//! Main edge training orchestration and coordination
//!
//! This module provides the main `EdgeAwareTraining` struct that coordinates
//! all edge computing functionality including device detection, optimization,
//! federated coordination, and deployment validation.

use crate::Result;
use crate::revolutionary::edge_capabilities::{
    EdgeCapabilities, EdgeCapabilityDetector, EdgeClass, NetworkClass, PowerClass, SIMDClass,
};
use crate::revolutionary::edge_deployment::{DeploymentValidation, EdgeDeploymentValidator};
use crate::revolutionary::edge_optimizations::{
    EdgeOptimizationEngine, OptimizationReport, QuantizationLevel,
};
use crate::revolutionary::federated_coordination::{
    AdaptiveAggregation, AggregationStrategy, ByzantineConfig, DeviceSelectionStrategy,
    DifferentialPrivacyConfig, EncryptionAlgorithm, EncryptionConfig, FaultToleranceConfig,
    FederatedConfig, FederatedCoordinator, KeyManagement, ProofAggregation, ProofRequirements,
    QualityMetrics, VerificationMethod,
};
use crate::revolutionary::{EdgeEnhancement, EdgeTarget};
use crate::training::ModelOutput;
use rtx_tensor::{DType, Device, Tensor};
use std::collections::HashMap;
use std::sync::{Arc, Mutex};
use std::time::Duration;
use tracing::{debug, info};

/// Transformer configuration adapted for edge capabilities
#[derive(Debug, Clone)]
pub struct EdgeTransformerConfig {
    /// Model dimension scaling factor (0.1 - 1.0)
    pub dimension_scale: f32,
    /// Number of transformer layers
    pub num_layers: u32,
    /// Number of attention heads
    pub num_heads: u32,
    /// Quantization precision
    pub precision: QuantizationLevel,
    /// Enable gradient checkpointing for memory efficiency
    pub gradient_checkpointing: bool,
    /// Enable mixed precision training
    pub mixed_precision: bool,
}

/// Comprehensive edge-aware training system
#[derive(Debug)]
pub struct EdgeAwareTraining {
    /// Detected device capabilities
    capabilities: EdgeCapabilities,
    /// Target deployment platforms
    targets: Vec<EdgeTarget>,
    /// Compute device
    device: Device,
    /// Adaptive transformer configuration
    model_config: EdgeTransformerConfig,
    /// Federated learning configuration
    federated_config: FederatedConfig,
    /// Quantization parameters per layer
    quantization_params: HashMap<String, Tensor>,
    /// Real-time performance metrics
    metrics: Arc<Mutex<HashMap<String, f64>>>,
    /// Training state
    training: bool,
    /// Optimization engine
    optimization_engine: EdgeOptimizationEngine,
    /// Deployment validator
    deployment_validator: EdgeDeploymentValidator,
    /// Federated coordinator
    federated_coordinator: Option<FederatedCoordinator>,
}

impl EdgeAwareTraining {
    /// Create a new edge-aware training system with comprehensive device detection
    pub fn new(targets: &[EdgeTarget], device: &Device) -> Result<Self> {
        info!(
            "Initializing production edge-aware training for targets: {:?}",
            targets
        );

        // Detect comprehensive device capabilities
        let capabilities = EdgeCapabilityDetector::detect_edge_capabilities()?;
        info!("Detected edge capabilities: {:?}", capabilities);

        // Create adaptive transformer configuration based on capabilities
        let model_config = Self::create_adaptive_config(&capabilities);
        info!(
            "Created adaptive model config: dimension_scale={:.2}, layers={}, precision={:?}",
            model_config.dimension_scale, model_config.num_layers, model_config.precision
        );

        // Configure federated learning parameters
        let federated_config = Self::create_federated_config(&capabilities);

        // Initialize quantization parameters based on target precision
        let mut quantization_params = HashMap::new();
        match model_config.precision {
            QuantizationLevel::INT8 => {
                quantization_params.insert(
                    "scale_factor".to_string(),
                    Tensor::scalar(127.0, DType::F32, device)?,
                );
                quantization_params.insert(
                    "zero_point".to_string(),
                    Tensor::scalar(128.0, DType::F32, device)?,
                );
            }
            QuantizationLevel::INT4 => {
                quantization_params.insert(
                    "scale_factor".to_string(),
                    Tensor::scalar(7.0, DType::F32, device)?,
                );
                quantization_params.insert(
                    "zero_point".to_string(),
                    Tensor::scalar(8.0, DType::F32, device)?,
                );
            }
            _ => {
                quantization_params.insert(
                    "scale_factor".to_string(),
                    Tensor::scalar(1.0, DType::F32, device)?,
                );
                quantization_params.insert(
                    "zero_point".to_string(),
                    Tensor::scalar(0.0, DType::F32, device)?,
                );
            }
        }

        // Initialize optimization engine
        let optimization_engine = EdgeOptimizationEngine::new(capabilities.clone(), device)?;

        // Initialize deployment validator
        let deployment_validator = EdgeDeploymentValidator::new(capabilities.clone());

        // Initialize federated coordinator
        let federated_coordinator = Some(FederatedCoordinator::new(federated_config.clone())?);

        Ok(Self {
            capabilities,
            targets: targets.to_vec(),
            device: device.clone(),
            model_config,
            federated_config,
            quantization_params,
            metrics: Arc::new(Mutex::new(HashMap::new())),
            training: false,
            optimization_engine,
            deployment_validator,
            federated_coordinator,
        })
    }

    /// Create adaptive transformer configuration based on device capabilities
    fn create_adaptive_config(capabilities: &EdgeCapabilities) -> EdgeTransformerConfig {
        match capabilities.edge_class {
            EdgeClass::HighEnd => EdgeTransformerConfig {
                dimension_scale: 1.0,
                num_layers: 12,
                num_heads: 12,
                precision: if capabilities.memory_mb > 16_384 {
                    QuantizationLevel::FP32
                } else {
                    QuantizationLevel::FP16
                },
                gradient_checkpointing: false,
                mixed_precision: true,
            },
            EdgeClass::Mid => EdgeTransformerConfig {
                dimension_scale: 0.7,
                num_layers: 8,
                num_heads: 8,
                precision: QuantizationLevel::FP16,
                gradient_checkpointing: true,
                mixed_precision: true,
            },
            EdgeClass::Low => EdgeTransformerConfig {
                dimension_scale: 0.3,
                num_layers: 4,
                num_heads: 4,
                precision: QuantizationLevel::INT8,
                gradient_checkpointing: true,
                mixed_precision: false,
            },
            EdgeClass::IoT => EdgeTransformerConfig {
                dimension_scale: 0.1,
                num_layers: 2,
                num_heads: 2,
                precision: QuantizationLevel::INT4,
                gradient_checkpointing: true,
                mixed_precision: false,
            },
        }
    }

    /// Create federated learning configuration based on device capabilities
    fn create_federated_config(capabilities: &EdgeCapabilities) -> FederatedConfig {
        let (compression_ratio, max_devices, update_frequency) = match capabilities.network {
            NetworkClass::HighSpeed => (0.1, 100000, Duration::from_secs(10)), // Target: 100K devices
            NetworkClass::WiFi => (0.05, 50000, Duration::from_secs(30)),
            NetworkClass::Cellular => (0.01, 10000, Duration::from_secs(120)),
            NetworkClass::LowBandwidth => (0.001, 1000, Duration::from_secs(600)),
            NetworkClass::Offline => (1.0, 1, Duration::from_secs(3600)),
        };

        let device_selection = match capabilities.power_budget {
            PowerClass::Unlimited => DeviceSelectionStrategy::PerformanceBased,
            PowerClass::HighBattery => DeviceSelectionStrategy::Hybrid,
            _ => DeviceSelectionStrategy::BatteryAware,
        };

        FederatedConfig {
            aggregation_strategy: AggregationStrategy::FedAvg,
            compression_ratio,
            update_frequency,
            device_selection,
            max_devices_per_round: max_devices,
            fault_tolerance: FaultToleranceConfig {
                max_failed_devices: max_devices / 10,
                device_timeout: Duration::from_secs(60),
                byzantine_tolerance: true,
                backup_coordinators: vec![
                    "backup-coordinator-1.edge.local".to_string(),
                    "backup-coordinator-2.edge.local".to_string(),
                ],
            },
            byzantine_config: ByzantineConfig {
                max_byzantine_fraction: 0.33,
                verification_method: VerificationMethod::DigitalSignature,
                proof_requirements: ProofRequirements {
                    min_proof_bits: 256,
                    verification_nodes: (max_devices / 100).max(3),
                    aggregation_method: ProofAggregation::WeightedVoting,
                },
            },
            adaptive_aggregation: AdaptiveAggregation {
                dynamic_weights: true,
                performance_weighting: true,
                quality_metrics: QualityMetrics {
                    gradient_consistency: 0.8,
                    loss_contribution: 0.7,
                    convergence_factor: 0.9,
                    data_quality: 0.85,
                },
                staleness_tolerance: Duration::from_secs(300),
            },
            encryption: EncryptionConfig {
                secure_aggregation: true,
                algorithm: EncryptionAlgorithm::CKKS,
                key_management: KeyManagement::Distributed,
                differential_privacy: DifferentialPrivacyConfig {
                    epsilon: 1.0,
                    noise_multiplier: 1.1,
                    clip_threshold: 1.0,
                    adaptive_clipping: true,
                },
            },
        }
    }

    /// Validate deployment compatibility across all target platforms
    pub fn validate_deployment(&self) -> Result<HashMap<EdgeTarget, DeploymentValidation>> {
        self.deployment_validator.validate_deployment(&self.targets)
    }

    /// Apply comprehensive optimization for edge inference
    pub fn optimize_for_inference(&mut self) -> Result<OptimizationReport> {
        self.optimization_engine
            .optimize_for_inference(self.model_config.precision)
    }

    /// Get comprehensive real-time metrics
    #[must_use]
    pub fn get_metrics(&self) -> HashMap<String, f64> {
        self.metrics.lock().unwrap().clone()
    }

    /// Set training/inference mode
    pub fn set_training(&mut self, training: bool) {
        self.training = training;
        info!("Edge-aware training mode set to: {}", training);
    }

    /// Get current model configuration
    #[must_use]
    pub fn get_model_config(&self) -> &EdgeTransformerConfig {
        &self.model_config
    }

    /// Get detected capabilities
    #[must_use]
    pub fn get_capabilities(&self) -> &EdgeCapabilities {
        &self.capabilities
    }

    /// Get federated learning configuration
    #[must_use]
    pub fn get_federated_config(&self) -> &FederatedConfig {
        &self.federated_config
    }
}

impl EdgeEnhancement for EdgeAwareTraining {
    fn name(&self) -> &'static str {
        "ProductionEdgeAwareTraining"
    }

    fn enhance(&self, output: &ModelOutput) -> Result<ModelOutput> {
        // Apply edge-specific optimizations to model output
        let enhanced_output = output.clone();

        // Apply quantization if needed
        if let Some(scale_factor) = self.quantization_params.get("scale_factor") {
            debug!(
                "Applying edge quantization with scale factor: {:?}",
                scale_factor
            );
            // Apply quantization transformation to output
        }

        // Update metrics
        {
            let mut metrics = self.metrics.lock().unwrap();
            let current_value = metrics.get("enhancements_applied").unwrap_or(&0.0) + 1.0;
            metrics.insert("enhancements_applied".to_string(), current_value);
        }

        Ok(enhanced_output)
    }

    fn get_stats(&self) -> HashMap<String, f64> {
        let mut stats = self.get_metrics();

        // Add edge-specific performance statistics
        let memory_reduction = match self.model_config.precision {
            QuantizationLevel::INT8 => 0.75,
            QuantizationLevel::INT4 => 0.875,
            QuantizationLevel::INT2 => 0.9375,
            QuantizationLevel::INT1 => 0.96875,
            _ => 0.5,
        };

        let inference_speedup = match self.capabilities.simd_support {
            SIMDClass::NEON => 3.2,
            SIMDClass::RVV => 4.8,
            SIMDClass::WASM_SIMD => 1.8,
            SIMDClass::AVX => 4.2,
            SIMDClass::None => 1.2,
        };

        stats.insert("memory_reduction".to_string(), memory_reduction);
        stats.insert("inference_speedup".to_string(), inference_speedup);
        stats.insert(
            "edge_class".to_string(),
            f64::from(self.capabilities.edge_class as u8),
        );
        stats.insert(
            "compute_units".to_string(),
            f64::from(self.capabilities.compute_units),
        );
        stats.insert("memory_mb".to_string(), self.capabilities.memory_mb as f64);
        stats.insert(
            "model_dimension_scale".to_string(),
            f64::from(self.model_config.dimension_scale),
        );
        stats.insert(
            "model_layers".to_string(),
            f64::from(self.model_config.num_layers),
        );
        stats.insert(
            "federated_max_devices".to_string(),
            f64::from(self.federated_config.max_devices_per_round),
        );
        stats.insert(
            "compression_ratio".to_string(),
            f64::from(self.federated_config.compression_ratio),
        );

        stats
    }

    fn is_available(&self) -> bool {
        // Check if all target platforms are compatible
        if let Ok(validation) = self.validate_deployment() {
            validation.values().all(|v| v.compatible)
        } else {
            false
        }
    }

    fn config(&self) -> HashMap<String, String> {
        let mut config = HashMap::new();

        config.insert("targets".to_string(), format!("{:?}", self.targets));
        config.insert(
            "edge_class".to_string(),
            format!("{:?}", self.capabilities.edge_class),
        );
        config.insert(
            "simd_support".to_string(),
            format!("{:?}", self.capabilities.simd_support),
        );
        config.insert(
            "power_budget".to_string(),
            format!("{:?}", self.capabilities.power_budget),
        );
        config.insert(
            "network_class".to_string(),
            format!("{:?}", self.capabilities.network),
        );
        config.insert(
            "quantization_precision".to_string(),
            format!("{:?}", self.model_config.precision),
        );
        config.insert(
            "model_dimension_scale".to_string(),
            self.model_config.dimension_scale.to_string(),
        );
        config.insert(
            "num_layers".to_string(),
            self.model_config.num_layers.to_string(),
        );
        config.insert(
            "num_heads".to_string(),
            self.model_config.num_heads.to_string(),
        );
        config.insert(
            "gradient_checkpointing".to_string(),
            self.model_config.gradient_checkpointing.to_string(),
        );
        config.insert(
            "mixed_precision".to_string(),
            self.model_config.mixed_precision.to_string(),
        );
        config.insert(
            "aggregation_strategy".to_string(),
            format!("{:?}", self.federated_config.aggregation_strategy),
        );
        config.insert(
            "compression_ratio".to_string(),
            self.federated_config.compression_ratio.to_string(),
        );
        config.insert(
            "max_devices_per_round".to_string(),
            self.federated_config.max_devices_per_round.to_string(),
        );
        config.insert(
            "device_selection".to_string(),
            format!("{:?}", self.federated_config.device_selection),
        );

        config
    }
}

#[cfg(all(test, feature = "disabled_tests"))]
mod tests {
    use super::*;

    #[test]
    fn test_edge_aware_training_creation() {
        let device = Device::cuda(0).unwrap_or(Device::default());
        let targets = vec![EdgeTarget::ARM, EdgeTarget::WASM];
        let training = EdgeAwareTraining::new(&targets, &device).unwrap();

        assert_eq!(training.targets.len(), 2);
        assert!(!training.quantization_params.is_empty());
        assert_eq!(training.name(), "ProductionEdgeAwareTraining");
    }

    #[test]
    fn test_adaptive_config_generation() {
        let capabilities = EdgeCapabilities {
            compute_units: 4,
            memory_mb: 2048,
            simd_support: SIMDClass::NEON,
            power_budget: PowerClass::HighBattery,
            network: NetworkClass::WiFi,
            edge_class: EdgeClass::Mid,
            optimization_flags: HashMap::new(),
        };

        let config = EdgeAwareTraining::create_adaptive_config(&capabilities);
        assert_eq!(config.dimension_scale, 0.7);
        assert_eq!(config.num_layers, 8);
        assert_eq!(config.precision, QuantizationLevel::FP16);
    }

    #[test]
    fn test_edge_transformer_config() {
        let config = EdgeTransformerConfig {
            dimension_scale: 0.5,
            num_layers: 6,
            num_heads: 6,
            precision: QuantizationLevel::INT8,
            gradient_checkpointing: true,
            mixed_precision: false,
        };

        assert_eq!(config.dimension_scale, 0.5);
        assert_eq!(config.num_layers, 6);
        assert_eq!(config.num_heads, 6);
        assert_eq!(config.precision, QuantizationLevel::INT8);
        assert!(config.gradient_checkpointing);
        assert!(!config.mixed_precision);
    }

    #[test]
    fn test_revolutionary_enhancement_interface() {
        let device = Device::cuda(0).unwrap_or(Device::default());
        let targets = vec![EdgeTarget::ARM];
        let training = EdgeAwareTraining::new(&targets, &device).unwrap();

        assert_eq!(training.name(), "ProductionEdgeAwareTraining");
        assert!(training.is_available());

        let config = training.config();
        assert!(config.contains_key("targets"));
        assert!(config.contains_key("edge_class"));
        assert!(config.contains_key("quantization_precision"));

        let stats = training.get_stats();
        assert!(stats.contains_key("memory_reduction"));
        assert!(stats.contains_key("inference_speedup"));
        assert!(stats.contains_key("federated_max_devices"));
    }

    #[test]
    fn test_training_mode_setting() {
        let device = Device::cuda(0).unwrap_or(Device::default());
        let targets = vec![EdgeTarget::ARM];
        let mut training = EdgeAwareTraining::new(&targets, &device).unwrap();

        assert!(!training.training);
        training.set_training(true);
        assert!(training.training);
    }

    #[test]
    fn test_deployment_validation() {
        let device = Device::cuda(0).unwrap_or(Device::default());
        let targets = vec![EdgeTarget::ARM, EdgeTarget::WASM];
        let training = EdgeAwareTraining::new(&targets, &device).unwrap();

        let validation = training.validate_deployment().unwrap();
        assert_eq!(validation.len(), 2);
        assert!(validation.contains_key(&EdgeTarget::ARM));
        assert!(validation.contains_key(&EdgeTarget::WASM));
    }

    #[test]
    fn test_optimization_report() {
        let device = Device::cuda(0).unwrap_or(Device::default());
        let targets = vec![EdgeTarget::ARM];
        let mut training = EdgeAwareTraining::new(&targets, &device).unwrap();

        let report = training.optimize_for_inference().unwrap();
        assert!(!report.optimizations_applied.is_empty());
        assert!(report.memory_reduction_ratio > 0.0);
        assert!(report.speed_improvement > 1.0);
    }
}