//! Concrete Exotic Hardware Implementations
//!
//! This module provides concrete implementations for TPU, FPGA, and other
//! exotic hardware accelerators.

use crate::error::{Result, SklearsError};
use crate::exotic_hardware::{HardwareCapabilities, HardwareId, HardwareType, Precision};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// TPU (Tensor Processing Unit) implementation
///
/// Provides concrete implementation for Google's TPU accelerators
/// with matrix operation optimization and automatic graph compilation.
#[derive(Debug)]
pub struct TPUAccelerator {
    /// Hardware identification
    pub hardware_id: HardwareId,
    /// TPU capabilities
    pub capabilities: TPUCapabilities,
    /// Compilation cache for reusing compiled graphs
    pub compilation_cache: HashMap<String, CompiledGraph>,
    /// Current execution context
    pub context: TPUContext,
}

/// TPU-specific capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TPUCapabilities {
    /// Base hardware capabilities
    pub base: HardwareCapabilities,
    /// Number of TPU cores
    pub num_cores: u32,
    /// Matrix multiply units per core
    pub mxu_per_core: u32,
    /// High bandwidth memory in GB
    pub hbm_gb: f64,
    /// Peak TFLOPS
    pub peak_tflops: f64,
    /// Supports bfloat16
    pub supports_bfloat16: bool,
}

/// TPU execution context
#[derive(Debug, Clone)]
pub struct TPUContext {
    /// Current batch size
    pub batch_size: usize,
    /// Precision mode
    pub precision: Precision,
    /// Enable XLA compilation
    pub use_xla: bool,
    /// Tensor layout format
    pub layout: TensorLayout,
}

/// Tensor layout format
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TensorLayout {
    /// Row-major (C-style)
    RowMajor,
    /// Column-major (Fortran-style)
    ColumnMajor,
    /// TPU-optimized tile format
    Tiled { tile_size: usize },
}

/// Compiled computation graph for TPU
#[derive(Debug, Clone)]
pub struct CompiledGraph {
    /// Graph identifier
    pub id: String,
    /// Compilation timestamp
    pub compiled_at: std::time::SystemTime,
    /// Optimized operations
    pub operations: Vec<TPUOperation>,
    /// Memory layout
    pub memory_layout: Vec<MemoryAllocation>,
}

/// TPU operation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TPUOperation {
    /// Matrix multiplication
    MatMul {
        m: usize,
        n: usize,
        k: usize,
        precision: Precision,
    },
    /// Convolution
    Conv2D {
        input_channels: usize,
        output_channels: usize,
        kernel_size: (usize, usize),
    },
    /// Element-wise operation
    ElementWise {
        op_type: ElementWiseOp,
        num_elements: usize,
    },
    /// Reduction operation
    Reduce {
        op_type: ReductionOp,
        axis: Option<usize>,
    },
}

/// Element-wise operation type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ElementWiseOp {
    Add,
    Multiply,
    ReLU,
    Tanh,
    Sigmoid,
}

/// Reduction operation type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ReductionOp {
    Sum,
    Mean,
    Max,
    Min,
}

/// Memory allocation on TPU
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryAllocation {
    /// Allocation ID
    pub id: String,
    /// Size in bytes
    pub size_bytes: usize,
    /// Memory type
    pub memory_type: MemoryType,
}

/// TPU memory type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MemoryType {
    /// High bandwidth memory (HBM)
    HBM,
    /// Chip memory
    ChipMemory,
    /// Host memory
    HostMemory,
}

impl TPUAccelerator {
    /// Create a new TPU accelerator
    pub fn new(device_index: u32) -> Self {
        Self {
            hardware_id: HardwareId {
                device_type: HardwareType::TPU,
                device_index,
                vendor: "Google".to_string(),
                model: "TPU v4".to_string(),
            },
            capabilities: TPUCapabilities {
                base: HardwareCapabilities {
                    compute_units: 1,
                    memory_gb: 96.0,
                    peak_performance_ops: 275e12,
                    supported_precisions: vec![
                        Precision::Float32,
                        Precision::BFloat16,
                        Precision::Float16,
                    ],
                    supports_sparsity: true,
                    supports_quantization: true,
                    supports_dynamic_shapes: true,
                    custom_features: HashMap::new(),
                },
                num_cores: 2,
                mxu_per_core: 128,
                hbm_gb: 96.0,
                peak_tflops: 275.0,
                supports_bfloat16: true,
            },
            compilation_cache: HashMap::new(),
            context: TPUContext {
                batch_size: 32,
                precision: Precision::BFloat16,
                use_xla: true,
                layout: TensorLayout::Tiled { tile_size: 128 },
            },
        }
    }

    /// Compile a computation graph for TPU execution
    pub fn compile_graph(&mut self, operations: Vec<TPUOperation>) -> Result<String> {
        let graph_id = format!("graph_{}", self.compilation_cache.len());

        let compiled = CompiledGraph {
            id: graph_id.clone(),
            compiled_at: std::time::SystemTime::now(),
            operations,
            memory_layout: vec![],
        };

        self.compilation_cache.insert(graph_id.clone(), compiled);
        Ok(graph_id)
    }

    /// Execute a compiled graph
    pub fn execute_graph(&self, graph_id: &str, inputs: &[f32]) -> Result<Vec<f32>> {
        let _graph = self.compilation_cache.get(graph_id).ok_or_else(|| {
            SklearsError::InvalidOperation(format!("Graph {} not found", graph_id))
        })?;

        // Simulate execution
        Ok(inputs.to_vec())
    }

    /// Get performance estimate for an operation
    pub fn estimate_performance(&self, operation: &TPUOperation) -> PerformanceEstimate {
        match operation {
            TPUOperation::MatMul { m, n, k, precision } => {
                let ops = 2 * m * n * k;
                let flops_per_second = match precision {
                    Precision::BFloat16 => self.capabilities.peak_tflops * 1e12,
                    Precision::Float32 => self.capabilities.peak_tflops * 0.5 * 1e12,
                    _ => self.capabilities.peak_tflops * 0.25 * 1e12,
                };

                let time_ms = (ops as f64 / flops_per_second * 1000.0).max(0.001); // Ensure at least 0.001 ms
                PerformanceEstimate {
                    execution_time_ms: time_ms.ceil() as u64, // Round up to ensure at least 1
                    memory_bandwidth_gb: (*m * *n * 4) as f64 / 1e9,
                    utilization: 0.8,
                }
            }
            _ => PerformanceEstimate {
                execution_time_ms: 1,
                memory_bandwidth_gb: 0.1,
                utilization: 0.5,
            },
        }
    }
}

/// Performance estimate for an operation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceEstimate {
    /// Estimated execution time in milliseconds
    pub execution_time_ms: u64,
    /// Memory bandwidth usage in GB
    pub memory_bandwidth_gb: f64,
    /// Hardware utilization (0.0 to 1.0)
    pub utilization: f64,
}

/// FPGA (Field-Programmable Gate Array) implementation
///
/// Provides concrete implementation for FPGA accelerators with
/// customizable pipeline configurations.
#[derive(Debug)]
pub struct FPGAAccelerator {
    /// Hardware identification
    pub hardware_id: HardwareId,
    /// FPGA capabilities
    pub capabilities: FPGACapabilities,
    /// Configured pipelines
    pub pipelines: Vec<FPGAPipeline>,
    /// Bitstream cache
    pub bitstream_cache: HashMap<String, Bitstream>,
}

/// FPGA-specific capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FPGACapabilities {
    /// Base hardware capabilities
    pub base: HardwareCapabilities,
    /// Number of logic elements
    pub logic_elements: usize,
    /// DSP blocks
    pub dsp_blocks: usize,
    /// Block RAM in kilobytes
    pub block_ram_kb: usize,
    /// Maximum clock frequency in MHz
    pub max_clock_mhz: f64,
}

/// FPGA pipeline configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FPGAPipeline {
    /// Pipeline name
    pub name: String,
    /// Pipeline stages
    pub stages: Vec<PipelineStage>,
    /// Throughput (operations per second)
    pub throughput: f64,
    /// Latency in clock cycles
    pub latency_cycles: usize,
}

/// Pipeline stage
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PipelineStage {
    /// Stage name
    pub name: String,
    /// Operation type
    pub operation: String,
    /// Resource utilization
    pub resource_usage: ResourceUsage,
}

/// FPGA resource usage
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUsage {
    /// Logic elements used
    pub logic_elements: usize,
    /// DSP blocks used
    pub dsp_blocks: usize,
    /// Block RAM used (in KB)
    pub block_ram_kb: usize,
}

/// FPGA bitstream
#[derive(Debug, Clone)]
pub struct Bitstream {
    /// Bitstream identifier
    pub id: String,
    /// Bitstream data
    pub data: Vec<u8>,
    /// Configuration for this bitstream
    pub config: FPGAConfig,
}

/// FPGA configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FPGAConfig {
    /// Clock frequency in MHz
    pub clock_mhz: f64,
    /// Pipeline depth
    pub pipeline_depth: usize,
    /// Data width in bits
    pub data_width: usize,
}

impl FPGAAccelerator {
    /// Create a new FPGA accelerator
    pub fn new(device_index: u32) -> Self {
        Self {
            hardware_id: HardwareId {
                device_type: HardwareType::FPGA,
                device_index,
                vendor: "Xilinx".to_string(),
                model: "Alveo U250".to_string(),
            },
            capabilities: FPGACapabilities {
                base: HardwareCapabilities {
                    compute_units: 1,
                    memory_gb: 64.0,
                    peak_performance_ops: 90e12,
                    supported_precisions: vec![
                        Precision::Float32,
                        Precision::Int32,
                        Precision::Int16,
                        Precision::Custom(8),
                    ],
                    supports_sparsity: true,
                    supports_quantization: true,
                    supports_dynamic_shapes: false,
                    custom_features: HashMap::new(),
                },
                logic_elements: 1172000,
                dsp_blocks: 12288,
                block_ram_kb: 77824,
                max_clock_mhz: 450.0,
            },
            pipelines: Vec::new(),
            bitstream_cache: HashMap::new(),
        }
    }

    /// Configure a new pipeline
    pub fn configure_pipeline(&mut self, pipeline: FPGAPipeline) -> Result<()> {
        // Validate resource usage
        let total_usage = pipeline.stages.iter().fold(
            ResourceUsage {
                logic_elements: 0,
                dsp_blocks: 0,
                block_ram_kb: 0,
            },
            |acc, stage| ResourceUsage {
                logic_elements: acc.logic_elements + stage.resource_usage.logic_elements,
                dsp_blocks: acc.dsp_blocks + stage.resource_usage.dsp_blocks,
                block_ram_kb: acc.block_ram_kb + stage.resource_usage.block_ram_kb,
            },
        );

        if total_usage.logic_elements > self.capabilities.logic_elements {
            return Err(SklearsError::InvalidOperation(
                "Insufficient logic elements".to_string(),
            ));
        }

        self.pipelines.push(pipeline);
        Ok(())
    }

    /// Program the FPGA with a bitstream
    pub fn program_bitstream(&mut self, bitstream: Bitstream) -> Result<()> {
        self.bitstream_cache.insert(bitstream.id.clone(), bitstream);
        Ok(())
    }

    /// Execute a pipeline
    pub fn execute_pipeline(&self, pipeline_name: &str, data: &[f32]) -> Result<Vec<f32>> {
        let _pipeline = self
            .pipelines
            .iter()
            .find(|p| p.name == pipeline_name)
            .ok_or_else(|| {
                SklearsError::InvalidOperation(format!("Pipeline {} not found", pipeline_name))
            })?;

        // Simulate execution
        Ok(data.to_vec())
    }
}

// ============================================================================
// Quantum Computing Implementation
// ============================================================================

/// Quantum Computing accelerator for quantum machine learning
///
/// Provides interface for quantum computing platforms with support for
/// variational quantum algorithms, quantum kernels, and quantum neural networks.
#[derive(Debug)]
pub struct QuantumAccelerator {
    /// Hardware identification
    pub hardware_id: HardwareId,
    /// Quantum capabilities
    pub capabilities: QuantumCapabilities,
    /// Quantum circuits
    pub circuits: HashMap<String, QuantumCircuit>,
    /// Current quantum backend
    pub backend: QuantumBackend,
}

/// Quantum computing capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumCapabilities {
    /// Base hardware capabilities
    pub base: HardwareCapabilities,
    /// Number of qubits
    pub num_qubits: usize,
    /// Qubit connectivity graph
    pub connectivity: ConnectivityGraph,
    /// Gate fidelity (0.0-1.0)
    pub gate_fidelity: f64,
    /// Measurement fidelity (0.0-1.0)
    pub measurement_fidelity: f64,
    /// T1 coherence time in microseconds
    pub t1_coherence_us: f64,
    /// T2 coherence time in microseconds
    pub t2_coherence_us: f64,
    /// Supported gate set
    pub supported_gates: Vec<QuantumGate>,
    /// Supports mid-circuit measurement
    pub supports_mid_circuit_measurement: bool,
}

/// Qubit connectivity graph
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConnectivityGraph {
    /// Number of qubits
    pub num_qubits: usize,
    /// Edges (qubit pairs that can be connected with 2-qubit gates)
    pub edges: Vec<(usize, usize)>,
    /// Topology type
    pub topology: TopologyType,
}

/// Quantum chip topology type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum TopologyType {
    /// Linear chain
    Linear,
    /// 2D grid
    Grid2D,
    /// Heavy-hex (IBM)
    HeavyHex,
    /// All-to-all (full connectivity)
    AllToAll,
    /// Custom topology
    Custom,
}

/// Quantum gate types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantumGate {
    // Single-qubit gates
    Hadamard,
    PauliX,
    PauliY,
    PauliZ,
    RX,
    RY,
    RZ,
    Phase,
    T,
    S,
    // Two-qubit gates
    CNOT,
    CZ,
    SWAP,
    // Three-qubit gates
    Toffoli,
    Fredkin,
    // Measurement
    Measure,
}

/// Quantum circuit representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumCircuit {
    /// Circuit name
    pub name: String,
    /// Number of qubits
    pub num_qubits: usize,
    /// Number of classical bits for measurement
    pub num_classical_bits: usize,
    /// Gates in the circuit
    pub gates: Vec<QuantumGateOp>,
    /// Circuit depth
    pub depth: usize,
}

/// Quantum gate operation in a circuit
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumGateOp {
    /// Gate type
    pub gate: QuantumGate,
    /// Target qubit(s)
    pub qubits: Vec<usize>,
    /// Gate parameters (for parameterized gates)
    pub parameters: Vec<f64>,
    /// Classical control (for conditional gates)
    pub control: Option<ClassicalControl>,
}

/// Classical control for conditional operations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClassicalControl {
    /// Classical register bit
    pub bit: usize,
    /// Value to condition on
    pub value: bool,
}

/// Quantum backend type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantumBackend {
    /// Ideal simulator (no noise)
    Simulator,
    /// Noisy simulator with realistic errors
    NoisySimulator,
    /// Actual quantum hardware
    Hardware,
    /// Cloud-based quantum processor
    Cloud,
}

/// Quantum measurement result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumMeasurement {
    /// Bitstring outcomes
    pub outcomes: Vec<String>,
    /// Counts for each outcome
    pub counts: HashMap<String, usize>,
    /// Total number of shots
    pub total_shots: usize,
}

impl QuantumAccelerator {
    /// Create a new quantum accelerator
    pub fn new(num_qubits: usize, backend: QuantumBackend) -> Self {
        let hardware_id = HardwareId {
            device_type: HardwareType::Quantum,
            device_index: 0,
            vendor: "SkleaRS".to_string(),
            model: format!("Q{}", num_qubits),
        };

        let connectivity = ConnectivityGraph {
            num_qubits,
            edges: Self::generate_linear_connectivity(num_qubits),
            topology: TopologyType::Linear,
        };

        let capabilities = QuantumCapabilities {
            base: HardwareCapabilities {
                compute_units: num_qubits as u32,
                memory_gb: 0.001, // Quantum systems need minimal classical memory
                peak_performance_ops: 2.0_f64.powi(num_qubits as i32), // 2^n state space
                supported_precisions: vec![Precision::Float64],
                supports_sparsity: false,
                supports_quantization: false,
                supports_dynamic_shapes: false,
                custom_features: HashMap::new(),
            },
            num_qubits,
            connectivity,
            gate_fidelity: match backend {
                QuantumBackend::Simulator => 1.0,
                QuantumBackend::NoisySimulator => 0.99,
                QuantumBackend::Hardware => 0.995,
                QuantumBackend::Cloud => 0.998,
            },
            measurement_fidelity: match backend {
                QuantumBackend::Simulator => 1.0,
                QuantumBackend::NoisySimulator => 0.95,
                QuantumBackend::Hardware => 0.97,
                QuantumBackend::Cloud => 0.98,
            },
            t1_coherence_us: 100.0,
            t2_coherence_us: 50.0,
            supported_gates: vec![
                QuantumGate::Hadamard,
                QuantumGate::PauliX,
                QuantumGate::PauliY,
                QuantumGate::PauliZ,
                QuantumGate::RX,
                QuantumGate::RY,
                QuantumGate::RZ,
                QuantumGate::CNOT,
                QuantumGate::CZ,
                QuantumGate::Measure,
            ],
            supports_mid_circuit_measurement: matches!(
                backend,
                QuantumBackend::Simulator | QuantumBackend::NoisySimulator
            ),
        };

        Self {
            hardware_id,
            capabilities,
            circuits: HashMap::new(),
            backend,
        }
    }

    /// Generate linear connectivity (nearest-neighbor)
    fn generate_linear_connectivity(num_qubits: usize) -> Vec<(usize, usize)> {
        (0..num_qubits.saturating_sub(1))
            .map(|i| (i, i + 1))
            .collect()
    }

    /// Add a circuit
    pub fn add_circuit(&mut self, circuit: QuantumCircuit) {
        self.circuits.insert(circuit.name.clone(), circuit);
    }

    /// Create a variational quantum circuit (for QML)
    pub fn create_variational_circuit(&self, name: String, num_layers: usize) -> QuantumCircuit {
        let mut gates = Vec::new();
        let num_qubits = self.capabilities.num_qubits;

        for _layer in 0..num_layers {
            // Rotation layer
            for qubit in 0..num_qubits {
                gates.push(QuantumGateOp {
                    gate: QuantumGate::RY,
                    qubits: vec![qubit],
                    parameters: vec![0.0], // Will be trained
                    control: None,
                });
            }

            // Entanglement layer
            for qubit in 0..num_qubits - 1 {
                gates.push(QuantumGateOp {
                    gate: QuantumGate::CNOT,
                    qubits: vec![qubit, qubit + 1],
                    parameters: vec![],
                    control: None,
                });
            }
        }

        // Measurement
        for qubit in 0..num_qubits {
            gates.push(QuantumGateOp {
                gate: QuantumGate::Measure,
                qubits: vec![qubit],
                parameters: vec![],
                control: None,
            });
        }

        QuantumCircuit {
            name,
            num_qubits,
            num_classical_bits: num_qubits,
            depth: num_layers * 2,
            gates,
        }
    }

    /// Execute a quantum circuit
    pub fn execute_circuit(&self, circuit_name: &str, shots: usize) -> Result<QuantumMeasurement> {
        let circuit = self.circuits.get(circuit_name).ok_or_else(|| {
            SklearsError::InvalidOperation(format!("Circuit {} not found", circuit_name))
        })?;

        // Simulate execution (in real implementation, would execute on quantum backend)
        let num_outcomes = 2_usize.pow(circuit.num_classical_bits as u32).min(shots);
        let mut counts = HashMap::new();

        // Generate simulated outcomes
        for i in 0..num_outcomes {
            let bitstring = format!("{:0width$b}", i, width = circuit.num_classical_bits);
            counts.insert(bitstring.clone(), shots / num_outcomes);
        }

        let outcomes: Vec<String> = counts.keys().cloned().collect();

        Ok(QuantumMeasurement {
            outcomes,
            counts,
            total_shots: shots,
        })
    }

    /// Calculate quantum kernel between two data points
    pub fn quantum_kernel(&self, x1: &[f64], x2: &[f64]) -> Result<f64> {
        if x1.len() != x2.len() {
            return Err(SklearsError::InvalidInput(
                "Input vectors must have same length".to_string(),
            ));
        }

        // Simplified quantum kernel computation
        // In real implementation, this would encode data in quantum states
        let inner_product: f64 = x1.iter().zip(x2.iter()).map(|(a, b)| a * b).sum();
        Ok(inner_product.cos().abs())
    }
}

// ============================================================================
// Neuromorphic Computing Implementation
// ============================================================================

/// Neuromorphic computing accelerator
///
/// Provides support for brain-inspired spiking neural networks with
/// event-driven processing and ultra-low power consumption.
#[derive(Debug)]
pub struct NeuromorphicAccelerator {
    /// Hardware identification
    pub hardware_id: HardwareId,
    /// Neuromorphic capabilities
    pub capabilities: NeuromorphicCapabilities,
    /// Spiking neural networks
    pub networks: HashMap<String, SpikingNeuralNetwork>,
    /// Event processing config
    pub config: NeuromorphicConfig,
}

/// Neuromorphic computing capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuromorphicCapabilities {
    /// Base hardware capabilities
    pub base: HardwareCapabilities,
    /// Number of neurons
    pub num_neurons: usize,
    /// Number of synapses
    pub num_synapses: usize,
    /// Event processing rate (events/second)
    pub event_rate_eps: f64,
    /// Power consumption in watts
    pub power_consumption_watts: f64,
    /// Supports online learning
    pub supports_online_learning: bool,
    /// Neuron model types supported
    pub supported_neuron_models: Vec<NeuronModel>,
    /// Plasticity rules supported
    pub supported_plasticity: Vec<PlasticityRule>,
}

/// Neuron model types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NeuronModel {
    /// Leaky Integrate-and-Fire
    LIF,
    /// Izhikevich model
    Izhikevich,
    /// Hodgkin-Huxley model
    HodgkinHuxley,
    /// Adaptive Exponential Integrate-and-Fire
    AdEx,
}

/// Synaptic plasticity rules
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PlasticityRule {
    /// Spike-Timing Dependent Plasticity
    STDP,
    /// Triplet STDP
    TripletSTDP,
    /// Homeostatic plasticity
    Homeostatic,
    /// Reward-modulated STDP
    RewardModulated,
}

/// Spiking neural network configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpikingNeuralNetwork {
    /// Network name
    pub name: String,
    /// Neuron populations
    pub populations: Vec<NeuronPopulation>,
    /// Synaptic connections
    pub connections: Vec<SynapticConnection>,
    /// Network topology
    pub topology: NetworkTopology,
}

/// Neuron population
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuronPopulation {
    /// Population ID
    pub id: String,
    /// Number of neurons
    pub size: usize,
    /// Neuron model
    pub neuron_model: NeuronModel,
    /// Neuron parameters
    pub parameters: NeuronParameters,
}

/// Neuron model parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuronParameters {
    /// Membrane time constant (ms)
    pub tau_mem: f64,
    /// Resting potential (mV)
    pub v_rest: f64,
    /// Threshold potential (mV)
    pub v_threshold: f64,
    /// Reset potential (mV)
    pub v_reset: f64,
    /// Refractory period (ms)
    pub tau_refrac: f64,
}

/// Synaptic connection between populations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SynapticConnection {
    /// Source population
    pub source: String,
    /// Target population
    pub target: String,
    /// Connection weights
    pub weights: Vec<f64>,
    /// Connection delays (ms)
    pub delays: Vec<f64>,
    /// Plasticity rule
    pub plasticity: Option<PlasticityRule>,
}

/// Network topology type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NetworkTopology {
    /// Feedforward
    Feedforward,
    /// Recurrent
    Recurrent,
    /// Convolutional
    Convolutional,
    /// Reservoir (liquid state machine)
    Reservoir,
}

/// Neuromorphic configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuromorphicConfig {
    /// Time step in milliseconds
    pub time_step_ms: f64,
    /// Simulation duration in milliseconds
    pub simulation_duration_ms: f64,
    /// Enable spike recording
    pub record_spikes: bool,
    /// Enable voltage recording
    pub record_voltage: bool,
    /// Event-driven processing
    pub event_driven: bool,
}

impl Default for NeuromorphicConfig {
    fn default() -> Self {
        Self {
            time_step_ms: 1.0,
            simulation_duration_ms: 1000.0,
            record_spikes: true,
            record_voltage: false,
            event_driven: true,
        }
    }
}

/// Spike event
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SpikeEvent {
    /// Neuron ID
    pub neuron_id: usize,
    /// Spike time (ms)
    pub time_ms: f64,
}

/// Simulation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuromorphicResult {
    /// Spike trains
    pub spike_trains: Vec<Vec<SpikeEvent>>,
    /// Total spikes
    pub total_spikes: usize,
    /// Firing rates (Hz)
    pub firing_rates: Vec<f64>,
    /// Energy consumed (joules)
    pub energy_consumed: f64,
}

impl NeuromorphicAccelerator {
    /// Create a new neuromorphic accelerator
    pub fn new(num_neurons: usize, num_synapses: usize) -> Self {
        let hardware_id = HardwareId {
            device_type: HardwareType::Neuromorphic,
            device_index: 0,
            vendor: "SkleaRS".to_string(),
            model: format!("N{}", num_neurons),
        };

        let capabilities = NeuromorphicCapabilities {
            base: HardwareCapabilities {
                compute_units: num_neurons as u32,
                memory_gb: (num_synapses * 8) as f64 / 1e9, // 8 bytes per synapse
                peak_performance_ops: num_neurons as f64 * 1000.0, // Events per second
                supported_precisions: vec![Precision::Float32, Precision::Int16],
                supports_sparsity: true, // Event-driven is inherently sparse
                supports_quantization: true,
                supports_dynamic_shapes: true,
                custom_features: HashMap::new(),
            },
            num_neurons,
            num_synapses,
            event_rate_eps: num_neurons as f64 * 1000.0,
            power_consumption_watts: (num_neurons as f64 * 1e-6), // Ultra-low power
            supports_online_learning: true,
            supported_neuron_models: vec![
                NeuronModel::LIF,
                NeuronModel::Izhikevich,
                NeuronModel::AdEx,
            ],
            supported_plasticity: vec![
                PlasticityRule::STDP,
                PlasticityRule::TripletSTDP,
                PlasticityRule::Homeostatic,
            ],
        };

        Self {
            hardware_id,
            capabilities,
            networks: HashMap::new(),
            config: NeuromorphicConfig::default(),
        }
    }

    /// Add a spiking neural network
    pub fn add_network(&mut self, network: SpikingNeuralNetwork) {
        self.networks.insert(network.name.clone(), network);
    }

    /// Create a simple feedforward SNN
    pub fn create_feedforward_snn(
        &self,
        name: String,
        layer_sizes: &[usize],
    ) -> SpikingNeuralNetwork {
        let mut populations = Vec::new();
        let mut connections = Vec::new();

        // Create populations
        for (i, &size) in layer_sizes.iter().enumerate() {
            populations.push(NeuronPopulation {
                id: format!("layer_{}", i),
                size,
                neuron_model: NeuronModel::LIF,
                parameters: NeuronParameters {
                    tau_mem: 20.0,
                    v_rest: -70.0,
                    v_threshold: -50.0,
                    v_reset: -70.0,
                    tau_refrac: 2.0,
                },
            });
        }

        // Create connections between consecutive layers
        for i in 0..layer_sizes.len() - 1 {
            let num_weights = layer_sizes[i] * layer_sizes[i + 1];
            connections.push(SynapticConnection {
                source: format!("layer_{}", i),
                target: format!("layer_{}", i + 1),
                weights: vec![0.1; num_weights], // Initial weights
                delays: vec![1.0; num_weights],  // 1ms delay
                plasticity: Some(PlasticityRule::STDP),
            });
        }

        SpikingNeuralNetwork {
            name,
            populations,
            connections,
            topology: NetworkTopology::Feedforward,
        }
    }

    /// Simulate a network
    pub fn simulate(
        &self,
        network_name: &str,
        input_spikes: &[SpikeEvent],
    ) -> Result<NeuromorphicResult> {
        let network = self.networks.get(network_name).ok_or_else(|| {
            SklearsError::InvalidOperation(format!("Network {} not found", network_name))
        })?;

        // Simplified simulation (in real implementation, would run full SNN simulation)
        let total_neurons: usize = network.populations.iter().map(|p| p.size).sum();
        let mut spike_trains = vec![Vec::new(); total_neurons];

        // Propagate input spikes (simplified)
        for event in input_spikes {
            spike_trains[event.neuron_id % total_neurons].push(event.clone());
        }

        let total_spikes = input_spikes.len();
        let firing_rates = spike_trains
            .iter()
            .map(|train| (train.len() as f64 / self.config.simulation_duration_ms) * 1000.0)
            .collect();

        let energy_consumed = (self.capabilities.power_consumption_watts
            * self.config.simulation_duration_ms
            / 1000.0)
            * total_spikes as f64;

        Ok(NeuromorphicResult {
            spike_trains,
            total_spikes,
            firing_rates,
            energy_consumed,
        })
    }
}

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

    #[test]
    fn test_tpu_creation() {
        let tpu = TPUAccelerator::new(0);
        assert_eq!(tpu.hardware_id.device_type, HardwareType::TPU);
        assert_eq!(tpu.capabilities.num_cores, 2);
    }

    #[test]
    fn test_tpu_compile_graph() {
        let mut tpu = TPUAccelerator::new(0);

        let operations = vec![TPUOperation::MatMul {
            m: 128,
            n: 128,
            k: 128,
            precision: Precision::BFloat16,
        }];

        let graph_id = tpu
            .compile_graph(operations)
            .expect("compile_graph should succeed");
        assert!(tpu.compilation_cache.contains_key(&graph_id));
    }

    #[test]
    fn test_tpu_execute_graph() {
        let mut tpu = TPUAccelerator::new(0);

        let operations = vec![TPUOperation::MatMul {
            m: 10,
            n: 10,
            k: 10,
            precision: Precision::Float32,
        }];

        let graph_id = tpu
            .compile_graph(operations)
            .expect("compile_graph should succeed");
        let inputs = vec![1.0; 100];
        let outputs = tpu
            .execute_graph(&graph_id, &inputs)
            .expect("execute_graph should succeed");

        assert_eq!(outputs.len(), 100);
    }

    #[test]
    fn test_tpu_performance_estimate() {
        let tpu = TPUAccelerator::new(0);

        let op = TPUOperation::MatMul {
            m: 1024,
            n: 1024,
            k: 1024,
            precision: Precision::BFloat16,
        };

        let estimate = tpu.estimate_performance(&op);
        assert!(estimate.execution_time_ms > 0);
        assert!(estimate.utilization > 0.0);
    }

    #[test]
    fn test_fpga_creation() {
        let fpga = FPGAAccelerator::new(0);
        assert_eq!(fpga.hardware_id.device_type, HardwareType::FPGA);
        assert!(fpga.capabilities.logic_elements > 0);
    }

    #[test]
    fn test_fpga_configure_pipeline() {
        let mut fpga = FPGAAccelerator::new(0);

        let pipeline = FPGAPipeline {
            name: "matmul_pipeline".to_string(),
            stages: vec![PipelineStage {
                name: "multiply".to_string(),
                operation: "matmul".to_string(),
                resource_usage: ResourceUsage {
                    logic_elements: 10000,
                    dsp_blocks: 100,
                    block_ram_kb: 1000,
                },
            }],
            throughput: 1e9,
            latency_cycles: 10,
        };

        fpga.configure_pipeline(pipeline)
            .expect("configure_pipeline should succeed");
        assert_eq!(fpga.pipelines.len(), 1);
    }

    #[test]
    fn test_fpga_excessive_resources() {
        let mut fpga = FPGAAccelerator::new(0);

        let pipeline = FPGAPipeline {
            name: "too_large".to_string(),
            stages: vec![PipelineStage {
                name: "huge_op".to_string(),
                operation: "matmul".to_string(),
                resource_usage: ResourceUsage {
                    logic_elements: 999999999, // Way too much
                    dsp_blocks: 100,
                    block_ram_kb: 1000,
                },
            }],
            throughput: 1e9,
            latency_cycles: 10,
        };

        let result = fpga.configure_pipeline(pipeline);
        assert!(result.is_err());
    }

    #[test]
    fn test_tensor_layout() {
        assert_ne!(TensorLayout::RowMajor, TensorLayout::ColumnMajor);
        assert_eq!(TensorLayout::RowMajor, TensorLayout::RowMajor);
    }

    #[test]
    fn test_element_wise_op() {
        assert_ne!(ElementWiseOp::Add, ElementWiseOp::Multiply);
        assert_eq!(ElementWiseOp::ReLU, ElementWiseOp::ReLU);
    }

    #[test]
    fn test_memory_type() {
        assert_ne!(MemoryType::HBM, MemoryType::ChipMemory);
        assert_eq!(MemoryType::HostMemory, MemoryType::HostMemory);
    }

    // ============================================================================
    // Quantum Computing Tests
    // ============================================================================

    #[test]
    fn test_quantum_accelerator_creation() {
        let quantum = QuantumAccelerator::new(5, QuantumBackend::Simulator);
        assert_eq!(quantum.hardware_id.device_type, HardwareType::Quantum);
        assert_eq!(quantum.capabilities.num_qubits, 5);
        assert_eq!(quantum.backend, QuantumBackend::Simulator);
    }

    #[test]
    fn test_quantum_gate_fidelity() {
        let sim = QuantumAccelerator::new(5, QuantumBackend::Simulator);
        let noisy_sim = QuantumAccelerator::new(5, QuantumBackend::NoisySimulator);
        let hardware = QuantumAccelerator::new(5, QuantumBackend::Hardware);

        assert_eq!(sim.capabilities.gate_fidelity, 1.0);
        assert!(noisy_sim.capabilities.gate_fidelity < 1.0);
        assert!(hardware.capabilities.gate_fidelity < 1.0);
    }

    #[test]
    fn test_quantum_connectivity() {
        let quantum = QuantumAccelerator::new(4, QuantumBackend::Simulator);

        assert_eq!(quantum.capabilities.connectivity.num_qubits, 4);
        assert_eq!(
            quantum.capabilities.connectivity.topology,
            TopologyType::Linear
        );
        // Linear topology with 4 qubits should have 3 edges
        assert_eq!(quantum.capabilities.connectivity.edges.len(), 3);
    }

    #[test]
    fn test_quantum_variational_circuit() {
        let quantum = QuantumAccelerator::new(3, QuantumBackend::Simulator);
        let circuit = quantum.create_variational_circuit("vqc".to_string(), 2);

        assert_eq!(circuit.name, "vqc");
        assert_eq!(circuit.num_qubits, 3);
        assert_eq!(circuit.num_classical_bits, 3);
        assert_eq!(circuit.depth, 4); // 2 layers * 2 (rotation + entanglement)
        assert!(!circuit.gates.is_empty());
    }

    #[test]
    fn test_quantum_add_circuit() {
        let mut quantum = QuantumAccelerator::new(2, QuantumBackend::Simulator);

        let circuit = QuantumCircuit {
            name: "test_circuit".to_string(),
            num_qubits: 2,
            num_classical_bits: 2,
            gates: vec![],
            depth: 1,
        };

        quantum.add_circuit(circuit);
        assert_eq!(quantum.circuits.len(), 1);
        assert!(quantum.circuits.contains_key("test_circuit"));
    }

    #[test]
    fn test_quantum_execute_circuit() {
        let mut quantum = QuantumAccelerator::new(2, QuantumBackend::Simulator);

        let circuit = quantum.create_variational_circuit("test".to_string(), 1);
        quantum.add_circuit(circuit);

        let measurement = quantum
            .execute_circuit("test", 1000)
            .expect("execute_circuit should succeed");

        assert_eq!(measurement.total_shots, 1000);
        assert!(!measurement.outcomes.is_empty());
        assert!(!measurement.counts.is_empty());
    }

    #[test]
    fn test_quantum_kernel() {
        let quantum = QuantumAccelerator::new(4, QuantumBackend::Simulator);

        let x1 = vec![1.0, 0.0, 0.0, 1.0];
        let x2 = vec![0.0, 1.0, 1.0, 0.0];

        let kernel_value = quantum
            .quantum_kernel(&x1, &x2)
            .expect("quantum_kernel should succeed");
        assert!((0.0..=1.0).contains(&kernel_value));
    }

    #[test]
    fn test_quantum_kernel_mismatch() {
        let quantum = QuantumAccelerator::new(4, QuantumBackend::Simulator);

        let x1 = vec![1.0, 0.0];
        let x2 = vec![0.0, 1.0, 1.0];

        let result = quantum.quantum_kernel(&x1, &x2);
        assert!(result.is_err());
    }

    #[test]
    fn test_quantum_gate_types() {
        assert_eq!(QuantumGate::Hadamard, QuantumGate::Hadamard);
        assert_ne!(QuantumGate::PauliX, QuantumGate::PauliY);
        assert_ne!(QuantumGate::CNOT, QuantumGate::CZ);
    }

    #[test]
    fn test_quantum_backend_types() {
        assert_eq!(QuantumBackend::Simulator, QuantumBackend::Simulator);
        assert_ne!(QuantumBackend::Hardware, QuantumBackend::Cloud);
    }

    #[test]
    fn test_topology_types() {
        assert_eq!(TopologyType::Linear, TopologyType::Linear);
        assert_ne!(TopologyType::Grid2D, TopologyType::HeavyHex);
        assert_ne!(TopologyType::AllToAll, TopologyType::Custom);
    }

    #[test]
    fn test_quantum_mid_circuit_measurement() {
        let sim = QuantumAccelerator::new(4, QuantumBackend::Simulator);
        let hardware = QuantumAccelerator::new(4, QuantumBackend::Hardware);

        assert!(sim.capabilities.supports_mid_circuit_measurement);
        assert!(!hardware.capabilities.supports_mid_circuit_measurement);
    }

    // ============================================================================
    // Neuromorphic Computing Tests
    // ============================================================================

    #[test]
    fn test_neuromorphic_accelerator_creation() {
        let neuro = NeuromorphicAccelerator::new(1000, 10000);
        assert_eq!(neuro.hardware_id.device_type, HardwareType::Neuromorphic);
        assert_eq!(neuro.capabilities.num_neurons, 1000);
        assert_eq!(neuro.capabilities.num_synapses, 10000);
    }

    #[test]
    fn test_neuromorphic_ultra_low_power() {
        let neuro = NeuromorphicAccelerator::new(10000, 100000);

        // Neuromorphic should have very low power consumption
        assert!(neuro.capabilities.power_consumption_watts < 0.1);
        // Power should scale with neurons
        assert!((neuro.capabilities.power_consumption_watts - 0.01).abs() < 0.01);
    }

    #[test]
    fn test_neuromorphic_sparsity_support() {
        let neuro = NeuromorphicAccelerator::new(1000, 10000);

        // Event-driven processing means inherent sparsity support
        assert!(neuro.capabilities.base.supports_sparsity);
        assert!(neuro.capabilities.supports_online_learning);
    }

    #[test]
    fn test_neuromorphic_create_feedforward_snn() {
        let neuro = NeuromorphicAccelerator::new(1000, 10000);
        let snn = neuro.create_feedforward_snn("test_snn".to_string(), &[10, 20, 10]);

        assert_eq!(snn.name, "test_snn");
        assert_eq!(snn.populations.len(), 3);
        assert_eq!(snn.connections.len(), 2);
        assert_eq!(snn.topology, NetworkTopology::Feedforward);
    }

    #[test]
    fn test_neuromorphic_population_sizes() {
        let neuro = NeuromorphicAccelerator::new(1000, 10000);
        let snn = neuro.create_feedforward_snn("test".to_string(), &[5, 15, 10]);

        assert_eq!(snn.populations[0].size, 5);
        assert_eq!(snn.populations[1].size, 15);
        assert_eq!(snn.populations[2].size, 10);
    }

    #[test]
    fn test_neuromorphic_add_network() {
        let mut neuro = NeuromorphicAccelerator::new(1000, 10000);
        let snn = neuro.create_feedforward_snn("my_network".to_string(), &[10, 20]);

        neuro.add_network(snn);
        assert_eq!(neuro.networks.len(), 1);
        assert!(neuro.networks.contains_key("my_network"));
    }

    #[test]
    fn test_neuromorphic_simulate() {
        let mut neuro = NeuromorphicAccelerator::new(100, 1000);
        let snn = neuro.create_feedforward_snn("test".to_string(), &[10, 10]);
        neuro.add_network(snn);

        let input_spikes = vec![
            SpikeEvent {
                neuron_id: 0,
                time_ms: 1.0,
            },
            SpikeEvent {
                neuron_id: 1,
                time_ms: 2.0,
            },
        ];

        let result = neuro
            .simulate("test", &input_spikes)
            .expect("simulate should succeed");

        assert_eq!(result.total_spikes, 2);
        assert_eq!(result.spike_trains.len(), 20); // 10 + 10 neurons
        assert_eq!(result.firing_rates.len(), 20);
    }

    #[test]
    fn test_neuromorphic_energy_consumption() {
        let mut neuro = NeuromorphicAccelerator::new(100, 1000);
        let snn = neuro.create_feedforward_snn("test".to_string(), &[5, 5]);
        neuro.add_network(snn);

        let input_spikes = vec![SpikeEvent {
            neuron_id: 0,
            time_ms: 1.0,
        }];

        let result = neuro
            .simulate("test", &input_spikes)
            .expect("simulate should succeed");

        // Energy consumption should be very low
        assert!(result.energy_consumed < 0.001);
        assert!(result.energy_consumed > 0.0);
    }

    #[test]
    fn test_neuron_model_types() {
        assert_eq!(NeuronModel::LIF, NeuronModel::LIF);
        assert_ne!(NeuronModel::Izhikevich, NeuronModel::HodgkinHuxley);
        assert_ne!(NeuronModel::AdEx, NeuronModel::LIF);
    }

    #[test]
    fn test_plasticity_rules() {
        assert_eq!(PlasticityRule::STDP, PlasticityRule::STDP);
        assert_ne!(PlasticityRule::TripletSTDP, PlasticityRule::Homeostatic);
        assert_ne!(PlasticityRule::RewardModulated, PlasticityRule::STDP);
    }

    #[test]
    fn test_network_topology_types() {
        assert_eq!(NetworkTopology::Feedforward, NetworkTopology::Feedforward);
        assert_ne!(NetworkTopology::Recurrent, NetworkTopology::Convolutional);
        assert_ne!(NetworkTopology::Reservoir, NetworkTopology::Feedforward);
    }

    #[test]
    fn test_neuromorphic_config_default() {
        let config = NeuromorphicConfig::default();

        assert_eq!(config.time_step_ms, 1.0);
        assert_eq!(config.simulation_duration_ms, 1000.0);
        assert!(config.record_spikes);
        assert!(!config.record_voltage);
        assert!(config.event_driven);
    }

    #[test]
    fn test_neuron_parameters() {
        let params = NeuronParameters {
            tau_mem: 20.0,
            v_rest: -70.0,
            v_threshold: -50.0,
            v_reset: -70.0,
            tau_refrac: 2.0,
        };

        assert_eq!(params.tau_mem, 20.0);
        assert_eq!(params.v_threshold, -50.0);
        assert!(params.v_threshold > params.v_reset);
    }

    #[test]
    fn test_spike_event_creation() {
        let spike = SpikeEvent {
            neuron_id: 42,
            time_ms: 15.5,
        };

        assert_eq!(spike.neuron_id, 42);
        assert_eq!(spike.time_ms, 15.5);
    }

    #[test]
    fn test_neuromorphic_supported_models() {
        let neuro = NeuromorphicAccelerator::new(100, 1000);

        assert!(neuro
            .capabilities
            .supported_neuron_models
            .contains(&NeuronModel::LIF));
        assert!(neuro
            .capabilities
            .supported_neuron_models
            .contains(&NeuronModel::Izhikevich));
    }

    #[test]
    fn test_neuromorphic_supported_plasticity() {
        let neuro = NeuromorphicAccelerator::new(100, 1000);

        assert!(neuro
            .capabilities
            .supported_plasticity
            .contains(&PlasticityRule::STDP));
        assert!(neuro
            .capabilities
            .supported_plasticity
            .contains(&PlasticityRule::Homeostatic));
    }
}
