//! Edge-aware integration for transformers

use crate::Result;
use crate::training::{ModelConfig, ModelOutput, TransformerModel};
use rtx_tensor::{Device, Tensor};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use tracing::{debug, info};

// Edge computing modules
pub mod edge_aware_training;
pub mod edge_capabilities;
pub mod edge_deployment;
pub mod edge_optimizations;
pub mod edge_trainer;
pub mod federated_coordination;
pub mod hybrid_orchestrator;

// Orchestrator modules
pub mod orchestrator_core;
pub mod orchestrator_edge;
pub mod orchestrator_types;

// Re-export edge components
pub use edge_aware_training::{EdgeAwareTrainingSystem, EdgeTrainingConfig};
pub use edge_capabilities::{
    EdgeCapabilities, EdgeCapabilityDetector, EdgeClass, NetworkClass, PowerClass, SIMDClass,
};
pub use edge_deployment::{DeploymentValidation, EdgeDeploymentValidator, PerformanceEstimate};
pub use edge_optimizations::{EdgeOptimizationEngine, OptimizationReport, QuantizationLevel};
pub use edge_trainer::{EdgeAwareTraining as ModularEdgeAwareTraining, EdgeTransformerConfig};
pub use federated_coordination::{
    CoordinatorMessage, DeviceMessage, FederatedConfig, FederatedCoordinator,
};
pub use hybrid_orchestrator::HybridOrchestrator;

// Re-export orchestrator components
pub use orchestrator_edge::{
    EdgeConstraints, EdgeHealthReport, EdgeHealthStatus, EdgeMetrics, EdgeOrchestrator,
};
pub use orchestrator_types::*;

/// Edge-aware capabilities configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RevolutionaryConfig {
    /// Enable edge-aware training
    pub edge_aware_training: bool,
    /// Edge deployment targets
    pub edge_targets: Vec<EdgeTarget>,
}

impl Default for RevolutionaryConfig {
    fn default() -> Self {
        Self {
            edge_aware_training: true,
            edge_targets: vec![EdgeTarget::ARM, EdgeTarget::Mobile],
        }
    }
}

/// Edge deployment targets
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub enum EdgeTarget {
    /// ARM processors
    ARM,
    /// RISC-V processors
    RISCV,
    /// WebAssembly
    WASM,
    /// Mobile devices
    Mobile,
    /// Mobile GPU devices
    MobileGPU,
    /// FPGA acceleration
    FPGA,
    /// `IoT` devices
    IoT,
    /// Embedded systems
    Embedded,
    /// Custom target
    Custom(String),
}

/// Edge enhancement trait
pub trait EdgeEnhancement: Send + Sync {
    /// Get enhancement name
    fn name(&self) -> &'static str;

    /// Apply enhancement to model output
    fn enhance(&self, output: &ModelOutput) -> Result<ModelOutput>;

    /// Get enhancement statistics
    fn get_stats(&self) -> HashMap<String, f64>;

    /// Check if enhancement is available
    fn is_available(&self) -> bool;

    /// Get enhancement configuration
    fn config(&self) -> HashMap<String, String>;
}

/// Edge-aware transformer wrapper
#[derive(Debug)]
pub struct EdgeAwareTransformer {
    /// Base transformer model
    base_model: Box<dyn TransformerModel>,
    /// Configuration
    config: RevolutionaryConfig,
    /// Edge-aware training
    edge_aware: Option<EdgeAwareTrainingSystem>,
    /// Hybrid orchestrator
    orchestrator: HybridOrchestrator,
    /// Device
    device: Device,
    /// Performance metrics
    metrics: HashMap<String, f64>,
}

impl EdgeAwareTransformer {
    /// Create a new edge-aware transformer
    pub fn new(
        base_model: Box<dyn TransformerModel>,
        config: RevolutionaryConfig,
        device: &Device,
    ) -> Result<Self> {
        info!("Creating edge-aware transformer with config: {:?}", config);

        // Initialize edge-aware training
        let edge_aware = if config.edge_aware_training {
            Some(EdgeAwareTrainingSystem::new(EdgeTrainingConfig::default())?)
        } else {
            None
        };

        // Initialize hybrid orchestrator
        let orchestrator = HybridOrchestrator::new(&config, device)?;

        info!(
            "Edge-aware transformer created with edge-aware training: {}",
            edge_aware.is_some()
        );

        Ok(Self {
            base_model,
            config,
            edge_aware,
            orchestrator,
            device: device.clone(),
            metrics: HashMap::new(),
        })
    }

    /// Validate for edge deployment
    pub fn validate_edge_deployment(&self) -> Result<HashMap<EdgeTarget, bool>> {
        if let Some(ref edge) = self.edge_aware {
            // TODO: Implement validate_deployment method
            Ok(HashMap::new())
        } else {
            Ok(HashMap::new())
        }
    }

    /// Get edge performance metrics
    #[must_use] 
    pub fn get_edge_metrics(&self) -> HashMap<String, f64> {
        let mut all_metrics = self.metrics.clone();

        // Add edge metrics
        if let Some(ref edge) = self.edge_aware {
            // TODO: Implement get_stats method
            for (key, value) in HashMap::<String, f64>::new() {
                all_metrics.insert(format!("edge_{key}"), value);
            }
        }

        all_metrics
    }

    /// Get available capabilities
    #[must_use] 
    pub fn get_capabilities(&self) -> Vec<String> {
        let mut capabilities = Vec::new();

        if self.edge_aware.is_some() {
            capabilities.push("Edge-Aware Training".to_string());
        }

        capabilities.push("Hybrid Orchestration".to_string());

        capabilities
    }

    /// Optimize for specific deployment scenario
    pub fn optimize_for_deployment(&mut self, scenario: DeploymentScenario) -> Result<()> {
        info!("Optimizing for deployment scenario: {:?}", scenario);

        match scenario {
            DeploymentScenario::EdgeInference => {
                // Optimize for edge deployment
                if let Some(ref mut edge) = self.edge_aware {
                    // TODO: Implement optimize_for_inference method
                }
            }
            DeploymentScenario::HybridPerformance => {
                // Balance performance
                futures::executor::block_on(async {
                    self.orchestrator.optimize_hybrid_performance().await
                })?;
            }
        }

        Ok(())
    }
}

/// Deployment scenarios for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DeploymentScenario {
    /// Optimize for edge inference
    EdgeInference,
    /// Balance hybrid performance
    HybridPerformance,
}

impl TransformerModel for EdgeAwareTransformer {
    fn forward(&mut self, input_ids: &Tensor, labels: Option<&Tensor>) -> Result<ModelOutput> {
        debug!("Edge-aware transformer forward pass");

        // Forward through base model
        let output = self.base_model.forward(input_ids, labels)?;

        // Update metrics
        self.metrics.insert(
            "edge_forward_calls".to_string(),
            self.metrics.get("edge_forward_calls").unwrap_or(&0.0) + 1.0,
        );

        Ok(output)
    }

    fn parameters(&self) -> HashMap<String, Tensor> {
        self.base_model.parameters()
    }

    fn update_parameters(&mut self, updates: &HashMap<String, Tensor>) -> Result<()> {
        self.base_model.update_parameters(updates)
    }

    fn config(&self) -> ModelConfig {
        let mut base_config = self.base_model.config();
        base_config.model_type = "EdgeAwareTransformer".to_string();

        // Add edge capabilities
        base_config.config.insert(
            "edge_aware_enabled".to_string(),
            self.edge_aware.is_some().to_string(),
        );

        base_config
    }

    fn set_training(&mut self, training: bool) {
        self.base_model.set_training(training);

        if let Some(ref mut edge) = self.edge_aware {
            // TODO: Implement set_training method
        }
    }

    fn memory_stats(&self) -> HashMap<String, usize> {
        let mut stats = self.base_model.memory_stats();

        // Add edge memory usage
        stats.insert(
            "edge_enhancements".to_string(),
            usize::from(self.edge_aware.is_some()),
        );

        stats
    }
}

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

    // Mock transformer model for testing
    #[derive(Debug)]
    struct MockTransformerModel;

    impl TransformerModel for MockTransformerModel {
        fn forward(&mut self, input_ids: &Tensor, _labels: Option<&Tensor>) -> Result<ModelOutput> {
            Ok(ModelOutput {
                loss: None,
                logits: input_ids.clone(),
                additional_outputs: HashMap::new(),
            })
        }

        fn parameters(&self) -> HashMap<String, Tensor> {
            HashMap::new()
        }

        fn update_parameters(&mut self, _updates: &HashMap<String, Tensor>) -> Result<()> {
            Ok(())
        }

        fn config(&self) -> ModelConfig {
            ModelConfig {
                model_type: "Mock".to_string(),
                num_parameters: 1000,
                dtype: DType::F32,
                config: HashMap::new(),
            }
        }

        fn set_training(&mut self, _training: bool) {}

        fn memory_stats(&self) -> HashMap<String, usize> {
            HashMap::new()
        }
    }

    #[test]
    fn test_edge_aware_config() {
        let config = RevolutionaryConfig::default();
        assert!(config.edge_aware_training);
        assert_eq!(config.edge_targets.len(), 2);
    }

    #[test]
    fn test_edge_aware_transformer_creation() {
        let base_model = Box::new(MockTransformerModel);
        let config = RevolutionaryConfig::default();
        let device = Device::cuda(0).unwrap_or(Device::default());

        let edge_aware = EdgeAwareTransformer::new(base_model, config, &device);
        assert!(edge_aware.is_ok());

        let edge_aware = edge_aware.unwrap();
        let capabilities = edge_aware.get_capabilities();
        assert!(!capabilities.is_empty());
        assert!(capabilities.contains(&"Edge-Aware Training".to_string()));
    }

    #[test]
    fn test_deployment_scenarios() {
        let scenarios = vec![
            DeploymentScenario::EdgeInference,
            DeploymentScenario::HybridPerformance,
        ];

        for scenario in scenarios {
            // Test serialization
            let json = serde_json::to_string(&scenario).unwrap();
            let _deserialized: DeploymentScenario = serde_json::from_str(&json).unwrap();
        }
    }

    #[test]
    fn test_edge_targets() {
        let targets = vec![
            EdgeTarget::ARM,
            EdgeTarget::RISCV,
            EdgeTarget::WASM,
            EdgeTarget::Mobile,
            EdgeTarget::FPGA,
            EdgeTarget::IoT,
            EdgeTarget::Embedded,
            EdgeTarget::Custom("test".to_string()),
        ];

        for target in targets {
            // Test serialization
            let json = serde_json::to_string(&target).unwrap();
            let _deserialized: EdgeTarget = serde_json::from_str(&json).unwrap();
        }
    }
}