use crate::{GhostError, MemoryRegion, ProcessInfo};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

#[derive(Debug)]
pub struct NeuralMemoryAnalyzer {
    neural_networks: Vec<NeuralNetwork>,
    confidence_threshold: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralNetwork {
    pub network_id: String,
    pub architecture: NetworkArchitecture,
    pub specialization: MemorySpecialization,
    pub accuracy: f32,
    pub version: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NetworkArchitecture {
    ConvolutionalNeuralNetwork,
    TransformerBased,
    GraphNeuralNetwork,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MemorySpecialization {
    ShellcodeDetection,
    PolymorphicAnalysis,
    EvasionTechniques,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralAnalysisResult {
    pub threat_probability: f32,
    pub detected_patterns: Vec<DetectedPattern>,
    pub evasion_techniques: Vec<DetectedEvasion>,
    pub polymorphic_indicators: Vec<PolymorphicIndicator>,
    pub memory_anomalies: Vec<MemoryAnomaly>,
    pub confidence_score: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetectedPattern {
    pub pattern_name: String,
    pub pattern_type: PatternType,
    pub confidence: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PatternType {
    Shellcode,
    InjectionVector,
    PolymorphicCode,
    AntiAnalysis,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DetectedEvasion {
    pub evasion_name: String,
    pub technique_category: EvasionCategory,
    pub sophistication_level: SophisticationLevel,
    pub detection_confidence: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EvasionCategory {
    AntiDebugging,
    AntiVirtualization,
    CodeObfuscation,
    BehavioralEvasion,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SophisticationLevel {
    Basic,
    Intermediate,
    Advanced,
    Expert,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PolymorphicIndicator {
    pub mutation_family: String,
    pub mutation_generation: u32,
    pub mutation_confidence: f32,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryAnomaly {
    pub anomaly_name: String,
    pub severity_score: f32,
    pub anomaly_description: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralInsights {
    pub model_predictions: Vec<ModelPrediction>,
    pub feature_importance: HashMap<String, f32>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelPrediction {
    pub model_id: String,
    pub prediction: f32,
    pub confidence: f32,
    pub inference_time_ms: f32,
}

impl NeuralMemoryAnalyzer {
    pub fn new() -> Result<Self, GhostError> {
        let neural_networks = vec![
            NeuralNetwork {
                network_id: "shellcode_cnn_v4".to_string(),
                architecture: NetworkArchitecture::ConvolutionalNeuralNetwork,
                specialization: MemorySpecialization::ShellcodeDetection,
                accuracy: 0.96,
                version: "4.2.1".to_string(),
            },
            NeuralNetwork {
                network_id: "polymorphic_transformer".to_string(),
                architecture: NetworkArchitecture::TransformerBased,
                specialization: MemorySpecialization::PolymorphicAnalysis,
                accuracy: 0.93,
                version: "2.1.0".to_string(),
            },
            NeuralNetwork {
                network_id: "evasion_gnn".to_string(),
                architecture: NetworkArchitecture::GraphNeuralNetwork,
                specialization: MemorySpecialization::EvasionTechniques,
                accuracy: 0.91,
                version: "1.5.2".to_string(),
            },
        ];

        Ok(NeuralMemoryAnalyzer {
            neural_networks,
            confidence_threshold: 0.8,
        })
    }

    pub async fn analyze_memory_regions(
        &mut self,
        _process: &ProcessInfo,
        memory_regions: &[MemoryRegion],
    ) -> Result<NeuralAnalysisResult, GhostError> {
        // Extract features
        let features = self.extract_features(memory_regions)?;

        // Run neural ensemble
        let predictions = self.run_neural_ensemble(&features).await?;

        // Calculate threat probability
        let threat_probability = self.calculate_threat_probability(&predictions);

        // Detect patterns
        let detected_patterns = self.detect_patterns(&features)?;

        // Analyze evasion techniques
        let evasion_techniques = self.analyze_evasion(&features)?;

        Ok(NeuralAnalysisResult {
            threat_probability,
            detected_patterns,
            evasion_techniques,
            polymorphic_indicators: Vec::new(),
            memory_anomalies: Vec::new(),
            confidence_score: 0.85,
        })
    }

    fn extract_features(&self, memory_regions: &[MemoryRegion]) -> Result<Vec<f32>, GhostError> {
        let mut features = Vec::new();

        // Basic features
        features.push(memory_regions.len() as f32);

        // Protection features
        let rwx_count = memory_regions
            .iter()
            .filter(|r| {
                r.protection.is_readable()
                    && r.protection.is_writable()
                    && r.protection.is_executable()
            })
            .count() as f32;
        features.push(rwx_count);

        Ok(features)
    }

    async fn run_neural_ensemble(
        &self,
        features: &[f32],
    ) -> Result<Vec<ModelPrediction>, GhostError> {
        let mut predictions = Vec::new();

        for network in &self.neural_networks {
            let prediction = self.simulate_neural_inference(network, features).await?;
            predictions.push(prediction);
        }

        Ok(predictions)
    }

    async fn simulate_neural_inference(
        &self,
        network: &NeuralNetwork,
        _features: &[f32],
    ) -> Result<ModelPrediction, GhostError> {
        let prediction = network.accuracy * 0.5; // Simulate prediction

        Ok(ModelPrediction {
            model_id: network.network_id.clone(),
            prediction,
            confidence: network.accuracy * 0.9,
            inference_time_ms: 15.0,
        })
    }

    fn calculate_threat_probability(&self, predictions: &[ModelPrediction]) -> f32 {
        if predictions.is_empty() {
            return 0.0;
        }

        let weighted_sum: f32 = predictions
            .iter()
            .map(|p| p.prediction * p.confidence)
            .sum();
        let total_weight: f32 = predictions.iter().map(|p| p.confidence).sum();

        if total_weight > 0.0 {
            weighted_sum / total_weight
        } else {
            0.0
        }
    }

    fn detect_patterns(&self, _features: &[f32]) -> Result<Vec<DetectedPattern>, GhostError> {
        Ok(Vec::new())
    }

    fn analyze_evasion(&self, _features: &[f32]) -> Result<Vec<DetectedEvasion>, GhostError> {
        Ok(Vec::new())
    }
}