//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

#[cfg(feature = "async_support")]
use super::functions::{
    ExoticHardware, HardwareCompiler, HardwareComputation, HardwareDiscovery, HardwareMemoryManager,
};
use crate::error::{Result, SklearsError};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::Duration;

#[cfg(feature = "async_support")]
pub type BoxFuture<'a, T> = std::pin::Pin<Box<dyn std::future::Future<Output = T> + Send + 'a>>;

/// Node identifier
pub type NodeId = u64;

/// Resource requirements for program execution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    pub memory_bytes: u64,
    pub compute_units: u32,
    pub execution_time_estimate_ms: f32,
}
/// FPGA-specific compiler (HLS + Place & Route)
pub struct FpgaCompiler {
    #[allow(dead_code)]
    pub(crate) supported_ops: Vec<OptimizationPass>,
}
impl FpgaCompiler {
    pub fn new() -> Self {
        Self {
            supported_ops: vec![
                OptimizationPass::LoopUnrolling,
                OptimizationPass::Custom("Pipeline_Optimization".to_string()),
                OptimizationPass::Custom("Resource_Sharing".to_string()),
                OptimizationPass::MemoryOptimization,
                OptimizationPass::Custom("Place_And_Route".to_string()),
            ],
        }
    }
}
/// Complex number for quantum amplitudes
#[derive(Debug, Clone, Copy, PartialEq)]
pub struct Complex64 {
    pub real: f64,
    pub imag: f64,
}
impl Complex64 {
    pub fn new(real: f64, imag: f64) -> Self {
        Self { real, imag }
    }
    pub fn magnitude_squared(&self) -> f64 {
        self.real * self.real + self.imag * self.imag
    }
}
/// Mock ML computation for testing
pub struct MockMLComputation {
    #[allow(dead_code)]
    pub(crate) graph: ComputationGraph,
}
impl MockMLComputation {
    pub fn new() -> Self {
        Self {
            graph: ComputationGraph {
                nodes: vec![
                    ComputationNode {
                        id: 1,
                        operation: Operation::MatMul,
                        attributes: HashMap::new(),
                        hardware_hints: vec![HardwareHint::PreferParallel],
                    },
                    ComputationNode {
                        id: 2,
                        operation: Operation::Activation(ActivationType::ReLU),
                        attributes: HashMap::new(),
                        hardware_hints: vec![HardwareHint::Fuse],
                    },
                ],
                edges: vec![ComputationEdge {
                    from: 1,
                    to: 2,
                    tensor_spec: TensorSpec {
                        shape: vec![1024, 512],
                        dtype: Precision::Float32,
                        layout: MemoryLayout::RowMajor,
                        sparsity: None,
                    },
                }],
                inputs: vec![1],
                outputs: vec![2],
                metadata: GraphMetadata {
                    name: "Simple MLP Layer".to_string(),
                    version: "1.0".to_string(),
                    framework_origin: Some("SkleaRS".to_string()),
                    optimization_level: 2,
                },
            },
        }
    }
}
/// FPGA vendors
#[derive(Debug, Clone, Copy)]
pub enum FpgaVendor {
    Xilinx,
    Intel,
}
/// Quantum computing device implementation
pub struct QuantumDevice {
    #[allow(dead_code)]
    pub(crate) id: HardwareId,
    pub(crate) capabilities: HardwareCapabilities,
    #[allow(dead_code)]
    pub(crate) is_initialized: bool,
    pub(crate) quantum_state: Option<QuantumState>,
}
impl QuantumDevice {
    /// Create new quantum computing device
    pub fn new(device_index: u32, backend: QuantumBackend) -> Self {
        let capabilities = match backend {
            QuantumBackend::Superconducting => HardwareCapabilities {
                compute_units: 4,
                memory_gb: 0.0,
                peak_performance_ops: 1e6,
                supported_precisions: vec![Precision::Float64],
                supports_sparsity: true,
                supports_quantization: false,
                supports_dynamic_shapes: true,
                custom_features: {
                    let mut features = HashMap::new();
                    features.insert(
                        "qubits".to_string(),
                        serde_json::Value::Number(serde_json::Number::from(4)),
                    );
                    features.insert(
                        "coherence_time_us".to_string(),
                        serde_json::Value::Number(
                            serde_json::Number::from_f64(100.0).expect("valid JSON operation"),
                        ),
                    );
                    features.insert(
                        "gate_error_rate".to_string(),
                        serde_json::Value::Number(
                            serde_json::Number::from_f64(0.001).expect("valid JSON operation"),
                        ),
                    );
                    features.insert(
                        "topology".to_string(),
                        serde_json::Value::String("heavy_hex".to_string()),
                    );
                    features
                },
            },
            QuantumBackend::IonTrap => HardwareCapabilities {
                compute_units: 4,
                memory_gb: 0.0,
                peak_performance_ops: 1e4,
                supported_precisions: vec![Precision::Float64],
                supports_sparsity: true,
                supports_quantization: false,
                supports_dynamic_shapes: true,
                custom_features: {
                    let mut features = HashMap::new();
                    features.insert(
                        "qubits".to_string(),
                        serde_json::Value::Number(serde_json::Number::from(4)),
                    );
                    features.insert(
                        "coherence_time_us".to_string(),
                        serde_json::Value::Number(
                            serde_json::Number::from_f64(10000.0).expect("valid JSON operation"),
                        ),
                    );
                    features.insert(
                        "gate_error_rate".to_string(),
                        serde_json::Value::Number(
                            serde_json::Number::from_f64(0.0001).expect("valid JSON operation"),
                        ),
                    );
                    features.insert(
                        "topology".to_string(),
                        serde_json::Value::String("all_to_all".to_string()),
                    );
                    features
                },
            },
            QuantumBackend::Photonic => HardwareCapabilities {
                compute_units: 216,
                memory_gb: 0.0,
                peak_performance_ops: 1e8,
                supported_precisions: vec![Precision::Float64],
                supports_sparsity: true,
                supports_quantization: false,
                supports_dynamic_shapes: true,
                custom_features: {
                    let mut features = HashMap::new();
                    features.insert(
                        "modes".to_string(),
                        serde_json::Value::Number(serde_json::Number::from(216)),
                    );
                    features.insert(
                        "room_temperature".to_string(),
                        serde_json::Value::Bool(true),
                    );
                    features.insert(
                        "measurement_based".to_string(),
                        serde_json::Value::Bool(true),
                    );
                    features
                },
            },
        };
        Self {
            id: HardwareId {
                device_type: HardwareType::Quantum,
                device_index,
                vendor: match backend {
                    QuantumBackend::Superconducting => "IBM".to_string(),
                    QuantumBackend::IonTrap => "IonQ".to_string(),
                    QuantumBackend::Photonic => "Xanadu".to_string(),
                },
                model: format!("{:?}", backend),
            },
            capabilities,
            is_initialized: false,
            quantum_state: None,
        }
    }
    /// Initialize quantum state
    pub async fn initialize_quantum_state(&mut self, num_qubits: usize) -> Result<()> {
        if num_qubits > self.capabilities.compute_units as usize {
            return Err(SklearsError::HardwareError(format!(
                "Requested {} qubits, but device only has {}",
                num_qubits, self.capabilities.compute_units
            )));
        }
        self.quantum_state = Some(QuantumState::new(num_qubits));
        Ok(())
    }
    /// Get quantum state (for debugging/simulation)
    pub fn quantum_state(&self) -> Option<&QuantumState> {
        self.quantum_state.as_ref()
    }
}
/// Sparsity patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SparsityPattern {
    Dense,
    CSR,
    CSC,
    COO,
    BlockSparse(Vec<i64>),
    Structured(String),
}
/// Operations supported by exotic hardware
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Operation {
    MatMul,
    Conv2D,
    Conv3D,
    DepthwiseConv,
    Activation(ActivationType),
    Pooling(PoolingType),
    TpuEinsum,
    TpuBatchMatMul,
    TpuSparseDenseMatMul,
    FpgaCustomKernel(String),
    FpgaPipelinedOp,
    FpgaStreamingOp,
    QuantumGate(QuantumGateType),
    QuantumMeasurement,
    QuantumVariational,
    Custom(String),
}
/// Validation report for hardware compatibility
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ValidationReport {
    pub is_compatible: bool,
    pub warnings: Vec<String>,
    pub errors: Vec<String>,
    pub optimizations: Vec<String>,
    pub estimated_performance: Option<PerformanceEstimate>,
}
/// Optimization passes
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationPass {
    DeadCodeElimination,
    ConstantFolding,
    OperatorFusion,
    MemoryOptimization,
    LoopUnrolling,
    Vectorization,
    Quantization,
    Sparsification,
    Custom(String),
}
/// Precision types supported by hardware
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum Precision {
    Float64,
    Float32,
    Float16,
    BFloat16,
    Int64,
    Int32,
    Int16,
    Int8,
    Binary,
    Custom(u8),
}
/// Pooling operation types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PoolingType {
    Max,
    Average,
    AdaptiveMax,
    AdaptiveAverage,
}
/// Identifier for exotic hardware devices
#[derive(Debug, Clone, Hash, PartialEq, Eq, Serialize, Deserialize)]
pub struct HardwareId {
    pub device_type: HardwareType,
    pub device_index: u32,
    pub vendor: String,
    pub model: String,
}
/// TPU versions
#[derive(Debug, Clone, Copy)]
pub enum TpuVersion {
    V2,
    V3,
    V4,
}
/// Quantum state representation (simplified)
#[derive(Debug, Clone)]
pub struct QuantumState {
    pub num_qubits: usize,
    pub amplitudes: Vec<Complex64>,
    pub measurement_results: Vec<bool>,
}
impl QuantumState {
    pub fn new(num_qubits: usize) -> Self {
        if num_qubits > 20 {
            panic!("Too many qubits: {} (max 20 for memory safety)", num_qubits);
        }
        let num_amplitudes = 1 << num_qubits;
        let mut amplitudes = vec![Complex64::new(0.0, 0.0); num_amplitudes];
        amplitudes[0] = Complex64::new(1.0, 0.0);
        Self {
            num_qubits,
            amplitudes,
            measurement_results: vec![false; num_qubits],
        }
    }
}
/// Quantum discovery agent
pub struct QuantumDiscovery;
impl QuantumDiscovery {
    pub fn new() -> Self {
        Self
    }
}
/// FPGA memory manager (Block RAM + External DDR)
pub struct FpgaMemoryManager {
    #[allow(dead_code)]
    pub(crate) block_ram_mb: u64,
    #[allow(dead_code)]
    pub(crate) external_ram_gb: u64,
    #[allow(dead_code)]
    pub(crate) allocated_memory: HashMap<u64, MemoryHandle>,
    #[allow(dead_code)]
    pub(crate) next_handle_id: u64,
}
impl FpgaMemoryManager {
    pub fn new() -> Self {
        Self {
            block_ram_mb: 38,
            external_ram_gb: 4,
            allocated_memory: HashMap::new(),
            next_handle_id: 1,
        }
    }
}
/// Tensor data representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorData {
    pub spec: TensorSpec,
    pub data: Vec<u8>,
}
/// Edge in computation graph
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationEdge {
    pub from: NodeId,
    pub to: NodeId,
    pub tensor_spec: TensorSpec,
}
/// Tensor specification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TensorSpec {
    pub shape: Vec<i64>,
    pub dtype: Precision,
    pub layout: MemoryLayout,
    pub sparsity: Option<SparsityPattern>,
}
/// Memory layout patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MemoryLayout {
    RowMajor,
    ColumnMajor,
    Blocked(Vec<i64>),
    Custom(String),
}
/// Node in computation graph
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationNode {
    pub id: NodeId,
    pub operation: Operation,
    pub attributes: HashMap<String, serde_json::Value>,
    pub hardware_hints: Vec<HardwareHint>,
}
/// Memory usage statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryStats {
    pub total_bytes: u64,
    pub used_bytes: u64,
    pub free_bytes: u64,
    pub fragmentation_ratio: f32,
    pub allocation_count: u64,
    pub peak_usage_bytes: u64,
}
/// Hardware status information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareStatus {
    pub is_online: bool,
    pub temperature_celsius: Option<f32>,
    pub power_usage_watts: Option<f32>,
    pub memory_usage_percent: f32,
    pub compute_utilization_percent: f32,
    pub error_count: u64,
    pub uptime_seconds: u64,
}
/// TPU-specific compiler
pub struct TpuCompiler {
    #[allow(dead_code)]
    pub(crate) supported_ops: Vec<OptimizationPass>,
}
impl TpuCompiler {
    pub fn new() -> Self {
        Self {
            supported_ops: vec![
                OptimizationPass::OperatorFusion,
                OptimizationPass::MemoryOptimization,
                OptimizationPass::Quantization,
                OptimizationPass::Custom("XLA_Optimization".to_string()),
            ],
        }
    }
}
/// Hardware-specific optimization hints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum HardwareHint {
    PreferParallel,
    PreferSequential,
    UseSparsity,
    UseQuantization(Precision),
    CustomMemoryLayout(String),
    Pipeline,
    Fuse,
}
/// Compiled program metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProgramMetadata {
    pub compilation_time_ms: f32,
    pub optimization_passes_applied: Vec<String>,
    pub estimated_performance: PerformanceEstimate,
    pub checksum: String,
}
/// TPU memory manager
pub struct TpuMemoryManager {
    #[allow(dead_code)]
    pub(crate) allocated_memory: HashMap<u64, MemoryHandle>,
    #[allow(dead_code)]
    pub(crate) next_handle_id: u64,
    #[allow(dead_code)]
    pub(crate) total_memory: u64,
    #[allow(dead_code)]
    pub(crate) used_memory: u64,
}
impl TpuMemoryManager {
    pub fn new() -> Self {
        Self {
            allocated_memory: HashMap::new(),
            next_handle_id: 1,
            total_memory: 8 * 1024 * 1024 * 1024,
            used_memory: 0,
        }
    }
}
/// FPGA (Field-Programmable Gate Array) device implementation
pub struct FpgaDevice {
    #[allow(dead_code)]
    pub(crate) id: HardwareId,
    #[allow(dead_code)]
    pub(crate) capabilities: HardwareCapabilities,
    #[allow(dead_code)]
    pub(crate) is_initialized: bool,
    pub(crate) configuration: Option<FpgaConfiguration>,
}
impl FpgaDevice {
    /// Create new FPGA device
    pub fn new(device_index: u32, vendor: FpgaVendor) -> Self {
        let capabilities = match vendor {
            FpgaVendor::Xilinx => HardwareCapabilities {
                compute_units: 256,
                memory_gb: 4.0,
                peak_performance_ops: 20e12,
                supported_precisions: vec![
                    Precision::Float32,
                    Precision::Float16,
                    Precision::Int32,
                    Precision::Int16,
                    Precision::Int8,
                    Precision::Custom(4),
                ],
                supports_sparsity: true,
                supports_quantization: true,
                supports_dynamic_shapes: false,
                custom_features: {
                    let mut features = HashMap::new();
                    features.insert("reconfigurable".to_string(), serde_json::Value::Bool(true));
                    features.insert(
                        "dsp_slices".to_string(),
                        serde_json::Value::Number(serde_json::Number::from(2880)),
                    );
                    features.insert(
                        "block_ram_mb".to_string(),
                        serde_json::Value::Number(serde_json::Number::from(38)),
                    );
                    features
                },
            },
            FpgaVendor::Intel => HardwareCapabilities {
                compute_units: 512,
                memory_gb: 8.0,
                peak_performance_ops: 35e12,
                supported_precisions: vec![
                    Precision::Float32,
                    Precision::Float16,
                    Precision::Int32,
                    Precision::Int16,
                    Precision::Int8,
                    Precision::Binary,
                ],
                supports_sparsity: true,
                supports_quantization: true,
                supports_dynamic_shapes: true,
                custom_features: {
                    let mut features = HashMap::new();
                    features.insert("reconfigurable".to_string(), serde_json::Value::Bool(true));
                    features.insert(
                        "alms".to_string(),
                        serde_json::Value::Number(serde_json::Number::from(933120)),
                    );
                    features.insert(
                        "m20k_blocks".to_string(),
                        serde_json::Value::Number(serde_json::Number::from(11721)),
                    );
                    features
                },
            },
        };
        Self {
            id: HardwareId {
                device_type: HardwareType::FPGA,
                device_index,
                vendor: format!("{:?}", vendor),
                model: match vendor {
                    FpgaVendor::Xilinx => "Alveo U250".to_string(),
                    FpgaVendor::Intel => "Stratix 10".to_string(),
                },
            },
            capabilities,
            is_initialized: false,
            configuration: None,
        }
    }
    /// Reconfigure FPGA with new bitstream
    pub async fn reconfigure(&mut self, bitstream: &[u8]) -> Result<()> {
        #[cfg(feature = "async_support")]
        tokio::time::sleep(Duration::from_secs(10)).await;
        #[cfg(not(feature = "async_support"))]
        std::thread::sleep(Duration::from_secs(10));
        self.configuration = Some(FpgaConfiguration {
            bitstream_checksum: format!("checksum_{}", bitstream.len()),
            logic_utilization: 75.0,
            memory_utilization: 60.0,
            dsp_utilization: 85.0,
            power_consumption_watts: 75.0,
        });
        Ok(())
    }
}
/// Hardware discovery and management system
#[cfg(feature = "async_support")]
pub struct ExoticHardwareManager {
    pub(crate) devices: HashMap<HardwareId, Box<dyn ExoticHardware>>,
    pub(crate) discovery_agents: Vec<Box<dyn HardwareDiscovery>>,
}
#[cfg(feature = "async_support")]
impl ExoticHardwareManager {
    /// Create new hardware manager
    pub fn new() -> Self {
        Self {
            devices: HashMap::new(),
            discovery_agents: vec![
                Box::new(TpuDiscovery::new()),
                Box::new(FpgaDiscovery::new()),
                Box::new(QuantumDiscovery::new()),
            ],
        }
    }
    /// Discover all available exotic hardware
    pub async fn discover_hardware(&mut self) -> Result<Vec<HardwareId>> {
        let mut discovered_devices = Vec::new();
        for agent in &self.discovery_agents {
            let devices = agent.discover().await?;
            for device in devices {
                let id = device.hardware_id().clone();
                discovered_devices.push(id.clone());
                self.devices.insert(id, device);
            }
        }
        Ok(discovered_devices)
    }
    /// Get device by ID
    pub fn get_device(&self, id: &HardwareId) -> Option<&dyn ExoticHardware> {
        self.devices.get(id).map(|d| d.as_ref())
    }
    /// Get device by ID (mutable) - TODO: Fix lifetime issue
    /// List all available devices
    pub fn list_devices(&self) -> Vec<&HardwareId> {
        self.devices.keys().collect()
    }
    /// Find best device for computation
    pub async fn find_best_device(
        &self,
        computation: &dyn HardwareComputation,
    ) -> Result<Option<&HardwareId>> {
        let mut best_device = None;
        let mut best_score = 0.0;
        for (id, device) in &self.devices {
            if device.is_ready().await? {
                let validation = computation.validate_for_hardware(device.as_ref())?;
                if validation.is_compatible {
                    if let Some(perf) = validation.estimated_performance {
                        let score = perf.confidence * (1.0 / perf.latency_ms);
                        if score > best_score {
                            best_score = score;
                            best_device = Some(id);
                        }
                    }
                }
            }
        }
        Ok(best_device)
    }
}
/// TPU discovery agent
pub struct TpuDiscovery;
impl TpuDiscovery {
    pub fn new() -> Self {
        Self
    }
}
/// FPGA discovery agent
pub struct FpgaDiscovery;
impl FpgaDiscovery {
    pub fn new() -> Self {
        Self
    }
}
/// Computation metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationMetadata {
    pub name: String,
    pub version: String,
    pub estimated_flops: u64,
    pub memory_requirement_bytes: u64,
    pub latency_requirement_ms: Option<f32>,
    pub throughput_requirement_ops_per_sec: Option<f32>,
}
/// Quantum computing backends
#[derive(Debug, Clone, Copy)]
pub enum QuantumBackend {
    Superconducting,
    IonTrap,
    Photonic,
}
/// Performance estimation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceEstimate {
    pub latency_ms: f32,
    pub throughput_ops_per_sec: f32,
    pub memory_usage_bytes: u64,
    pub power_usage_watts: f32,
    pub confidence: f32,
}
/// Activation function types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ActivationType {
    ReLU,
    Tanh,
    Sigmoid,
    Swish,
    GELU,
    Custom(String),
}
/// Graph metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GraphMetadata {
    pub name: String,
    pub version: String,
    pub framework_origin: Option<String>,
    pub optimization_level: u8,
}
/// Compilation options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompilationOptions {
    pub optimization_level: u8,
    pub target_precision: Precision,
    pub enable_fusion: bool,
    pub enable_quantization: bool,
    pub enable_sparsity: bool,
    pub custom_passes: Vec<String>,
}
/// Compiled program for hardware execution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompiledProgram {
    pub binary: Vec<u8>,
    pub metadata: ProgramMetadata,
    pub resource_requirements: ResourceRequirements,
}
/// Computation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationResult {
    pub outputs: Vec<TensorData>,
    pub execution_time_ms: f32,
    pub memory_used_bytes: u64,
    pub hardware_metrics: HardwareMetrics,
}
/// Memory handle for hardware memory
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub struct MemoryHandle {
    pub id: u64,
    pub size: u64,
    pub alignment: u32,
}
/// TPU (Tensor Processing Unit) device implementation
#[cfg(feature = "async_support")]
pub struct TpuDevice {
    pub(crate) id: HardwareId,
    pub(crate) capabilities: HardwareCapabilities,
    pub(crate) is_initialized: bool,
    pub(crate) compiler: Option<Box<dyn HardwareCompiler>>,
    pub(crate) memory_manager: Option<Box<dyn HardwareMemoryManager>>,
}
#[cfg(feature = "async_support")]
impl TpuDevice {
    /// Create new TPU device
    pub fn new(device_index: u32, version: TpuVersion) -> Self {
        let capabilities = match version {
            TpuVersion::V2 => HardwareCapabilities {
                compute_units: 8,
                memory_gb: 8.0,
                peak_performance_ops: 45e12,
                supported_precisions: vec![
                    Precision::Float32,
                    Precision::BFloat16,
                    Precision::Int8,
                ],
                supports_sparsity: false,
                supports_quantization: true,
                supports_dynamic_shapes: false,
                custom_features: {
                    let mut features = HashMap::new();
                    features.insert(
                        "matrix_unit_size".to_string(),
                        serde_json::Value::String("128x128".to_string()),
                    );
                    features.insert("systolic_array".to_string(), serde_json::Value::Bool(true));
                    features
                },
            },
            TpuVersion::V3 => HardwareCapabilities {
                compute_units: 16,
                memory_gb: 16.0,
                peak_performance_ops: 420e12,
                supported_precisions: vec![
                    Precision::Float32,
                    Precision::BFloat16,
                    Precision::Int8,
                ],
                supports_sparsity: true,
                supports_quantization: true,
                supports_dynamic_shapes: true,
                custom_features: {
                    let mut features = HashMap::new();
                    features.insert(
                        "matrix_unit_size".to_string(),
                        serde_json::Value::String("128x128".to_string()),
                    );
                    features.insert("systolic_array".to_string(), serde_json::Value::Bool(true));
                    features.insert(
                        "sparsity_support".to_string(),
                        serde_json::Value::Bool(true),
                    );
                    features
                },
            },
            TpuVersion::V4 => HardwareCapabilities {
                compute_units: 32,
                memory_gb: 32.0,
                peak_performance_ops: 1100e12,
                supported_precisions: vec![
                    Precision::Float32,
                    Precision::BFloat16,
                    Precision::Int8,
                    Precision::Int8,
                ],
                supports_sparsity: true,
                supports_quantization: true,
                supports_dynamic_shapes: true,
                custom_features: {
                    let mut features = HashMap::new();
                    features.insert(
                        "matrix_unit_size".to_string(),
                        serde_json::Value::String("256x256".to_string()),
                    );
                    features.insert("systolic_array".to_string(), serde_json::Value::Bool(true));
                    features.insert(
                        "sparsity_support".to_string(),
                        serde_json::Value::Bool(true),
                    );
                    features.insert("int4_support".to_string(), serde_json::Value::Bool(true));
                    features
                },
            },
        };
        Self {
            id: HardwareId {
                device_type: HardwareType::TPU,
                device_index,
                vendor: "Google".to_string(),
                model: format!("TPU-{:?}", version),
            },
            capabilities,
            is_initialized: false,
            compiler: None,
            memory_manager: None,
        }
    }
}
/// FPGA configuration state
#[derive(Debug, Clone)]
pub struct FpgaConfiguration {
    pub bitstream_checksum: String,
    pub logic_utilization: f32,
    pub memory_utilization: f32,
    pub dsp_utilization: f32,
    pub power_consumption_watts: f32,
}
/// Quantum circuit compiler
pub struct QuantumCompiler {
    #[allow(dead_code)]
    pub(crate) supported_ops: Vec<OptimizationPass>,
}
impl QuantumCompiler {
    pub fn new() -> Self {
        Self {
            supported_ops: vec![
                OptimizationPass::Custom("Gate_Synthesis".to_string()),
                OptimizationPass::Custom("Circuit_Optimization".to_string()),
                OptimizationPass::Custom("Error_Mitigation".to_string()),
                OptimizationPass::DeadCodeElimination,
            ],
        }
    }
}
/// Quantum gate types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QuantumGateType {
    Hadamard,
    PauliX,
    PauliY,
    PauliZ,
    CNOT,
    Toffoli,
    RY(f64),
    RZ(f64),
    Custom(String),
}
/// Computation graph representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationGraph {
    pub nodes: Vec<ComputationNode>,
    pub edges: Vec<ComputationEdge>,
    pub inputs: Vec<NodeId>,
    pub outputs: Vec<NodeId>,
    pub metadata: GraphMetadata,
}
/// Hardware capability flags
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareCapabilities {
    pub compute_units: u32,
    pub memory_gb: f64,
    pub peak_performance_ops: f64,
    pub supported_precisions: Vec<Precision>,
    pub supports_sparsity: bool,
    pub supports_quantization: bool,
    pub supports_dynamic_shapes: bool,
    pub custom_features: HashMap<String, serde_json::Value>,
}
/// Types of exotic hardware supported
#[derive(Debug, Clone, Copy, Hash, PartialEq, Eq, Serialize, Deserialize)]
pub enum HardwareType {
    TPU,
    FPGA,
    Quantum,
    CustomASIC,
    Neuromorphic,
    Optical,
}
/// Hardware-specific execution metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareMetrics {
    pub compute_utilization: f32,
    pub memory_bandwidth_gbps: f32,
    pub energy_consumed_joules: f32,
    pub hardware_specific: HashMap<String, serde_json::Value>,
}
