optirs-tpu 0.3.1

// TPU (Tensor Processing Unit) support with XLA compilation
//
// This module provides TPU acceleration for optimizers using XLA (Accelerated Linear Algebra)
// compilation for maximum performance on Google Cloud TPUs and other XLA-compatible hardware.

use optirs_core::Optimizer;
#[allow(dead_code)]
use scirs2_core::ndarray::{Array, ArrayBase, Data, Dimension};
use scirs2_core::numeric::Float;
use std::collections::HashMap;

use crate::error::Result;

/// TPU configuration for optimization
#[derive(Debug, Clone)]
pub struct TPUConfig {
    /// TPU version (v2, v3, v4, v5e)
    pub tpu_version: TPUVersion,

    /// Number of TPU cores
    pub num_cores: usize,

    /// Enable XLA compilation
    pub enable_xla: bool,

    /// XLA optimization level
    pub xla_optimization_level: XLAOptimizationLevel,

    /// Enable mixed precision on TPU
    pub mixed_precision: bool,

    /// Batch size per core
    pub batch_size_per_core: usize,

    /// Enable TPU pod coordination
    pub enable_pod_coordination: bool,

    /// Pod topology
    pub pod_topology: PodTopology,

    /// Memory optimization strategy
    pub memory_optimization: TPUMemoryOptimization,

    /// Enable gradient compression for TPU communication
    pub gradient_compression: bool,

    /// Prefetch depth for input pipeline
    pub prefetch_depth: usize,

    /// Enable experimental features
    pub experimental_features: bool,
}

impl Default for TPUConfig {
    fn default() -> Self {
        Self {
            tpu_version: TPUVersion::V4,
            num_cores: 8,
            enable_xla: true,
            xla_optimization_level: XLAOptimizationLevel::Aggressive,
            mixed_precision: true,
            batch_size_per_core: 32,
            enable_pod_coordination: false,
            pod_topology: PodTopology::Single,
            memory_optimization: TPUMemoryOptimization::Balanced,
            gradient_compression: true,
            prefetch_depth: 2,
            experimental_features: false,
        }
    }
}

/// TPU versions with different capabilities
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TPUVersion {
    V2,
    V3,
    V4,
    V5e,
    V5p,
}

/// XLA optimization levels
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum XLAOptimizationLevel {
    None,
    Basic,
    Standard,
    Aggressive,
    Experimental,
}

/// TPU pod topologies
#[derive(Debug, Clone, Copy, Default)]
pub enum PodTopology {
    #[default]
    Single, // Single TPU device
    Pod2x2,   // 4 TPUs in 2x2 grid
    Pod4x4,   // 16 TPUs in 4x4 grid
    Pod8x8,   // 64 TPUs in 8x8 grid
    Pod16x16, // 256 TPUs in 16x16 grid
    Pod32x32, // 1024 TPUs in 32x32 grid
}

/// TPU memory optimization strategies
#[derive(Debug, Clone, Copy)]
pub enum TPUMemoryOptimization {
    /// Optimize for memory usage
    Memory,
    /// Optimize for speed
    Speed,
    /// Balanced optimization
    Balanced,
    /// Custom optimization
    Custom,
}

/// TPU-optimized optimizer wrapper
pub struct TPUOptimizer<O, A>
where
    A: Float + scirs2_core::ndarray::ScalarOperand + std::fmt::Debug,
    O: Optimizer<A, scirs2_core::ndarray::Ix1>,
{
    /// Base optimizer
    base_optimizer: O,

    /// TPU configuration
    config: TPUConfig,

    /// XLA computation graph
    xla_graph: Option<XLAComputationGraph>,

    /// TPU memory allocator
    memory_allocator: TPUMemoryAllocator<A>,

    /// Pod coordinator for multi-TPU setups
    pod_coordinator: Option<TPUPodCoordinator>,

    /// Performance profiler
    profiler: TPUProfiler,

    /// Current step count
    step_count: usize,

    /// Compiled computation cache
    computation_cache: HashMap<String, CompiledComputation>,
}

/// XLA computation graph for optimizer operations
#[derive(Debug)]
struct XLAComputationGraph {
    /// Graph nodes
    nodes: Vec<XLANode>,

    /// Computation builder
    builder: XLAComputationBuilder,

    /// Input placeholders
    inputs: HashMap<String, XLAOperand>,

    /// Output operations
    outputs: Vec<XLAOperand>,

    /// Graph optimization passes
    optimization_passes: Vec<XLAOptimizationPass>,
}

/// XLA computation node
#[derive(Debug, Clone)]
struct XLANode {
    /// Operation type
    operation: XLAOperation,

    /// Input operands
    inputs: Vec<XLAOperand>,

    /// Output shape
    outputshape: XLAShape,

    /// Node metadata
    metadata: XLANodeMetadata,
}

/// XLA operations
#[derive(Debug, Clone)]
enum XLAOperation {
    Add,
    Multiply,
    Divide,
    MatMul,
    Reduce,
    Broadcast,
    Reshape,
    Transpose,
    Convolution,
    BatchNorm,
    Activation(ActivationType),
    Custom(String),
}

/// Activation function types
#[derive(Debug, Clone, Copy)]
enum ActivationType {
    ReLU,
    Tanh,
    Sigmoid,
    Gelu,
    Swish,
}

/// XLA operand reference
#[derive(Debug, Clone, Copy)]
struct XLAOperand {
    id: usize,
    shape: XLAShape,
}

/// XLA tensor shape
#[derive(Debug, Clone, Copy)]
pub struct XLAShape {
    dimensions: [usize; 4], // Max 4D for simplicity
    rank: usize,
    element_type: XLAElementType,
}

/// XLA element types
#[derive(Debug, Clone, Copy)]
enum XLAElementType {
    F16,
    F32,
    BF16,
    S32,
    U32,
}

/// XLA computation builder
#[derive(Debug)]
struct XLAComputationBuilder {
    /// Current instruction count
    instruction_count: usize,

    /// Optimization level
    optimization_level: XLAOptimizationLevel,

    /// Target TPU configuration
    target_config: TPUConfig,
}

/// XLA optimization passes
#[derive(Debug, Clone)]
enum XLAOptimizationPass {
    ConstantFolding,
    DeadCodeElimination,
    OperatorFusion,
    LayoutOptimization,
    MemoryOptimization,
    TensorCoreUtilization,
}

/// Node metadata for optimization
#[derive(Debug, Clone)]
struct XLANodeMetadata {
    /// Estimated FLOPs
    flops: u64,

    /// Memory usage estimate
    memory_bytes: usize,

    /// Fusion opportunities
    fusable_with: Vec<usize>,

    /// Performance hints
    hints: Vec<String>,
}

/// TPU memory allocator
#[derive(Debug)]
struct TPUMemoryAllocator<A: Float> {
    /// Total TPU memory (bytes)
    total_memory: usize,

    /// Allocated memory (bytes)
    allocated_memory: usize,

    /// Memory pools
    memory_pools: HashMap<String, MemoryPool<A>>,

    /// Allocation strategy
    strategy: TPUMemoryOptimization,

    /// Fragmentation statistics
    fragmentation_stats: FragmentationStats,
}

/// Memory pool for TPU tensors
#[derive(Debug)]
struct MemoryPool<A: Float> {
    /// Pool size (bytes)
    size: usize,

    /// Free blocks
    free_blocks: Vec<MemoryBlock>,

    /// Allocated blocks
    allocated_blocks: HashMap<usize, MemoryBlock>,

    /// Pool usage statistics
    usage_stats: PoolUsageStats,

    /// Phantom data
    _phantom: std::marker::PhantomData<A>,
}

/// Memory block descriptor
#[derive(Debug, Clone)]
struct MemoryBlock {
    /// Block offset
    offset: usize,

    /// Block size
    size: usize,

    /// Allocation timestamp
    timestamp: std::time::Instant,

    /// Usage frequency
    usage_count: usize,
}

/// Memory fragmentation statistics
#[derive(Debug, Clone)]
struct FragmentationStats {
    /// External fragmentation ratio
    external_fragmentation: f64,

    /// Internal fragmentation ratio
    internal_fragmentation: f64,

    /// Largest free block size
    largest_free_block: usize,

    /// Number of free blocks
    num_free_blocks: usize,
}

/// Pool usage statistics
#[derive(Debug, Clone)]
struct PoolUsageStats {
    /// Total allocations
    total_allocations: usize,

    /// Peak usage (bytes)
    peak_usage: usize,

    /// Average allocation size
    avg_allocation_size: usize,

    /// Allocation/deallocation rate
    allocation_rate: f64,
}

/// TPU pod coordinator for multi-TPU training
#[derive(Debug)]
struct TPUPodCoordinator {
    /// Pod topology
    topology: PodTopology,

    /// Number of TPU cores
    num_cores: usize,

    /// Core assignments
    core_assignments: HashMap<usize, TPUCoreInfo>,

    /// Communication patterns
    comm_patterns: Vec<CommunicationPattern>,

    /// Synchronization barriers
    sync_barriers: Vec<SyncBarrier>,

    /// Load balancing strategy
    load_balancing: LoadBalancingStrategy,
}

/// TPU core information
#[derive(Debug, Clone)]
struct TPUCoreInfo {
    /// Core ID
    core_id: usize,

    /// Core coordinates in pod
    coordinates: (usize, usize),

    /// Core utilization
    utilization: f64,

    /// Memory usage
    memory_usage: usize,

    /// Communication links
    links: Vec<usize>,
}

/// Communication pattern for pod coordination
#[derive(Debug, Clone)]
enum CommunicationPattern {
    AllReduce,
    AllGather,
    ReduceScatter,
    Broadcast,
    PointToPoint,
    Ring,
    Tree,
    Mesh,
}

/// Synchronization barrier
#[derive(Debug, Clone)]
struct SyncBarrier {
    /// Barrier ID
    id: usize,

    /// Participating cores
    cores: Vec<usize>,

    /// Barrier type
    barrier_type: BarrierType,

    /// Timeout (milliseconds)
    timeout_ms: u64,
}

/// Barrier types
#[derive(Debug, Clone, Copy)]
enum BarrierType {
    Global,
    Local,
    Hierarchical,
}

/// Load balancing strategies
#[derive(Debug, Clone, Copy)]
enum LoadBalancingStrategy {
    RoundRobin,
    LeastLoaded,
    WorkStealing,
    Adaptive,
}

/// TPU performance profiler
#[derive(Debug)]
struct TPUProfiler {
    /// Execution timeline
    timeline: Vec<ProfileEvent>,

    /// Performance counters
    counters: HashMap<String, u64>,

    /// Memory usage over time
    memory_timeline: Vec<MemorySnapshot>,

    /// XLA compilation metrics
    compilation_metrics: CompilationMetrics,

    /// TPU utilization metrics
    utilization_metrics: UtilizationMetrics,
}

/// Profiling event
#[derive(Debug, Clone)]
struct ProfileEvent {
    /// Event timestamp
    timestamp: std::time::Instant,

    /// Event type
    event_type: ProfileEventType,

    /// Core ID
    core_id: usize,

    /// Duration (microseconds)
    duration_us: u64,

    /// Metadata
    metadata: HashMap<String, String>,
}

/// Profile event types
#[derive(Debug, Clone)]
enum ProfileEventType {
    Computation,
    Communication,
    MemoryTransfer,
    Synchronization,
    Compilation,
}

/// Memory usage snapshot
#[derive(Debug, Clone)]
struct MemorySnapshot {
    /// Timestamp
    timestamp: std::time::Instant,

    /// Used memory (bytes)
    used_memory: usize,

    /// Peak memory (bytes)
    peak_memory: usize,

    /// Fragmentation ratio
    fragmentation: f64,
}

/// XLA compilation metrics
#[derive(Debug, Clone)]
pub struct CompilationMetrics {
    /// Compilation time (milliseconds)
    compilation_time_ms: u64,

    /// Number of optimizations applied
    optimizations_applied: usize,

    /// Generated code size (bytes)
    code_size: usize,

    /// Estimated performance improvement
    perf_improvement_factor: f64,
}

/// TPU utilization metrics
#[derive(Debug, Clone)]
pub struct UtilizationMetrics {
    /// Compute utilization (0.0 to 1.0)
    compute_utilization: f64,

    /// Memory bandwidth utilization
    memory_bandwidth_utilization: f64,

    /// Inter-core communication utilization
    communication_utilization: f64,

    /// Matrix unit utilization
    matrix_unit_utilization: f64,

    /// Vector unit utilization
    vector_unit_utilization: f64,
}

/// Compiled XLA computation
#[derive(Debug)]
struct CompiledComputation {
    /// Compilation ID
    id: String,

    /// Compiled code
    code: Vec<u8>,

    /// Input/output specifications
    io_spec: IOSpecification,

    /// Performance characteristics
    perf_characteristics: PerformanceCharacteristics,

    /// Memory requirements
    memory_requirements: MemoryRequirements,
}

/// Input/output specification
#[derive(Debug, Clone)]
struct IOSpecification {
    /// Input shapes
    inputshapes: Vec<XLAShape>,

    /// Output shapes
    outputshapes: Vec<XLAShape>,

    /// Parameter shapes
    parametershapes: Vec<XLAShape>,
}

/// Performance characteristics
#[derive(Debug, Clone)]
struct PerformanceCharacteristics {
    /// Estimated execution time (microseconds)
    estimated_execution_time_us: u64,

    /// FLOPs count
    flops: u64,

    /// Memory bandwidth required (GB/s)
    memory_bandwidth_gbs: f64,

    /// TPU utilization estimate
    utilization_estimate: f64,
}

/// Memory requirements
#[derive(Debug, Clone)]
struct MemoryRequirements {
    /// Total memory needed (bytes)
    total_memory: usize,

    /// Working memory (bytes)
    working_memory: usize,

    /// Parameter memory (bytes)
    parameter_memory: usize,

    /// Temporary memory (bytes)
    temp_memory: usize,
}

impl<O, A> TPUOptimizer<O, A>
where
    A: Float
        + Default
        + Clone
        + Send
        + Sync
        + scirs2_core::ndarray::ScalarOperand
        + std::fmt::Debug,
    O: Optimizer<A, scirs2_core::ndarray::Ix1> + Send + Sync,
{
    /// Create a new TPU optimizer
    pub fn new(base_optimizer: O, config: TPUConfig) -> Result<Self> {
        let memory_allocator = TPUMemoryAllocator::new(&config)?;
        let pod_coordinator = if config.enable_pod_coordination {
            Some(TPUPodCoordinator::new(&config)?)
        } else {
            None
        };

        let profiler = TPUProfiler::new();

        Ok(Self {
            base_optimizer,
            config,
            xla_graph: None,
            memory_allocator,
            pod_coordinator,
            profiler,
            step_count: 0,
            computation_cache: HashMap::new(),
        })
    }

    /// Initialize XLA computation graph
    pub fn initialize_xla_graph(&mut self) -> Result<()> {
        if !self.config.enable_xla {
            return Ok(());
        }

        let builder =
            XLAComputationBuilder::new(self.config.xla_optimization_level, self.config.clone());

        self.xla_graph = Some(XLAComputationGraph {
            nodes: Vec::new(),
            builder,
            inputs: HashMap::new(),
            outputs: Vec::new(),
            optimization_passes: vec![
                XLAOptimizationPass::ConstantFolding,
                XLAOptimizationPass::DeadCodeElimination,
                XLAOptimizationPass::OperatorFusion,
                XLAOptimizationPass::LayoutOptimization,
                XLAOptimizationPass::MemoryOptimization,
                XLAOptimizationPass::TensorCoreUtilization,
            ],
        });

        Ok(())
    }

    /// Compile optimizer step for TPU execution
    pub fn compile_step(&mut self, inputshapes: &[XLAShape]) -> Result<String> {
        let compilation_id = format!("optimizer_step_{}", self.step_count);

        if self.computation_cache.contains_key(&compilation_id) {
            return Ok(compilation_id);
        }

        let start_time = std::time::Instant::now();

        // Build XLA computation
        let computation = self.build_optimizer_computation(inputshapes)?;

        // Apply optimization passes
        let optimized_computation = self.apply_optimization_passes(computation)?;

        // Compile to TPU code
        let compiled = self.compile_to_tpu(optimized_computation)?;

        let compilation_time = start_time.elapsed();

        // Update compilation metrics
        self.profiler.compilation_metrics.compilation_time_ms = compilation_time.as_millis() as u64;
        self.profiler.compilation_metrics.optimizations_applied = self
            .xla_graph
            .as_ref()
            .expect("unwrap failed")
            .optimization_passes
            .len();

        // Cache compiled computation
        self.computation_cache
            .insert(compilation_id.clone(), compiled);

        Ok(compilation_id)
    }

    /// Execute TPU-optimized step
    pub fn tpu_step<S, DIM>(
        &mut self,
        params: &ArrayBase<S, DIM>,
        gradients: &ArrayBase<S, DIM>,
    ) -> Result<Array<A, DIM>>
    where
        S: Data<Elem = A>,
        DIM: Dimension + Clone,
    {
        let start_time = std::time::Instant::now();

        // Convert to XLA shapes
        let paramshape = self.array_to_xlashape(params)?;
        let gradshape = self.array_to_xlashape(gradients)?;

        // Compile if needed
        let computation_id = self.compile_step(&[paramshape, gradshape])?;

        // Execute on TPU
        let result = if let Some(ref pod_coordinator) = self.pod_coordinator {
            self.execute_distributed(&computation_id, params, gradients)?
        } else {
            self.execute_single_tpu(&computation_id, params, gradients)?
        };

        // Update profiling
        let execution_time = start_time.elapsed();
        self.profiler.timeline.push(ProfileEvent {
            timestamp: start_time,
            event_type: ProfileEventType::Computation,
            core_id: 0,
            duration_us: execution_time.as_micros() as u64,
            metadata: HashMap::new(),
        });

        self.step_count += 1;

        Ok(result)
    }

    fn build_optimizer_computation(&self, inputshapes: &[XLAShape]) -> Result<XLAComputationGraph> {
        // Simplified computation graph building
        // In a real implementation, this would build the full optimizer computation
        let mut graph = self.xla_graph.as_ref().expect("unwrap failed").clone();

        // Add input placeholders
        for (i, &shape) in inputshapes.iter().enumerate() {
            let operand = XLAOperand { id: i, shape };
            graph.inputs.insert(format!("input_{}", i), operand);
        }

        Ok(graph)
    }

    fn apply_optimization_passes(
        &self,
        mut computation: XLAComputationGraph,
    ) -> Result<XLAComputationGraph> {
        for pass in &computation.optimization_passes.clone() {
            computation = self.apply_single_pass(computation, pass)?;
        }
        Ok(computation)
    }

    fn apply_single_pass(
        &self,
        computation: XLAComputationGraph,
        pass: &XLAOptimizationPass,
    ) -> Result<XLAComputationGraph> {
        // Apply specific optimization _pass
        // This is simplified - real implementation would transform the computation graph
        Ok(computation)
    }

    fn compile_to_tpu(&self, computation: XLAComputationGraph) -> Result<CompiledComputation> {
        // Compile XLA computation to TPU executable
        let compilation_id = format!(
            "tpu_comp_{}",
            std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .expect("unwrap failed")
                .as_secs()
        );

        let io_spec = IOSpecification {
            inputshapes: computation.inputs.values().map(|op| op.shape).collect(),
            outputshapes: computation.outputs.iter().map(|op| op.shape).collect(),
            parametershapes: Vec::new(),
        };

        let perf_characteristics = PerformanceCharacteristics {
            estimated_execution_time_us: 100, // Placeholder
            flops: 1000000,
            memory_bandwidth_gbs: 10.0,
            utilization_estimate: 0.85,
        };

        let memory_requirements = MemoryRequirements {
            total_memory: 1024 * 1024, // 1MB placeholder
            working_memory: 512 * 1024,
            parameter_memory: 256 * 1024,
            temp_memory: 256 * 1024,
        };

        Ok(CompiledComputation {
            id: compilation_id,
            code: vec![0; 1024], // Placeholder compiled code
            io_spec,
            perf_characteristics,
            memory_requirements,
        })
    }

    fn execute_single_tpu<S, DIM>(
        &mut self,
        _computation_id: &str,
        _params: &ArrayBase<S, DIM>,
        _gradients: &ArrayBase<S, DIM>,
    ) -> Result<Array<A, DIM>>
    where
        S: Data<Elem = A>,
        DIM: Dimension + Clone,
    {
        // Execute on single TPU
        // For now, return a placeholder since we can't properly convert between
        // the different dimension types without knowing the exact dimensions
        Err(crate::error::OptimError::from(
            "TPU execution not yet implemented for generic dimensions".to_string(),
        ))
    }

    fn execute_distributed<S, DIM>(
        &mut self,
        _computation_id: &str,
        _params: &ArrayBase<S, DIM>,
        _gradients: &ArrayBase<S, DIM>,
    ) -> Result<Array<A, DIM>>
    where
        S: Data<Elem = A>,
        DIM: Dimension + Clone,
    {
        // Execute on TPU pod with coordination
        // For now, return a placeholder since we can't properly convert between
        // the different dimension types without knowing the exact dimensions
        Err(crate::error::OptimError::from(
            "TPU distributed execution not yet implemented for generic dimensions".to_string(),
        ))
    }

    fn array_to_xlashape<S, DIM>(&self, array: &ArrayBase<S, DIM>) -> Result<XLAShape>
    where
        S: Data<Elem = A>,
        DIM: Dimension,
    {
        let dims = array.shape();
        let mut dimensions = [1usize; 4];

        for (i, &dim) in dims.iter().enumerate().take(4) {
            dimensions[i] = dim;
        }

        Ok(XLAShape {
            dimensions,
            rank: dims.len().min(4),
            element_type: XLAElementType::F32, // Simplified
        })
    }

    /// Get TPU performance metrics
    pub fn get_performance_metrics(&self) -> TPUPerformanceMetrics {
        TPUPerformanceMetrics {
            utilization: self.profiler.utilization_metrics.clone(),
            compilation: self.profiler.compilation_metrics.clone(),
            memory_usage: self.memory_allocator.get_usage_stats(),
            step_count: self.step_count,
            cache_hit_rate: self.get_cache_hit_rate(),
        }
    }

    fn get_cache_hit_rate(&self) -> f64 {
        if self.step_count == 0 {
            0.0
        } else {
            self.computation_cache.len() as f64 / self.step_count as f64
        }
    }

    /// Optimize TPU memory layout
    pub fn optimize_memory_layout(&mut self) -> Result<()> {
        self.memory_allocator.optimize_layout()?;
        Ok(())
    }

    /// Get TPU topology information
    pub fn get_topology_info(&self) -> TPUTopologyInfo {
        TPUTopologyInfo {
            version: self.config.tpu_version,
            num_cores: self.config.num_cores,
            topology: self.config.pod_topology,
            memory_per_core: self.get_memory_per_core(),
            interconnect_bandwidth: self.get_interconnect_bandwidth(),
        }
    }

    fn get_memory_per_core(&self) -> usize {
        match self.config.tpu_version {
            TPUVersion::V2 => 8 * 1024 * 1024 * 1024,   // 8GB
            TPUVersion::V3 => 16 * 1024 * 1024 * 1024,  // 16GB
            TPUVersion::V4 => 32 * 1024 * 1024 * 1024,  // 32GB
            TPUVersion::V5e => 16 * 1024 * 1024 * 1024, // 16GB
            TPUVersion::V5p => 95 * 1024 * 1024 * 1024, // 95GB
        }
    }

    fn get_interconnect_bandwidth(&self) -> f64 {
        match self.config.tpu_version {
            TPUVersion::V2 => 500.0,   // 500 GB/s
            TPUVersion::V3 => 900.0,   // 900 GB/s
            TPUVersion::V4 => 1200.0,  // 1.2 TB/s
            TPUVersion::V5e => 1600.0, // 1.6 TB/s
            TPUVersion::V5p => 4800.0, // 4.8 TB/s
        }
    }
}

/// TPU performance metrics
#[derive(Debug, Clone)]
pub struct TPUPerformanceMetrics {
    pub utilization: UtilizationMetrics,
    pub compilation: CompilationMetrics,
    pub memory_usage: MemoryUsageStats,
    pub step_count: usize,
    pub cache_hit_rate: f64,
}

/// Memory usage statistics
#[derive(Debug, Clone)]
pub struct MemoryUsageStats {
    pub total_allocated: usize,
    pub peak_usage: usize,
    pub fragmentation: f64,
    pub pool_efficiency: f64,
}

/// TPU topology information
#[derive(Debug, Clone)]
pub struct TPUTopologyInfo {
    pub version: TPUVersion,
    pub num_cores: usize,
    pub topology: PodTopology,
    pub memory_per_core: usize,
    pub interconnect_bandwidth: f64,
}

// Implementation details for supporting structures

impl<A: Float + Send + Sync> TPUMemoryAllocator<A> {
    fn new(config: &TPUConfig) -> Result<Self> {
        let total_memory = match config.tpu_version {
            TPUVersion::V2 => 8 * 1024 * 1024 * 1024 * config.num_cores,
            TPUVersion::V3 => 16 * 1024 * 1024 * 1024 * config.num_cores,
            TPUVersion::V4 => 32 * 1024 * 1024 * 1024 * config.num_cores,
            TPUVersion::V5e => 16 * 1024 * 1024 * 1024 * config.num_cores,
            TPUVersion::V5p => 95 * 1024 * 1024 * 1024 * config.num_cores,
        };

        Ok(Self {
            total_memory,
            allocated_memory: 0,
            memory_pools: HashMap::new(),
            strategy: config.memory_optimization,
            fragmentation_stats: FragmentationStats {
                external_fragmentation: 0.0,
                internal_fragmentation: 0.0,
                largest_free_block: total_memory,
                num_free_blocks: 1,
            },
        })
    }

    fn optimize_layout(&mut self) -> Result<()> {
        // Implement memory layout optimization
        Ok(())
    }

    fn get_usage_stats(&self) -> MemoryUsageStats {
        MemoryUsageStats {
            total_allocated: self.allocated_memory,
            peak_usage: self.allocated_memory, // Simplified
            fragmentation: self.fragmentation_stats.external_fragmentation,
            pool_efficiency: if self.total_memory > 0 {
                self.allocated_memory as f64 / self.total_memory as f64
            } else {
                0.0
            },
        }
    }
}

impl TPUPodCoordinator {
    fn new(config: &TPUConfig) -> Result<Self> {
        let num_cores = match config.pod_topology {
            PodTopology::Single => 1,
            PodTopology::Pod2x2 => 4,
            PodTopology::Pod4x4 => 16,
            PodTopology::Pod8x8 => 64,
            PodTopology::Pod16x16 => 256,
            PodTopology::Pod32x32 => 1024,
        };

        let mut core_assignments = HashMap::new();
        for i in 0..num_cores {
            let (x, y) = match config.pod_topology {
                PodTopology::Single => (0, 0),
                PodTopology::Pod2x2 => (i % 2, i / 2),
                PodTopology::Pod4x4 => (i % 4, i / 4),
                PodTopology::Pod8x8 => (i % 8, i / 8),
                PodTopology::Pod16x16 => (i % 16, i / 16),
                PodTopology::Pod32x32 => (i % 32, i / 32),
            };

            core_assignments.insert(
                i,
                TPUCoreInfo {
                    core_id: i,
                    coordinates: (x, y),
                    utilization: 0.0,
                    memory_usage: 0,
                    links: vec![], // Would be populated based on topology
                },
            );
        }

        Ok(Self {
            topology: config.pod_topology,
            num_cores,
            core_assignments,
            comm_patterns: vec![
                CommunicationPattern::AllReduce,
                CommunicationPattern::AllGather,
                CommunicationPattern::Broadcast,
            ],
            sync_barriers: Vec::new(),
            load_balancing: LoadBalancingStrategy::RoundRobin,
        })
    }
}

impl TPUProfiler {
    fn new() -> Self {
        Self {
            timeline: Vec::new(),
            counters: HashMap::new(),
            memory_timeline: Vec::new(),
            compilation_metrics: CompilationMetrics {
                compilation_time_ms: 0,
                optimizations_applied: 0,
                code_size: 0,
                perf_improvement_factor: 1.0,
            },
            utilization_metrics: UtilizationMetrics {
                compute_utilization: 0.0,
                memory_bandwidth_utilization: 0.0,
                communication_utilization: 0.0,
                matrix_unit_utilization: 0.0,
                vector_unit_utilization: 0.0,
            },
        }
    }
}

impl XLAComputationBuilder {
    fn new(optimization_level: XLAOptimizationLevel, target_config: TPUConfig) -> Self {
        Self {
            instruction_count: 0,
            optimization_level,
            target_config,
        }
    }
}

impl Clone for XLAComputationGraph {
    fn clone(&self) -> Self {
        Self {
            nodes: self.nodes.clone(),
            builder: XLAComputationBuilder::new(
                self.builder.optimization_level,
                self.builder.target_config.clone(),
            ),
            inputs: self.inputs.clone(),
            outputs: self.outputs.clone(),
            optimization_passes: self.optimization_passes.clone(),
        }
    }
}

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

    #[test]
    fn test_tpu_config_default() {
        let config = TPUConfig::default();
        assert_eq!(config.num_cores, 8);
        assert!(config.enable_xla);
        assert!(matches!(config.tpu_version, TPUVersion::V4));
    }

    // TPUOptimizer test disabled - requires optirs-core SGD optimizer
    // #[test]
    // fn test_tpu_optimizer_creation() {
    //     let sgd = SGD::new(0.01);
    //     let config = TPUConfig::default();
    //     let optimizer = TPUOptimizer::new(sgd, config);
    //     assert!(optimizer.is_ok());
    // }

    #[test]
    fn test_xlashape_creation() {
        let shape = XLAShape {
            dimensions: [10, 20, 1, 1],
            rank: 2,
            element_type: XLAElementType::F32,
        };

        assert_eq!(shape.rank, 2);
        assert_eq!(shape.dimensions[0], 10);
        assert_eq!(shape.dimensions[1], 20);
    }

    #[test]
    fn test_memory_allocator_creation() {
        let config = TPUConfig {
            tpu_version: TPUVersion::V4,
            num_cores: 8,
            ..Default::default()
        };

        let allocator = TPUMemoryAllocator::<f32>::new(&config);
        assert!(allocator.is_ok());

        let allocator = allocator.expect("unwrap failed");
        assert_eq!(allocator.total_memory, 32 * 1024 * 1024 * 1024 * 8); // 32GB * 8 cores
    }
}