scirs2-vision 0.5.1

//! Neural Architecture Search (NAS) for computer vision pipelines
//!
//! This module implements neural architecture search techniques for automatically
//! discovering optimal processing architectures for computer vision tasks.

use crate::error::Result;
use scirs2_core::random::prelude::*;
use std::collections::HashMap;
use std::time::Instant;

// Import from reinforcement_learning module
use super::reinforcement_learning::RLLearningParams;

/// Neural Architecture Search system
#[derive(Debug)]
pub struct NeuralArchitectureSearch {
    /// Search space definition
    _searchspace: ArchitectureSearchSpace,
    /// Current architectures being evaluated
    candidate_architectures: Vec<ProcessingArchitecture>,
    /// Performance database
    performance_db: HashMap<String, ArchitecturePerformance>,
    /// Search strategy
    search_strategy: SearchStrategy,
    /// Search iteration
    current_iteration: usize,
}

/// Architecture search space
#[derive(Debug, Clone)]
pub struct ArchitectureSearchSpace {
    /// Available layer types
    pub layer_types: Vec<LayerType>,
    /// Depth range (min, max)
    pub depth_range: (usize, usize),
    /// Width range for each layer
    pub width_range: (usize, usize),
    /// Available activation functions
    pub activations: Vec<ActivationType>,
    /// Available connection patterns
    pub connections: Vec<ConnectionType>,
}

/// Layer types for neural processing
#[derive(Debug, Clone, PartialEq)]
pub enum LayerType {
    /// Convolutional layer
    Convolution {
        /// Size of the convolution kernel
        kernel_size: usize,
        /// Stride of the convolution
        stride: usize,
    },
    /// Separable convolution
    SeparableConv {
        /// Size of the convolution kernel
        kernel_size: usize,
    },
    /// Dilated convolution
    DilatedConv {
        /// Size of the convolution kernel
        kernel_size: usize,
        /// Dilation factor
        dilation: usize,
    },
    /// Depthwise convolution
    DepthwiseConv {
        /// Size of the convolution kernel
        kernel_size: usize,
    },
    /// Pooling layer
    Pooling {
        /// Type of pooling operation
        pool_type: PoolingType,
        /// Size of the pooling window
        size: usize,
    },
    /// Normalization layer
    Normalization {
        /// Type of normalization
        norm_type: NormalizationType,
    },
    /// Attention mechanism
    Attention {
        /// Type of attention mechanism
        attention_type: AttentionType,
    },
}

/// Pooling types
#[derive(Debug, Clone, PartialEq)]
pub enum PoolingType {
    /// Maximum pooling
    Max,
    /// Average pooling
    Average,
    /// Adaptive pooling
    Adaptive,
}

/// Normalization types
#[derive(Debug, Clone, PartialEq)]
pub enum NormalizationType {
    /// Batch normalization
    Batch,
    /// Layer normalization
    Layer,
    /// Instance normalization
    Instance,
}

/// Attention types
#[derive(Debug, Clone, PartialEq)]
pub enum AttentionType {
    /// Self-attention mechanism
    SelfAttention,
    /// Cross-attention mechanism
    CrossAttention,
    /// Spatial attention
    Spatial,
}

/// Activation function types
#[derive(Debug, Clone, PartialEq)]
pub enum ActivationType {
    /// ReLU activation
    ReLU,
    /// Leaky ReLU activation
    LeakyReLU,
    /// Swish activation
    Swish,
    /// GELU activation
    GELU,
    /// Mish activation
    Mish,
}

/// Connection patterns
#[derive(Debug, Clone, PartialEq)]
pub enum ConnectionType {
    /// Sequential connections
    Sequential,
    /// Skip connections
    Skip,
    /// Dense connections
    Dense,
    /// Attention-based connections
    Attention,
}

/// Processing architecture candidate
#[derive(Debug, Clone)]
pub struct ProcessingArchitecture {
    /// Architecture identifier
    pub id: String,
    /// Layer sequence
    pub layers: Vec<LayerType>,
    /// Connection pattern
    pub connections: Vec<ConnectionType>,
    /// Architecture complexity
    pub complexity: f64,
    /// Estimated parameters
    pub parameter_count: usize,
}

/// Architecture performance metrics
#[derive(Debug, Clone)]
pub struct ArchitecturePerformance {
    /// Processing accuracy
    pub accuracy: f64,
    /// Processing speed (FPS)
    pub speed: f64,
    /// Memory usage (MB)
    pub memory_usage: f64,
    /// Energy consumption
    pub energy: f64,
    /// Architecture efficiency score
    pub efficiency_score: f64,
}

/// Search strategies for NAS
#[derive(Debug, Clone)]
pub enum SearchStrategy {
    /// Random search
    Random,
    /// Evolutionary search
    Evolutionary {
        /// Size of the evolutionary population
        populationsize: usize,
    },
    /// Reinforcement learning-based
    ReinforcementLearning {
        /// Parameters for RL controller
        controller_params: RLLearningParams,
    },
    /// Bayesian optimization
    BayesianOptimization {
        /// Acquisition function to use
        acquisition_fn: AcquisitionFunction,
    },
}

/// Acquisition functions for Bayesian optimization
#[derive(Debug, Clone)]
pub enum AcquisitionFunction {
    /// Expected improvement acquisition
    ExpectedImprovement,
    /// Upper confidence bound acquisition
    UpperConfidenceBound,
    /// Probability of improvement acquisition
    ProbabilityOfImprovement,
}

impl NeuralArchitectureSearch {
    /// Create a new NAS instance
    pub fn new(_searchspace: ArchitectureSearchSpace, strategy: SearchStrategy) -> Self {
        Self {
            _searchspace,
            candidate_architectures: Vec::new(),
            performance_db: HashMap::new(),
            search_strategy: strategy,
            current_iteration: 0,
        }
    }

    /// Generate candidate architectures
    pub fn generate_candidates(&mut self, numcandidates: usize) -> Vec<ProcessingArchitecture> {
        let candidates = match &self.search_strategy {
            SearchStrategy::Random => self.random_search(numcandidates),
            SearchStrategy::Evolutionary { populationsize } => {
                self.evolutionary_search(*populationsize)
            }
            SearchStrategy::ReinforcementLearning { .. } => self.rl_search(numcandidates),
            SearchStrategy::BayesianOptimization { .. } => self.bayesian_search(numcandidates),
        };

        self.candidate_architectures = candidates.clone();
        candidates
    }

    /// Random architecture search
    fn random_search(&self, numcandidates: usize) -> Vec<ProcessingArchitecture> {
        let mut candidates = Vec::new();
        let mut rng = thread_rng();

        for i in 0..numcandidates {
            let depth = rng
                .random_range(self._searchspace.depth_range.0..self._searchspace.depth_range.1 + 1);
            let mut layers = Vec::new();
            let mut connections = Vec::new();

            for _ in 0..depth {
                let idx = rng.random_range(0..self._searchspace.layer_types.len());
                let layer_type = self._searchspace.layer_types[idx].clone();
                layers.push(layer_type);

                let idx = rng.random_range(0..self._searchspace.connections.len());
                let connection = self._searchspace.connections[idx].clone();
                connections.push(connection);
            }

            let complexity = self.calculate_complexity(&layers);
            let parameter_count = self.estimate_parameters(&layers);
            let architecture = ProcessingArchitecture {
                id: format!("arch_{i}"),
                layers,
                connections,
                complexity,
                parameter_count,
            };

            candidates.push(architecture);
        }

        candidates
    }

    /// Evolutionary architecture search
    fn evolutionary_search(&self, populationsize: usize) -> Vec<ProcessingArchitecture> {
        // Initialize with random population if first iteration
        if self.current_iteration == 0 {
            return self.random_search(populationsize);
        }

        // Evolve existing population
        let mut new_population = Vec::new();
        let mut rng = thread_rng();

        // Select best performing architectures
        let mut ranked_archs: Vec<_> = self
            .candidate_architectures
            .iter()
            .filter_map(|arch_| {
                self.performance_db
                    .get(&arch_.id)
                    .map(|perf| (arch_, perf.efficiency_score))
            })
            .collect();

        ranked_archs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

        // Keep top performers
        let elite_count = populationsize / 4;
        for (arch_, _) in ranked_archs.iter().take(elite_count) {
            new_population.push((*arch_).clone());
        }

        // Generate offspring through mutation and crossover
        while new_population.len() < populationsize {
            if ranked_archs.len() >= 2 {
                let idx = rng.random_range(0..ranked_archs.len());
                let parent1 = ranked_archs[idx].0;
                let idx = rng.random_range(0..ranked_archs.len());
                let parent2 = ranked_archs[idx].0;

                let offspring = self.crossover_architectures(parent1, parent2);
                let mutated = self.mutate_architecture(offspring);

                new_population.push(mutated);
            } else {
                // Fallback to random generation
                new_population.extend(self.random_search(1));
            }
        }

        new_population
    }

    /// RL-based architecture search
    fn rl_search(&self, numcandidates: usize) -> Vec<ProcessingArchitecture> {
        // Simplified RL search - would use a controller network in practice
        self.random_search(numcandidates)
    }

    /// Bayesian optimization search
    fn bayesian_search(&self, numcandidates: usize) -> Vec<ProcessingArchitecture> {
        // Simplified Bayesian search - would use Gaussian processes in practice
        self.random_search(numcandidates)
    }

    /// Calculate architecture complexity
    fn calculate_complexity(&self, layers: &[LayerType]) -> f64 {
        layers
            .iter()
            .map(|layer| match layer {
                LayerType::Convolution { kernel_size, .. } => *kernel_size as f64,
                LayerType::SeparableConv { kernel_size } => *kernel_size as f64 * 0.5,
                LayerType::DilatedConv {
                    kernel_size,
                    dilation,
                } => *kernel_size as f64 * *dilation as f64,
                LayerType::DepthwiseConv { kernel_size } => *kernel_size as f64 * 0.3,
                LayerType::Pooling { .. } => 1.0,
                LayerType::Normalization { .. } => 0.5,
                LayerType::Attention { .. } => 10.0,
            })
            .sum()
    }

    /// Estimate parameter count
    fn estimate_parameters(&self, layers: &[LayerType]) -> usize {
        layers
            .iter()
            .map(|layer| match layer {
                LayerType::Convolution { kernel_size, .. } => kernel_size * kernel_size * 64,
                LayerType::SeparableConv { kernel_size } => kernel_size * kernel_size * 32,
                LayerType::DilatedConv { kernel_size, .. } => kernel_size * kernel_size * 64,
                LayerType::DepthwiseConv { kernel_size } => kernel_size * kernel_size * 16,
                LayerType::Pooling { .. } => 0,
                LayerType::Normalization { .. } => 128,
                LayerType::Attention { .. } => 1024,
            })
            .sum()
    }

    /// Crossover two architectures
    fn crossover_architectures(
        &self,
        parent1: &ProcessingArchitecture,
        parent2: &ProcessingArchitecture,
    ) -> ProcessingArchitecture {
        let mut rng = thread_rng();
        let min_depth = parent1.layers.len().min(parent2.layers.len());
        let crossover_point = rng.random_range(1..min_depth);

        let mut new_layers = Vec::new();
        let mut new_connections = Vec::new();

        // Take first part from parent1
        new_layers.extend_from_slice(&parent1.layers[..crossover_point]);
        new_connections.extend_from_slice(&parent1.connections[..crossover_point]);

        // Take second part from parent2
        if crossover_point < parent2.layers.len() {
            new_layers.extend_from_slice(&parent2.layers[crossover_point..]);
            new_connections.extend_from_slice(&parent2.connections[crossover_point..]);
        }

        let complexity = self.calculate_complexity(&new_layers);
        let parameter_count = self.estimate_parameters(&new_layers);
        ProcessingArchitecture {
            id: format!("crossover_{}", self.current_iteration),
            layers: new_layers,
            connections: new_connections,
            complexity,
            parameter_count,
        }
    }

    /// Mutate an architecture
    fn mutate_architecture(
        &self,
        mut architecture: ProcessingArchitecture,
    ) -> ProcessingArchitecture {
        let mut rng = thread_rng();

        // Randomly mutate some layers
        for layer in &mut architecture.layers {
            if rng.random::<f64>() < 0.1 {
                // 10% mutation rate
                let idx = rng.random_range(0..self._searchspace.layer_types.len());
                *layer = self._searchspace.layer_types[idx].clone();
            }
        }

        // Update complexity and parameter count
        architecture.complexity = self.calculate_complexity(&architecture.layers);
        architecture.parameter_count = self.estimate_parameters(&architecture.layers);
        architecture.id = format!("mutated_{}", self.current_iteration);

        architecture
    }

    /// Record architecture performance
    pub fn record_performance(
        &mut self,
        architecture_id: &str,
        performance: ArchitecturePerformance,
    ) {
        self.performance_db
            .insert(architecture_id.to_string(), performance);
    }

    /// Get best architecture found so far
    pub fn get_best_architecture(
        &self,
    ) -> Option<(&ProcessingArchitecture, &ArchitecturePerformance)> {
        let mut best_arch = None;
        let mut best_score = f64::NEG_INFINITY;

        for arch_ in &self.candidate_architectures {
            if let Some(perf) = self.performance_db.get(&arch_.id) {
                if perf.efficiency_score > best_score {
                    best_score = perf.efficiency_score;
                    best_arch = Some((arch_, perf));
                }
            }
        }

        best_arch
    }

    /// Advance to next iteration
    pub fn next_iteration(&mut self) {
        self.current_iteration += 1;
    }

    /// Initialize search space
    pub async fn initialize_search_space(&mut self) -> Result<()> {
        // Reset candidate architectures
        self.candidate_architectures.clear();

        // Reset performance database
        self.performance_db.clear();

        // Reset iteration counter
        self.current_iteration = 0;

        Ok(())
    }
}