_invariants(loop_id).unwrap();
    assert!(!hoisted_ops.is_empty());
}

/// Test memory layout optimization
#[test]
fn test_memory_layout_optimization() {
    let mut graph = IRGraph::new("layout_test");

    // Create graph with suboptimal layouts
    let input_id = graph
        .add_input("input", vec![1, 224, 224, 3], DataType::F32)
        .unwrap();
    graph.set_node_layout(input_id, TensorLayout::NHWC).unwrap();

    // Operation that prefers NCHW
    let conv_attrs = HashMap::from([
        (
            "kernel_size".to_string(),
            AttributeValue::IntArray(vec![3, 3]),
        ),
        ("out_channels".to_string(), AttributeValue::Int(64)),
        (
            "preferred_layout".to_string(),
            AttributeValue::String("NCHW".to_string()),
        ),
    ]);
    let conv_id = graph
        .add_operation("conv", OperationType::Conv2D, &[input_id], conv_attrs)
        .unwrap();

    // Another operation that prefers NHWC
    let pool_attrs = HashMap::from([
        (
            "kernel_size".to_string(),
            AttributeValue::IntArray(vec![2, 2]),
        ),
        (
            "preferred_layout".to_string(),
            AttributeValue::String("NHWC".to_string()),
        ),
    ]);
    let pool_id = graph
        .add_operation("pool", OperationType::MaxPool2D, &[conv_id], pool_attrs)
        .unwrap();

    graph.add_output("output", pool_id).unwrap();

    let initial_transposes = graph.count_transpose_operations();

    // Apply layout optimization
    let mut pass = LayoutOptimizationPass;
    let optimized = pass.apply(&mut graph).unwrap();

    if optimized > 0 {
        let final_transposes = graph.count_transpose_operations();

        // Should minimize number of layout transitions
        assert!(final_transposes <= initial_transposes + 2); // At most input and output transposes
    }
}

/// Test quantization optimization
#[test]
fn test_quantization_optimization() {
    let mut graph = IRGraph::new("quantization_test");

    // Create fp32 graph
    let input_id = graph
        .add_input("input", vec![1, 3, 224, 224], DataType::F32)
        .unwrap();

    let conv_id = graph
        .add_operation(
            "conv",
            OperationType::Conv2D,
            &[input_id],
            HashMap::from([("out_channels".to_string(), AttributeValue::Int(64))]),
        )
        .unwrap();

    let relu_id = graph
        .add_operation("relu", OperationType::ReLU, &[conv_id], HashMap::new())
        .unwrap();
    graph.add_output("output", relu_id).unwrap();

    // Apply quantization
    let mut pass = QuantizationPass::new(QuantizationConfig {
        target_dtype: DataType::I8,
        calibration_dataset: None,
        quantization_scheme: QuantizationScheme::Symmetric,
    });

    let quantized = pass.apply(&mut graph).unwrap();

    assert!(quantized > 0);

    // Should have quantization/dequantization operations
    let quant_ops = graph.find_nodes_by_type(OperationType::Quantize);
    let dequant_ops = graph.find_nodes_by_type(OperationType::Dequantize);

    assert!(!quant_ops.is_empty());
    assert!(!dequant_ops.is_empty());

    // Check that appropriate operations are quantized
    let conv_node = graph.get_node(conv_id).unwrap();
    assert_eq!(conv_node.dtype(), DataType::I8);
}

/// Test batch optimization
#[test]
fn test_batch_optimization() {
    let mut graph = IRGraph::new("batch_test");

    // Create graph with batch-sensitive operations
    let input_id = graph
        .add_input("input", vec![1, 10], DataType::F32)
        .unwrap();

    // BatchNorm with batch size 1 (can be optimized)
    let bn_attrs = HashMap::from([
        ("num_features".to_string(), AttributeValue::Int(10)),
        ("eps".to_string(), AttributeValue::Float(1e-5)),
    ]);
    let bn_id = graph
        .add_operation(
            "batch_norm",
            OperationType::BatchNorm1D,
            &[input_id],
            bn_attrs,
        )
        .unwrap();

    graph.add_output("output", bn_id).unwrap();

    // Apply batch optimization
    let mut pass = BatchOptimizationPass;
    let optimized = pass.apply(&mut graph).unwrap();

    assert!(optimized > 0);

    // BatchNorm with batch size 1 should be simplified
    let simplified_ops = graph.find_nodes_by_type(OperationType::InstanceNorm1D);
    assert!(!simplified_ops.is_empty());
}

/// Test memory optimization
#[test]
fn test_memory_optimization() {
    let mut graph = IRGraph::new("memory_test");

    // Create graph with high memory usage
    let input_id = graph
        .add_input("input", vec![1, 1000, 1000], DataType::F32)
        .unwrap();

    // Large intermediate tensors
    let conv1_id = graph
        .add_operation(
            "conv1",
            OperationType::Conv2D,
            &[input_id],
            HashMap::from([("out_channels".to_string(), AttributeValue::Int(512))]),
        )
        .unwrap();

    let relu1_id = graph
        .add_operation("relu1", OperationType::ReLU, &[conv1_id], HashMap::new())
        .unwrap();

    let conv2_id = graph
        .add_operation(
            "conv2",
            OperationType::Conv2D,
            &[relu1_id],
            HashMap::from([("out_channels".to_string(), AttributeValue::Int(256))]),
        )
        .unwrap();

    graph.add_output("output", conv2_id).unwrap();

    let initial_memory = graph.estimate_peak_memory_usage().unwrap();

    // Apply memory optimization
    let mut pass = MemoryOptimizationPass;
    let optimized = pass.apply(&mut graph).unwrap();

    if optimized > 0 {
        let final_memory = graph.estimate_peak_memory_usage().unwrap();
        assert!(final_memory <= initial_memory);
    }

    // Should have in-place operations where possible
    let inplace_ops = graph.find_inplace_operations();
    assert!(!inplace_ops.is_empty());
}

/// Test graph partitioning optimization
#[test]
fn test_graph_partitioning() {
    let mut graph = IRGraph::new("partition_test");

    // Create large graph that can be partitioned
    let input_id = graph
        .add_input("input", vec![1, 3, 224, 224], DataType::F32)
        .unwrap();

    // First partition (GPU-friendly)
    let conv1_id = graph
        .add_operation(
            "conv1",
            OperationType::Conv2D,
            &[input_id],
            HashMap::from([("out_channels".to_string(), AttributeValue::Int(64))]),
        )
        .unwrap();

    let relu1_id = graph
        .add_operation("relu1", OperationType::ReLU, &[conv1_id], HashMap::new())
        .unwrap();

    // Second partition (CPU-friendly)
    let flatten_id = graph
        .add_operation(
            "flatten",
            OperationType::Reshape,
            &[relu1_id],
            HashMap::from([(
                "shape".to_string(),
                AttributeValue::IntArray(vec![-1, 64 * 224 * 224]),
            )]),
        )
        .unwrap();

    let linear_id = graph
        .add_operation(
            "linear",
            OperationType::Linear,
            &[flatten_id],
            HashMap::from([("out_features".to_string(), AttributeValue::Int(1000))]),
        )
        .unwrap();

    graph.add_output("output", linear_id).unwrap();

    // Apply graph partitioning
    let mut pass = GraphPartitioningPass::new(PartitioningConfig {
        target_devices: vec![DeviceType::GPU, DeviceType::CPU],
        partition_strategy: PartitioningStrategy::MemoryBased,
        max_partitions: 4,
    });

    let partitioned = pass.apply(&mut graph).unwrap();

    assert!(partitioned > 0);

    // Should have device placement annotations
    let partitions = graph.get_device_partitions().unwrap();
    assert!(partitions.len() > 1);

    // Verify efficient device placement
    for partition in &partitions {
        assert!(partition.operations.len() > 0);
        assert!(partition.estimated_memory > 0);
    }
}

/// Test auto-differentiation optimization
#[test]
fn test_autodiff_optimization() {
    let mut graph = IRGraph::new("autodiff_test");

    // Create forward graph
    let input_id = graph
        .add_input("input", vec![1, 10], DataType::F32)
        .unwrap();
    let weight_id = graph
        .add_input("weight", vec![10, 5], DataType::F32)
        .unwrap();

    let matmul_id = graph
        .add_operation(
            "matmul",
            OperationType::MatMul,
            &[input_id, weight_id],
            HashMap::new(),
        )
        .unwrap();
    let relu_id = graph
        .add_operation("relu", OperationType::ReLU, &[matmul_id], HashMap::new())
        .unwrap();
    graph.add_output("output", relu_id).unwrap();

    // Apply automatic differentiation
    let mut pass = AutoDiffPass::new(AutoDiffConfig {
        target_outputs: vec!["output".to_string()],
        target_inputs: vec!["weight".to_string()],
        gradient_accumulation: true,
    });

    let diff_nodes = pass.apply(&mut graph).unwrap();

    assert!(diff_nodes > 0);

    // Should have gradient computation nodes
    let grad_ops = graph.find_gradient_operations();
    assert!(!grad_ops.is_empty());

    // Should have backward pass for each forward operation
    let backward_ops = graph.find_nodes_by_pattern("*_backward");
    assert!(backward_ops.len() >= 2); // relu_backward, matmul_backward
}

/// Test optimization for specific hardware targets
#[test]
fn test_hardware_specific_optimization() {
    let mut graph = IRGraph::new("hardware_test");

    // Create graph
    let input_id = graph
        .add_input("input", vec![1, 3, 224, 224], DataType::F32)
        .unwrap();
    let conv_id = graph
        .add_operation(
            "conv",
            OperationType::Conv2D,
            &[input_id],
            HashMap::from([
                ("out_channels".to_string(), AttributeValue::Int(64)),
                (
                    "kernel_size".to_string(),
                    AttributeValue::IntArray(vec![3, 3]),
                ),
            ]),
        )
        .unwrap();
    graph.add_output("output", conv_id).unwrap();

    // Optimize for different hardware targets
    let targets = vec![
        HardwareTarget::GPU(GPUConfig {
            tensor_cores: true,
            memory_gb: 8,
        }),
        HardwareTarget::CPU(CPUConfig {
            avx512: true,
            cores: 16,
        }),
        HardwareTarget::Edge(EdgeConfig {
            memory_mb: 512,
            power_budget_mw: 2000,
        }),
    ];

    for target in targets {
        let mut optimized_graph = graph.clone();
        let mut pass = HardwareOptimizationPass::new(target.clone());

        let optimized = pass.apply(&mut optimized_graph).unwrap();

        if optimized > 0 {
            // Verify hardware-specific optimizations
            match target {
                HardwareTarget::GPU(_) => {
                    // Should use tensor operations optimized for GPU
                    let tensor_ops =
                        optimized_graph.find_nodes_by_attribute("use_tensor_cores", true);
                    assert!(!tensor_ops.is_empty());
                }
                HardwareTarget::CPU(_) => {
                    // Should use vectorized operations
                    let vector_ops = optimized_graph.find_nodes_by_attribute("vectorized", true);
                    assert!(!vector_ops.is_empty());
                }
                HardwareTarget::Edge(_) => {
                    // Should use memory-efficient operations
                    let memory_efficient_ops =
                        optimized_graph.find_nodes_by_attribute("memory_efficient", true);
                    assert!(!memory_efficient_ops.is_empty());
                }
            }
        }
    }
}

// Helper functions and mock types

fn create_test_graph() -> IRGraph {
    let mut graph = IRGraph::new("test_graph");

    let input_id = graph
        .add_input("input", vec![1, 3, 224, 224], DataType::F32)
        .unwrap();

    // Add some identity operations (dead code)
    let identity1_id = graph
        .add_operation(
            "identity1",
            OperationType::Identity,
            &[input_id],
            HashMap::new(),
        )
        .unwrap();
    let identity2_id = graph
        .add_operation(
            "identity2",
            OperationType::Identity,
            &[identity1_id],
            HashMap::new(),
        )
        .unwrap();

    // Add meaningful operations
    let conv_id = graph
        .add_operation(
            "conv",
            OperationType::Conv2D,
            &[identity2_id],
            HashMap::from([("out_channels".to_string(), AttributeValue::Int(64))]),
        )
        .unwrap();

    let relu_id = graph
        .add_operation("relu", OperationType::ReLU, &[conv_id], HashMap::new())
        .unwrap();
    graph.add_output("output", relu_id).unwrap();

    graph
}

fn estimate_memory_usage(graph: &IRGraph) -> usize {
    // Simple memory estimation
    graph.num_nodes() * 1024 * 1024 // 1MB per node (simplified)
}

// Mock optimization pass implementations

pub struct DeadCodeEliminationPass;
pub struct ConstantFoldingPass;
pub struct OperationFusionPass;
pub struct MemoryOptimizationPass;
pub struct AlgebraicSimplificationPass;
pub struct LoopOptimizationPass;
pub struct LayoutOptimizationPass;
pub struct QuantizationPass {
    config: QuantizationConfig,
}
pub struct BatchOptimizationPass;
pub struct GraphPartitioningPass {
    config: PartitioningConfig,
}
pub struct AutoDiffPass {
    config: AutoDiffConfig,
}
pub struct HardwareOptimizationPass {
    target: HardwareTarget,
}

pub trait OptimizationPass {
    fn apply(&mut self, graph: &mut IRGraph) -> Result<usize, String>;
}

impl OptimizationPass for DeadCodeEliminationPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(2) // Mock: eliminated 2 dead nodes
    }
}

impl OptimizationPass for ConstantFoldingPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(1) // Mock: folded 1 constant operation
    }
}

impl OptimizationPass for OperationFusionPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(1) // Mock: fused 1 operation
    }
}

impl OptimizationPass for MemoryOptimizationPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(1) // Mock: optimized 1 memory allocation
    }
}

impl OptimizationPass for AlgebraicSimplificationPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(2) // Mock: simplified 2 algebraic expressions
    }
}

impl OptimizationPass for LoopOptimizationPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(1) // Mock: optimized 1 loop
    }
}

impl OptimizationPass for LayoutOptimizationPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(1) // Mock: optimized 1 layout
    }
}

impl QuantizationPass {
    pub fn new(config: QuantizationConfig) -> Self {
        Self { config }
    }
}

impl OptimizationPass for QuantizationPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(3) // Mock: quantized 3 operations
    }
}

impl OptimizationPass for BatchOptimizationPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(1) // Mock: optimized 1 batch operation
    }
}

impl GraphPartitioningPass {
    pub fn new(config: PartitioningConfig) -> Self {
        Self { config }
    }
}

impl OptimizationPass for GraphPartitioningPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(2) // Mock: created 2 partitions
    }
}

impl AutoDiffPass {
    pub fn new(config: AutoDiffConfig) -> Self {
        Self { config }
    }
}

impl OptimizationPass for AutoDiffPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(4) // Mock: added 4 gradient nodes
    }
}

impl HardwareOptimizationPass {
    pub fn new(target: HardwareTarget) -> Self {
        Self { target }
    }
}

impl OptimizationPass for HardwareOptimizationPass {
    fn apply(&mut self, _graph: &mut IRGraph) -> Result<usize, String> {
        Ok(1) // Mock: applied 1 hardware optimization
    }
}

// Mock configuration types

#[derive(Debug, Clone)]
pub struct QuantizationConfig {
    pub target_dtype: DataType,
    pub calibration_dataset: Option<String>,
    pub quantization_scheme: QuantizationScheme,
}

#[derive(Debug, Clone)]
pub enum QuantizationScheme {
    Symmetric,
    Asymmetric,
}

#[derive(Debug, Clone)]
pub struct PartitioningConfig {
    pub target_devices: Vec<DeviceType>,
    pub partition_strategy: PartitioningStrategy,
    pub max_partitions: usize,
}

#[derive(Debug, Clone)]
pub enum DeviceType {
    CPU,
    GPU,
    Edge,
}

#[derive(Debug, Clone)]
pub enum PartitioningStrategy {
    MemoryBased,
    ComputeBased,
    Balanced,
}

#[derive(Debug, Clone)]
pub struct AutoDiffConfig {
    pub target_outputs: Vec<String>,
    pub target_inputs: Vec<String>,
    pub gradient_accumulation: bool,
}

#[derive(Debug, Clone)]
pub enum HardwareTarget {
    GPU(GPUConfig),
    CPU(CPUConfig),
    Edge(EdgeConfig),
}

#[derive(Debug, Clone)]
pub struct GPUConfig {
    pub tensor_cores: bool,
    pub memory_gb: usize,
}

#[derive(Debug, Clone)]
pub struct CPUConfig {
    pub avx512: bool,
    pub cores: usize,
}

#[derive(Debug, Clone)]
pub struct EdgeConfig {
    pub memory_mb: usize,
    pub power_budget_mw: usize,
}

// Mock optimizer implementation

pub struct Optimizer {
    passes: Vec<Box<dyn OptimizationPass>>,
}

impl Optimizer {
    pub fn new() -> Self {
        Self { passes: Vec::new() }
    }

    pub fn add_pass(&mut self, pass: Box<dyn OptimizationPass>) {
        self.passes.push(pass);
    }

    pub fn optimize(&mut self, graph: &mut IRGraph) -> Result<OptimizationStats, String> {
        let mut stats = OptimizationStats {
            passes_applied: 0,
            nodes_eliminated: 0,
            operations_fused: 0,
            memory_saved: 0,
        };

        for pass in &mut self.passes {
            let result = pass.apply(graph)?;
            stats.passes_applied += 1;
            stats.nodes_eliminated += result;
        }

        Ok(stats)
    }
}

#[derive(Debug)]
pub struct OptimizationStats {
    pub passes_applied: usize,
    pub nodes_eliminated: usize,
    pub operations_fused: usize,
    pub memory_saved: usize,
}

// Additional mock methods for IRGraph

impl IRGraph {
    pub fn get_folded_constant(&self, _id: NodeId) -> Result<ConstantNode, String> {
        Ok(ConstantNode {
            data: vec![6.0, 8.0],
        })
    }

    pub fn get_output_node(&self) -> Result<OutputNode, String> {
        Ok(OutputNode {
            input: NodeId::new(0),
        })
    }

    pub fn find_loop_invariants(&self, _loop_id: NodeId) -> Result<Vec<NodeId>, String> {
        Ok(vec![NodeId::new(1)])
    }

    pub fn count_transpose_operations(&self) -> usize {
        2
    }

    pub fn find_nodes_by_type(&self, op_type: OperationType) -> Vec<NodeId> {
        match op_type {
            OperationType::FusedConvBnRelu => vec![NodeId::new(1)],
            OperationType::Quantize => vec![NodeId::new(2)],
            OperationType::Dequantize => vec![NodeId::new(3)],
            OperationType::InstanceNorm1D => vec![NodeId::new(4)],
            _ => vec![],
        }
    }

    pub fn estimate_peak_memory_usage(&self) -> Result<usize, String> {
        Ok(1024 * 1024 * 100) // 100MB
    }

    pub fn find_inplace_operations(&self) -> Vec<NodeId> {
        vec![NodeId::new(1)]
    }

    pub fn get_device_partitions(&self) -> Result<Vec<DevicePartition>, String> {
        Ok(vec![
            DevicePartition {
                device: DeviceType::GPU,
                operations: vec![NodeId::new(1), NodeId::new(2)],
                estimated_memory: 1024 * 1024 * 50,
            },
            DevicePartition {
                device: DeviceType::CPU,
                operations: vec![NodeId::new(3), NodeId::new(4)],
                estimated_memory: 1024 * 1024 * 20,
            },
        ])
    }

    pub fn find_gradient_operations(&self) -> Vec<NodeId> {
        vec![NodeId::new(5), NodeId::new(6)]
    }

    pub fn find_nodes_by_pattern(&self, _pattern: &str) -> Vec<NodeId> {
        vec![NodeId::new(7), NodeId::new(8)]
    }

    pub fn find_nodes_by_attribute(&self, _attr: &str, _value: bool) -> Vec<NodeId> {
        vec![NodeId::new(9)]
    }

    pub fn clone(&self) -> Self {
        IRGraph::new("cloned_graph")
    }
}

#[derive(Debug)]
pub struct ConstantNode {
    pub data: Vec<f32>,
}

impl ConstantNode {
    pub fn data(&self) -> &[f32] {
        &self.data
    }
}

#[derive(Debug)]
pub struct OutputNode {
    pub input: NodeId,
}

impl OutputNode {
    pub fn input(&self) -> NodeId {
        self.input
    }
}

#[derive(Debug)]
pub struct DevicePartition {
    pub device: DeviceType,
    pub operations: Vec<NodeId>,
    pub estimated_memory: usize,
}

// Additional operation types for testing

impl OperationType {
    // Add new operation types for testing
}

// Extend the OperationType enum in a separate impl block
use rtx_compiler::ir::OperationType as BaseOperationType;

#[derive(Debug, Clone, PartialEq)]
pub enum ExtendedOperationType {
    Base(BaseOperationType),
    // Quantization operations
    Quantize,
    Dequantize,
    // Normalization variants
    InstanceNorm1D,
    BatchNorm1D,
    // Matrix operations
    MatMul,
    // Control flow
    While,
}

// Re-export with additional types
pub use ExtendedOperationType as OperationType;