torsh-fx 0.1.2

//! Emerging Hardware Architecture Support for ToRSh FX
//!
//! This module provides comprehensive support for emerging and next-generation
//! hardware architectures including neuromorphic processors, photonic computing,
//! DNA computing, optical processors, and advanced accelerators.

use crate::{FxGraph, Node, Result};
use serde::{Deserialize, Serialize};
use std::sync::{Arc, Mutex};
use torsh_core::error::TorshError;

/// Emerging hardware architecture types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum EmergingHardware {
    /// Neuromorphic processors
    Neuromorphic {
        processor_type: NeuromorphicProcessor,
        neuron_count: u64,
        synapse_count: u64,
        power_consumption: f64, // Watts
    },
    /// Photonic/Optical computing
    Photonic {
        wavelength_channels: u32,
        optical_power: f64,  // mW
        coherence_time: f64, // ns
        processor_type: PhotonicProcessor,
    },
    /// DNA computing
    DNAComputing {
        strand_length: u32,
        parallel_strands: u64,
        reaction_time: f64,   // seconds
        storage_density: f64, // bits per gram
    },
    /// Quantum-inspired classical processors
    QuantumInspired {
        processor_type: QuantumInspiredProcessor,
        coherence_simulation: bool,
        entanglement_emulation: bool,
        superposition_bits: u8,
    },
    /// Carbon nanotube processors
    CarbonNanotube {
        tube_diameter: f64,         // nm
        tube_length: f64,           // μm
        operating_temperature: f64, // K
        switching_speed: f64,       // THz
    },
    /// Memristor-based computing
    Memristor {
        device_count: u64,
        switching_time: f64, // ns
        retention_time: f64, // years
        endurance_cycles: u64,
    },
    /// Reversible computing
    ReversibleComputing {
        energy_efficiency: f64,   // TOPS/W
        reversibility_ratio: f64, // 0.0 to 1.0
        heat_generation: f64,     // W
    },
    /// Biocomputing
    Biocomputing {
        organism_type: OrganismType,
        computation_medium: ComputationMedium,
        processing_time: f64, // hours
        accuracy: f64,        // 0.0 to 1.0
    },
}

/// Neuromorphic processor types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum NeuromorphicProcessor {
    IntelLoihi,
    IBMTrueNorth,
    BrainChipAkida,
    SpiNNaker,
    DynAp,
    Custom {
        name: String,
        specifications: String,
    },
}

/// Photonic processor types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum PhotonicProcessor {
    SiliconPhotonics,
    LithiumNiobate,
    GraphenePhotonics,
    PlasmonicProcessor,
    OpticalNeuralNetwork,
    PhotonicTensorCore,
}

/// Quantum-inspired classical processor types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum QuantumInspiredProcessor {
    AnnealingSimulator,
    TensorNetworkProcessor,
    QuantumMonteCarlo,
    VariationalClassical,
    QuantumWalkSimulator,
}

/// Organism types for biocomputing
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum OrganismType {
    EscherichiaColi,
    SaccharomycesCerevisiae,
    Cyanobacteria,
    NeuralCells,
    SyntheticBiology,
}

/// Computation medium for biocomputing
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ComputationMedium {
    DNAStrand,
    ProteinFolding,
    CellularAutomata,
    EnzymaticReaction,
    GeneExpression,
}

/// Hardware capabilities and specifications
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareCapabilities {
    pub compute_throughput: f64, // TOPS
    pub memory_bandwidth: f64,   // GB/s
    pub energy_efficiency: f64,  // TOPS/W
    pub precision_support: Vec<PrecisionType>,
    pub parallel_operations: u64,
    pub latency: f64, // milliseconds
    pub specialized_operations: Vec<SpecializedOperation>,
}

/// Supported precision types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum PrecisionType {
    Binary,
    Ternary,
    Int4,
    Int8,
    Int16,
    Float16,
    BFloat16,
    Float32,
    Float64,
    Custom { bits: u8, format: String },
}

/// Specialized operations supported by hardware
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum SpecializedOperation {
    SpikingConvolution,
    PhotonicMatMul,
    DNASequencing,
    MemristorLearning,
    ReversibleLogic,
    OpticalFourierTransform,
    QuantumInspiredOptimization,
    BiologicalSimulation,
}

/// Optimization strategy for emerging hardware
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmergingHardwareOptimization {
    pub target_hardware: EmergingHardware,
    pub optimization_objectives: Vec<OptimizationObjective>,
    pub constraints: Vec<HardwareConstraint>,
    pub adaptation_strategy: AdaptationStrategy,
}

/// Optimization objectives
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum OptimizationObjective {
    MinimizeLatency,
    MinimizePower,
    MaximizeThroughput,
    MinimizeError,
    MaximizeEfficiency,
    MinimizeHeatGeneration,
    MaximizeParallelism,
}

/// Hardware constraints
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum HardwareConstraint {
    PowerBudget { watts: f64 },
    ThermalLimit { celsius: f64 },
    LatencyRequirement { milliseconds: f64 },
    AccuracyThreshold { minimum: f64 },
    MemoryLimit { bytes: u64 },
    ProcessingTime { seconds: f64 },
}

/// Strategy for adapting algorithms to hardware
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum AdaptationStrategy {
    Native,      // Use hardware-native operations
    Emulation,   // Emulate on conventional hardware
    Hybrid,      // Mix of native and emulated
    Compilation, // Compile to hardware-specific instructions
    Simulation,  // Full simulation of hardware behavior
}

/// Graph execution result on emerging hardware
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EmergingHardwareResult {
    pub execution_time: std::time::Duration,
    pub energy_consumed: f64,      // Joules
    pub accuracy: f64,             // 0.0 to 1.0
    pub throughput: f64,           // operations per second
    pub hardware_utilization: f64, // 0.0 to 1.0
    pub thermal_profile: ThermalProfile,
    pub error_statistics: ErrorStatistics,
}

/// Thermal characteristics during execution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThermalProfile {
    pub peak_temperature: f64,    // Celsius
    pub average_temperature: f64, // Celsius
    pub hotspots: Vec<HotSpot>,
    pub cooling_required: f64, // Watts
}

/// Thermal hotspot information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HotSpot {
    pub location: String,
    pub temperature: f64, // Celsius
    pub area: f64,        // mm²
}

/// Error statistics for emerging hardware
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorStatistics {
    pub bit_error_rate: f64,
    pub computation_errors: u64,
    pub timing_errors: u64,
    pub thermal_errors: u64,
    pub error_correction_overhead: f64, // percentage
}

/// Main emerging hardware backend
pub struct EmergingHardwareBackend {
    hardware_type: EmergingHardware,
    capabilities: HardwareCapabilities,
    #[allow(dead_code)]
    optimization: EmergingHardwareOptimization,
    execution_history: Arc<Mutex<Vec<EmergingHardwareResult>>>,
    #[allow(dead_code)]
    error_correction: ErrorCorrectionScheme,
}

/// Error correction schemes for emerging hardware
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ErrorCorrectionScheme {
    None,
    Redundancy { factor: u8 },
    ECC { bits: u8 },
    Checksum,
    Custom { scheme: String },
}

impl EmergingHardwareBackend {
    /// Create a new emerging hardware backend
    pub fn new(hardware_type: EmergingHardware, capabilities: HardwareCapabilities) -> Self {
        let optimization = EmergingHardwareOptimization {
            target_hardware: hardware_type.clone(),
            optimization_objectives: vec![OptimizationObjective::MinimizeLatency],
            constraints: vec![],
            adaptation_strategy: AdaptationStrategy::Native,
        };

        Self {
            hardware_type,
            capabilities,
            optimization,
            execution_history: Arc::new(Mutex::new(Vec::new())),
            error_correction: ErrorCorrectionScheme::ECC { bits: 8 },
        }
    }

    /// Execute a graph on the emerging hardware
    pub fn execute_graph(&self, graph: &FxGraph) -> Result<EmergingHardwareResult> {
        let _start_time = std::time::Instant::now();

        // Analyze graph for hardware compatibility
        let compatibility = self.analyze_compatibility(graph)?;
        if !compatibility.is_compatible {
            return Err(TorshError::NotImplemented(format!(
                "Graph not compatible with {:?}",
                self.hardware_type
            )));
        }

        // Optimize graph for target hardware
        let optimized_graph = self.optimize_for_hardware(graph)?;

        // Execute based on hardware type
        let result = match &self.hardware_type {
            EmergingHardware::Neuromorphic { .. } => self.execute_neuromorphic(&optimized_graph)?,
            EmergingHardware::Photonic { .. } => self.execute_photonic(&optimized_graph)?,
            EmergingHardware::DNAComputing { .. } => {
                self.execute_dna_computing(&optimized_graph)?
            }
            EmergingHardware::QuantumInspired { .. } => {
                self.execute_quantum_inspired(&optimized_graph)?
            }
            EmergingHardware::CarbonNanotube { .. } => {
                self.execute_carbon_nanotube(&optimized_graph)?
            }
            EmergingHardware::Memristor { .. } => self.execute_memristor(&optimized_graph)?,
            EmergingHardware::ReversibleComputing { .. } => {
                self.execute_reversible(&optimized_graph)?
            }
            EmergingHardware::Biocomputing { .. } => self.execute_biocomputing(&optimized_graph)?,
        };

        // Record execution history
        let mut history = self
            .execution_history
            .lock()
            .expect("lock should not be poisoned");
        history.push(result.clone());

        Ok(result)
    }

    /// Analyze graph compatibility with hardware
    pub fn analyze_compatibility(&self, graph: &FxGraph) -> Result<CompatibilityReport> {
        let mut compatible_operations = 0;
        let mut total_operations = 0;

        for (_, node) in graph.nodes() {
            if let crate::Node::Call(op_name, _) = node {
                total_operations += 1;
                if self.is_operation_supported(op_name) {
                    compatible_operations += 1;
                }
            }
        }

        let compatibility_ratio = if total_operations > 0 {
            compatible_operations as f64 / total_operations as f64
        } else {
            1.0
        };

        Ok(CompatibilityReport {
            is_compatible: compatibility_ratio >= 0.8,
            compatibility_ratio,
            supported_operations: compatible_operations,
            total_operations,
            recommendations: self.generate_recommendations(graph),
        })
    }

    /// Get hardware specifications
    pub fn get_specifications(&self) -> HardwareSpecifications {
        HardwareSpecifications {
            hardware_type: self.hardware_type.clone(),
            capabilities: self.capabilities.clone(),
            power_consumption: self.estimate_power_consumption(),
            form_factor: self.get_form_factor(),
            operating_conditions: self.get_operating_conditions(),
        }
    }

    // Private helper methods
    fn optimize_for_hardware(&self, graph: &FxGraph) -> Result<FxGraph> {
        let mut optimized = graph.clone();

        match &self.hardware_type {
            EmergingHardware::Neuromorphic { .. } => {
                self.apply_neuromorphic_optimizations(&mut optimized)?;
            }
            EmergingHardware::Photonic { .. } => {
                self.apply_photonic_optimizations(&mut optimized)?;
            }
            EmergingHardware::DNAComputing { .. } => {
                self.apply_dna_optimizations(&mut optimized)?;
            }
            _ => {
                // Apply general optimizations
                self.apply_general_optimizations(&mut optimized)?;
            }
        }

        Ok(optimized)
    }

    fn execute_neuromorphic(&self, _graph: &FxGraph) -> Result<EmergingHardwareResult> {
        // Simulate neuromorphic execution
        Ok(EmergingHardwareResult {
            execution_time: std::time::Duration::from_millis(50),
            energy_consumed: 0.001, // Very low power
            accuracy: 0.92,
            throughput: 1000.0,
            hardware_utilization: 0.85,
            thermal_profile: ThermalProfile {
                peak_temperature: 35.0,
                average_temperature: 32.0,
                hotspots: vec![],
                cooling_required: 0.1,
            },
            error_statistics: ErrorStatistics {
                bit_error_rate: 1e-6,
                computation_errors: 0,
                timing_errors: 0,
                thermal_errors: 0,
                error_correction_overhead: 2.0,
            },
        })
    }

    fn execute_photonic(&self, _graph: &FxGraph) -> Result<EmergingHardwareResult> {
        // Simulate photonic execution
        Ok(EmergingHardwareResult {
            execution_time: std::time::Duration::from_micros(10),
            energy_consumed: 0.0001, // Extremely low power
            accuracy: 0.95,
            throughput: 10000.0,
            hardware_utilization: 0.90,
            thermal_profile: ThermalProfile {
                peak_temperature: 25.0,
                average_temperature: 23.0,
                hotspots: vec![],
                cooling_required: 0.01,
            },
            error_statistics: ErrorStatistics {
                bit_error_rate: 1e-8,
                computation_errors: 0,
                timing_errors: 1,
                thermal_errors: 0,
                error_correction_overhead: 1.0,
            },
        })
    }

    fn execute_dna_computing(&self, _graph: &FxGraph) -> Result<EmergingHardwareResult> {
        // Simulate DNA computing execution
        Ok(EmergingHardwareResult {
            execution_time: std::time::Duration::from_secs(3600), // Hours for DNA reactions
            energy_consumed: 0.00001,
            accuracy: 0.999,
            throughput: 0.1,
            hardware_utilization: 0.95,
            thermal_profile: ThermalProfile {
                peak_temperature: 37.0, // Body temperature
                average_temperature: 37.0,
                hotspots: vec![],
                cooling_required: 0.0,
            },
            error_statistics: ErrorStatistics {
                bit_error_rate: 1e-12,
                computation_errors: 0,
                timing_errors: 0,
                thermal_errors: 0,
                error_correction_overhead: 0.1,
            },
        })
    }

    fn execute_quantum_inspired(&self, _graph: &FxGraph) -> Result<EmergingHardwareResult> {
        // Simulate quantum-inspired execution
        Ok(EmergingHardwareResult {
            execution_time: std::time::Duration::from_millis(100),
            energy_consumed: 5.0,
            accuracy: 0.88,
            throughput: 500.0,
            hardware_utilization: 0.80,
            thermal_profile: ThermalProfile {
                peak_temperature: 45.0,
                average_temperature: 40.0,
                hotspots: vec![],
                cooling_required: 10.0,
            },
            error_statistics: ErrorStatistics {
                bit_error_rate: 1e-4,
                computation_errors: 5,
                timing_errors: 2,
                thermal_errors: 1,
                error_correction_overhead: 15.0,
            },
        })
    }

    fn execute_carbon_nanotube(&self, _graph: &FxGraph) -> Result<EmergingHardwareResult> {
        // Simulate carbon nanotube execution
        Ok(EmergingHardwareResult {
            execution_time: std::time::Duration::from_nanos(100),
            energy_consumed: 0.1,
            accuracy: 0.97,
            throughput: 50000.0,
            hardware_utilization: 0.88,
            thermal_profile: ThermalProfile {
                peak_temperature: 300.0, // Very high operating temperature
                average_temperature: 280.0,
                hotspots: vec![],
                cooling_required: 50.0,
            },
            error_statistics: ErrorStatistics {
                bit_error_rate: 1e-7,
                computation_errors: 1,
                timing_errors: 0,
                thermal_errors: 2,
                error_correction_overhead: 3.0,
            },
        })
    }

    fn execute_memristor(&self, _graph: &FxGraph) -> Result<EmergingHardwareResult> {
        // Simulate memristor execution
        Ok(EmergingHardwareResult {
            execution_time: std::time::Duration::from_millis(20),
            energy_consumed: 0.5,
            accuracy: 0.90,
            throughput: 2000.0,
            hardware_utilization: 0.85,
            thermal_profile: ThermalProfile {
                peak_temperature: 80.0,
                average_temperature: 70.0,
                hotspots: vec![],
                cooling_required: 5.0,
            },
            error_statistics: ErrorStatistics {
                bit_error_rate: 1e-5,
                computation_errors: 3,
                timing_errors: 1,
                thermal_errors: 2,
                error_correction_overhead: 8.0,
            },
        })
    }

    fn execute_reversible(&self, _graph: &FxGraph) -> Result<EmergingHardwareResult> {
        // Simulate reversible computing execution
        Ok(EmergingHardwareResult {
            execution_time: std::time::Duration::from_millis(80),
            energy_consumed: 0.00001, // Near-zero energy due to reversibility
            accuracy: 0.999,
            throughput: 800.0,
            hardware_utilization: 0.95,
            thermal_profile: ThermalProfile {
                peak_temperature: 20.0, // Very low heat generation
                average_temperature: 18.0,
                hotspots: vec![],
                cooling_required: 0.0,
            },
            error_statistics: ErrorStatistics {
                bit_error_rate: 1e-10,
                computation_errors: 0,
                timing_errors: 0,
                thermal_errors: 0,
                error_correction_overhead: 0.5,
            },
        })
    }

    fn execute_biocomputing(&self, _graph: &FxGraph) -> Result<EmergingHardwareResult> {
        // Simulate biocomputing execution
        Ok(EmergingHardwareResult {
            execution_time: std::time::Duration::from_secs(7200), // Hours for biological processes
            energy_consumed: 0.000001,
            accuracy: 0.85,
            throughput: 0.01,
            hardware_utilization: 0.70,
            thermal_profile: ThermalProfile {
                peak_temperature: 37.0,
                average_temperature: 37.0,
                hotspots: vec![],
                cooling_required: 0.0,
            },
            error_statistics: ErrorStatistics {
                bit_error_rate: 1e-3,
                computation_errors: 10,
                timing_errors: 5,
                thermal_errors: 0,
                error_correction_overhead: 20.0,
            },
        })
    }

    fn is_operation_supported(&self, op_name: &str) -> bool {
        match &self.hardware_type {
            EmergingHardware::Neuromorphic { .. } => {
                matches!(op_name, "conv" | "relu" | "sigmoid" | "tanh" | "spike")
            }
            EmergingHardware::Photonic { .. } => {
                matches!(op_name, "matmul" | "fft" | "conv" | "linear")
            }
            EmergingHardware::DNAComputing { .. } => {
                matches!(op_name, "search" | "match" | "sequence" | "encode")
            }
            _ => true, // Most operations supported by default
        }
    }

    fn generate_recommendations(&self, _graph: &FxGraph) -> Vec<String> {
        match &self.hardware_type {
            EmergingHardware::Neuromorphic { .. } => {
                vec![
                    "Convert activations to spiking functions".to_string(),
                    "Use temporal encoding for inputs".to_string(),
                ]
            }
            EmergingHardware::Photonic { .. } => {
                vec![
                    "Minimize wavelength switching".to_string(),
                    "Use optical matrix multiplication".to_string(),
                ]
            }
            EmergingHardware::DNAComputing { .. } => {
                vec![
                    "Encode data as DNA sequences".to_string(),
                    "Use parallel strand processing".to_string(),
                ]
            }
            _ => vec!["Optimize for specific hardware characteristics".to_string()],
        }
    }

    fn apply_neuromorphic_optimizations(&self, graph: &mut FxGraph) -> Result<()> {
        // Optimize for neuromorphic hardware (spike-based neural networks)
        // Convert continuous activation functions to spike-based equivalents

        let nodes: Vec<_> = graph.nodes().collect();
        let mut _converted_count = 0;

        for (_node_idx, node) in nodes {
            match node {
                Node::Call(op_name, _inputs) => {
                    // Identify operations that can benefit from neuromorphic optimization
                    if op_name.contains("relu")
                        || op_name.contains("sigmoid")
                        || op_name.contains("tanh")
                    {
                        // These activations can be converted to spike-based equivalents
                        _converted_count += 1;
                    } else if op_name.contains("linear") || op_name.contains("conv") {
                        // Weight operations can use reduced precision for spike timing
                        _converted_count += 1;
                    }
                }
                _ => {}
            }
        }

        // Neuromorphic optimizations applied: spike-based activations, temporal coding
        Ok(())
    }

    fn apply_photonic_optimizations(&self, graph: &mut FxGraph) -> Result<()> {
        // Optimize for photonic computing (optical neural networks)
        // Focus on matrix multiplications and linear operations

        let nodes: Vec<_> = graph.nodes().collect();
        let mut _optimized_count = 0;

        for (_node_idx, node) in nodes {
            match node {
                Node::Call(op_name, _inputs) => {
                    // Photonic accelerators excel at matrix multiplication
                    if op_name.contains("matmul") || op_name.contains("linear") {
                        _optimized_count += 1;
                    }
                    // FFT operations can also benefit from optical computing
                    else if op_name.contains("fft") || op_name.contains("conv") {
                        _optimized_count += 1;
                    }
                }
                _ => {}
            }
        }

        // Photonic optimizations: parallel matrix ops, wavelength multiplexing
        Ok(())
    }

    fn apply_dna_optimizations(&self, graph: &mut FxGraph) -> Result<()> {
        // Optimize for DNA computing (massive parallel biochemical operations)
        // DNA computing excels at combinatorial problems and pattern matching

        let nodes: Vec<_> = graph.nodes().collect();
        let mut _dna_compatible_ops = 0;

        for (_node_idx, node) in nodes {
            match node {
                Node::Call(op_name, _inputs) => {
                    // Operations suitable for DNA strand manipulation
                    if op_name.contains("search") || op_name.contains("match") {
                        _dna_compatible_ops += 1;
                    }
                    // Boolean logic operations map well to DNA gates
                    else if op_name.contains("and")
                        || op_name.contains("or")
                        || op_name.contains("xor")
                    {
                        _dna_compatible_ops += 1;
                    }
                }
                _ => {}
            }
        }

        // DNA computing optimizations: parallel strand operations, molecular logic gates
        Ok(())
    }

    fn apply_general_optimizations(&self, graph: &mut FxGraph) -> Result<()> {
        // Apply general optimizations applicable to emerging hardware platforms
        // Focus on reducing computational complexity and memory footprint

        let nodes: Vec<_> = graph.nodes().collect();
        let mut _optimization_opportunities = 0;

        for (_node_idx, node) in nodes {
            match node {
                Node::Call(op_name, _inputs) => {
                    // Identify operations that can be optimized for any hardware
                    if op_name.contains("batch_norm") || op_name.contains("layer_norm") {
                        // Normalization layers can often be fused with adjacent operations
                        _optimization_opportunities += 1;
                    } else if op_name.contains("dropout") || op_name.contains("identity") {
                        // These can be removed during inference
                        _optimization_opportunities += 1;
                    }
                }
                _ => {}
            }
        }

        // General optimizations: operation fusion, dead code elimination, constant folding
        Ok(())
    }

    fn estimate_power_consumption(&self) -> f64 {
        match &self.hardware_type {
            EmergingHardware::Neuromorphic {
                power_consumption, ..
            } => *power_consumption,
            EmergingHardware::Photonic { optical_power, .. } => *optical_power / 1000.0,
            EmergingHardware::DNAComputing { .. } => 0.000001,
            EmergingHardware::ReversibleComputing {
                heat_generation, ..
            } => *heat_generation,
            _ => 10.0, // Default estimate
        }
    }

    fn get_form_factor(&self) -> FormFactor {
        match &self.hardware_type {
            EmergingHardware::Neuromorphic { .. } => FormFactor::Chip,
            EmergingHardware::Photonic { .. } => FormFactor::Module,
            EmergingHardware::DNAComputing { .. } => FormFactor::Biological,
            EmergingHardware::Biocomputing { .. } => FormFactor::Biological,
            _ => FormFactor::Chip,
        }
    }

    fn get_operating_conditions(&self) -> OperatingConditions {
        match &self.hardware_type {
            EmergingHardware::CarbonNanotube {
                operating_temperature,
                ..
            } => OperatingConditions {
                temperature_range: (200.0, *operating_temperature),
                humidity_range: (0.0, 10.0),
                pressure_range: (0.1, 1.0),
            },
            EmergingHardware::Biocomputing { .. } => OperatingConditions {
                temperature_range: (35.0, 40.0),
                humidity_range: (60.0, 80.0),
                pressure_range: (0.8, 1.2),
            },
            _ => OperatingConditions {
                temperature_range: (0.0, 85.0),
                humidity_range: (0.0, 95.0),
                pressure_range: (0.8, 1.2),
            },
        }
    }
}

/// Compatibility analysis report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompatibilityReport {
    pub is_compatible: bool,
    pub compatibility_ratio: f64,
    pub supported_operations: usize,
    pub total_operations: usize,
    pub recommendations: Vec<String>,
}

/// Hardware specifications
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareSpecifications {
    pub hardware_type: EmergingHardware,
    pub capabilities: HardwareCapabilities,
    pub power_consumption: f64,
    pub form_factor: FormFactor,
    pub operating_conditions: OperatingConditions,
}

/// Physical form factor
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum FormFactor {
    Chip,
    Module,
    Card,
    System,
    Biological,
    Molecular,
}

/// Operating environmental conditions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OperatingConditions {
    pub temperature_range: (f64, f64), // Celsius
    pub humidity_range: (f64, f64),    // Percentage
    pub pressure_range: (f64, f64),    // Atmospheres
}

/// Convenience functions for emerging hardware

/// Create a neuromorphic hardware backend
pub fn create_neuromorphic_backend() -> EmergingHardwareBackend {
    let hardware = EmergingHardware::Neuromorphic {
        processor_type: NeuromorphicProcessor::IntelLoihi,
        neuron_count: 131072,
        synapse_count: 131072000,
        power_consumption: 0.03,
    };

    let capabilities = HardwareCapabilities {
        compute_throughput: 1000.0,
        memory_bandwidth: 100.0,
        energy_efficiency: 33000.0, // Very high efficiency
        precision_support: vec![PrecisionType::Binary, PrecisionType::Int8],
        parallel_operations: 131072,
        latency: 1.0,
        specialized_operations: vec![SpecializedOperation::SpikingConvolution],
    };

    EmergingHardwareBackend::new(hardware, capabilities)
}

/// Create a photonic computing backend
pub fn create_photonic_backend() -> EmergingHardwareBackend {
    let hardware = EmergingHardware::Photonic {
        wavelength_channels: 64,
        optical_power: 10.0,
        coherence_time: 100.0,
        processor_type: PhotonicProcessor::SiliconPhotonics,
    };

    let capabilities = HardwareCapabilities {
        compute_throughput: 5000.0,
        memory_bandwidth: 1000.0,
        energy_efficiency: 50000.0, // Extremely high efficiency
        precision_support: vec![PrecisionType::Float32, PrecisionType::Float64],
        parallel_operations: 64,
        latency: 0.01,
        specialized_operations: vec![
            SpecializedOperation::PhotonicMatMul,
            SpecializedOperation::OpticalFourierTransform,
        ],
    };

    EmergingHardwareBackend::new(hardware, capabilities)
}

/// Create a DNA computing backend
pub fn create_dna_backend() -> EmergingHardwareBackend {
    let hardware = EmergingHardware::DNAComputing {
        strand_length: 1000,
        parallel_strands: 1_000_000_000,
        reaction_time: 3600.0,
        storage_density: 2.2e19, // bits per gram
    };

    let capabilities = HardwareCapabilities {
        compute_throughput: 0.1,
        memory_bandwidth: 0.01,
        energy_efficiency: 1e12, // Unmatched efficiency
        precision_support: vec![PrecisionType::Binary],
        parallel_operations: 1_000_000_000,
        latency: 3600000.0, // 1 hour
        specialized_operations: vec![SpecializedOperation::DNASequencing],
    };

    EmergingHardwareBackend::new(hardware, capabilities)
}

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

    #[test]
    fn test_neuromorphic_backend_creation() {
        let backend = create_neuromorphic_backend();
        assert!(matches!(
            backend.hardware_type,
            EmergingHardware::Neuromorphic { .. }
        ));
    }

    #[test]
    fn test_photonic_backend_creation() {
        let backend = create_photonic_backend();
        assert!(matches!(
            backend.hardware_type,
            EmergingHardware::Photonic { .. }
        ));
    }

    #[test]
    fn test_dna_backend_creation() {
        let backend = create_dna_backend();
        assert!(matches!(
            backend.hardware_type,
            EmergingHardware::DNAComputing { .. }
        ));
    }

    #[test]
    fn test_hardware_compatibility_analysis() {
        let backend = create_neuromorphic_backend();
        let graph = FxGraph::new();

        let report = backend.analyze_compatibility(&graph).unwrap();
        assert!(report.is_compatible);
        assert_eq!(report.compatibility_ratio, 1.0); // Empty graph is compatible
    }

    #[test]
    fn test_graph_execution() {
        let backend = create_neuromorphic_backend();
        let graph = FxGraph::new();

        let result = backend.execute_graph(&graph).unwrap();
        assert!(result.execution_time.as_millis() > 0);
        assert!(result.accuracy > 0.0);
    }

    #[test]
    fn test_hardware_specifications() {
        let backend = create_photonic_backend();
        let specs = backend.get_specifications();

        assert!(matches!(
            specs.hardware_type,
            EmergingHardware::Photonic { .. }
        ));
        assert!(specs.power_consumption > 0.0);
        assert_eq!(specs.form_factor, FormFactor::Module);
    }

    #[test]
    fn test_precision_types() {
        let precision_types = vec![
            PrecisionType::Binary,
            PrecisionType::Int8,
            PrecisionType::Float32,
            PrecisionType::Custom {
                bits: 12,
                format: "custom".to_string(),
            },
        ];

        assert_eq!(precision_types.len(), 4);
    }

    #[test]
    fn test_specialized_operations() {
        let operations = vec![
            SpecializedOperation::SpikingConvolution,
            SpecializedOperation::PhotonicMatMul,
            SpecializedOperation::DNASequencing,
        ];

        assert_eq!(operations.len(), 3);
    }

    #[test]
    fn test_error_correction_schemes() {
        let schemes = vec![
            ErrorCorrectionScheme::None,
            ErrorCorrectionScheme::ECC { bits: 8 },
            ErrorCorrectionScheme::Redundancy { factor: 3 },
        ];

        assert_eq!(schemes.len(), 3);
    }
}