// Architecture controllers for neural architecture search
//
// Implements various controllers for generating and managing optimizer architectures
// during the search process.

#[allow(dead_code)]

use scirs2_core::ndarray::{s, Array1, Array2};
use scirs2_core::numeric::Float;
use scirs2_core::random::Rng;
use scirs2_core::random::Rng as SCRRng;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;

use super::{
    architecture_space::{ComponentType, OptimizerComponent},
    OptimizerArchitecture, SearchResult, SearchSpaceConfig,
};
use crate::error::{OptimError, Result};

/// Base trait for architecture controllers
pub trait ArchitectureController<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
    /// Initialize the controller
    fn initialize(&mut self, searchspace: &SearchSpaceConfig) -> Result<()>;

    /// Generate new architecture
    fn generate_architecture(
        &mut self,
        context: &SearchContext<T>,
    ) -> Result<OptimizerArchitecture<T>>;

    /// Update controller with search results
    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()>;

    /// Get controller statistics
    fn get_statistics(&self) -> ControllerStatistics<T>;

    /// Get controller name
    fn name(&self) -> &str;

    /// Reset controller state
    fn reset(&mut self) -> Result<()>;
}

/// Search context for architecture generation
#[derive(Debug, Clone)]
pub struct SearchContext<T: Float + Debug + Send + Sync + 'static> {
    /// Current generation
    pub generation: usize,

    /// Search history
    pub history: Vec<SearchResult<T>>,

    /// Current best performance
    pub best_performance: T,

    /// Current average performance
    pub average_performance: T,

    /// Resource constraints
    pub resource_budget: ResourceBudget<T>,

    /// Search preferences
    pub preferences: SearchPreferences,
}

/// Resource budget for search
#[derive(Debug, Clone)]
pub struct ResourceBudget<T: Float + Debug + Send + Sync + 'static> {
    /// Remaining memory budget (GB)
    pub memory_gb: T,

    /// Remaining computation budget (FLOPS)
    pub computation_flops: T,

    /// Remaining time budget (seconds)
    pub time_seconds: T,

    /// Remaining energy budget (kWh)
    pub energy_kwh: T,
}

/// Search preferences
#[derive(Debug, Clone)]
pub struct SearchPreferences {
    /// Exploration vs exploitation balance
    pub exploration_factor: f64,

    /// Preferred architecture complexity
    pub complexity_preference: ComplexityPreference,

    /// Domain-specific preferences
    pub domain_preferences: Vec<DomainPreference>,
}

/// Architecture complexity preferences
#[derive(Debug, Clone, Copy)]
pub enum ComplexityPreference {
    Simple,
    Moderate,
    Complex,
    Adaptive,
}

/// Domain-specific preferences
#[derive(Debug, Clone)]
pub struct DomainPreference {
    pub domain: String,
    pub weight: f64,
    pub constraints: Vec<String>,
}

/// Controller statistics
#[derive(Debug, Clone)]
pub struct ControllerStatistics<T: Float + Debug + Send + Sync + 'static> {
    /// Total architectures generated
    pub total_generated: usize,

    /// Success rate (valid architectures)
    pub success_rate: f64,

    /// Average generation time
    pub avg_generation_time_ms: f64,

    /// Diversity measure
    pub diversity_score: T,

    /// Controller-specific metrics
    pub controller_metrics: HashMap<String, T>,
}

/// RNN-based architecture controller
pub struct RNNController<
    T: Float
        + Default
        + Clone
        + Send
        + Sync
        + 'static
        + std::iter::Sum
        + for<'a> std::iter::Sum<&'a T>
        + scirs2_core::ndarray::ScalarOperand,
> {
    /// Controller configuration
    config: RNNControllerConfig,

    /// RNN layers
    rnn_layers: Vec<RNNLayer<T>>,

    /// Output projection layer
    output_layer: OutputLayer<T>,

    /// Hidden states
    hidden_states: Vec<Array1<T>>,

    /// Cell states (for LSTM)
    cell_states: Vec<Array1<T>>,

    /// Embedding layer for component types
    embedding_layer: EmbeddingLayer<T>,

    /// Search space information
    search_space: Option<SearchSpaceConfig>,

    /// Controller statistics
    statistics: ControllerStatistics<T>,

    /// Training history
    training_history: VecDeque<TrainingBatch<T>>,

    /// Action space mapping
    action_space: ActionSpace,

    /// Generation count
    generation_count: usize,
}

/// RNN controller configuration
#[derive(Debug, Clone)]
pub struct RNNControllerConfig {
    /// Hidden size
    pub hiddensize: usize,

    /// Number of layers
    pub numlayers: usize,

    /// RNN type
    pub rnn_type: RNNType,

    /// Dropout rate
    pub dropout_rate: f64,

    /// Learning rate
    pub learning_rate: f64,

    /// Temperature for sampling
    pub temperature: f64,

    /// Use attention mechanism
    pub use_attention: bool,

    /// Sequence length limit
    pub max_sequence_length: usize,
}

/// RNN types
#[derive(Debug, Clone, Copy)]
pub enum RNNType {
    LSTM,
    GRU,
    RNN,
}

/// RNN layer
#[derive(Debug, Clone)]
pub struct RNNLayer<T: Float + Debug + Send + Sync + 'static> {
    /// Layer type
    layer_type: RNNType,

    /// Input to hidden weights
    weight_ih: Array2<T>,

    /// Hidden to hidden weights
    weight_hh: Array2<T>,

    /// Input to hidden bias
    bias_ih: Array1<T>,

    /// Hidden to hidden bias
    bias_hh: Array1<T>,

    /// Layer normalization (optional)
    layer_norm: Option<LayerNorm<T>>,

    /// Dropout mask
    dropout_mask: Option<Array1<bool>>,
}

/// Layer normalization
#[derive(Debug, Clone)]
pub struct LayerNorm<T: Float + Debug + Send + Sync + 'static> {
    /// Scale parameters
    scale: Array1<T>,

    /// Shift parameters
    shift: Array1<T>,

    /// Epsilon for numerical stability
    eps: T,
}

/// Output projection layer
#[derive(Debug, Clone)]
pub struct OutputLayer<T: Float + Debug + Send + Sync + 'static> {
    /// Weight matrix
    weight: Array2<T>,

    /// Bias vector
    bias: Array1<T>,

    /// Output activation
    activation: ActivationType,
}

/// Activation types
#[derive(Debug, Clone, Copy)]
pub enum ActivationType {
    Linear,
    Softmax,
    Sigmoid,
    Tanh,
    ReLU,
}

/// Embedding layer for discrete inputs
#[derive(Debug, Clone)]
pub struct EmbeddingLayer<T: Float + Debug + Send + Sync + 'static> {
    /// Embedding matrix
    embeddings: Array2<T>,

    /// Vocabulary size
    _vocab_size: usize,

    /// Embedding dimension
    embeddingdim: usize,
}

/// Training batch for controller
#[derive(Debug, Clone)]
pub struct TrainingBatch<T: Float + Debug + Send + Sync + 'static> {
    /// Input sequences
    inputs: Vec<Array1<usize>>,

    /// Target sequences
    targets: Vec<Array1<usize>>,

    /// Rewards
    rewards: Vec<T>,

    /// Sequence lengths
    lengths: Vec<usize>,
}

/// Action space for architecture generation
#[derive(Debug, Clone)]
pub struct ActionSpace {
    /// Available component types
    component_types: Vec<ComponentType>,

    /// Available connections
    connection_types: Vec<String>,

    /// Hyperparameter ranges
    hyperparameter_ranges: HashMap<ComponentType, Vec<(String, f64, f64)>>,

    /// Maximum architecture size
    max_components: usize,
}

/// Transformer-based architecture controller
pub struct TransformerController<T: Float + Debug + Send + Sync + 'static> {
    /// Model configuration
    config: TransformerConfig,

    /// Transformer layers
    layers: Vec<TransformerLayer<T>>,

    /// Positional encoding
    positional_encoding: PositionalEncoding<T>,

    /// Input embedding
    input_embedding: EmbeddingLayer<T>,

    /// Output projection
    output_projection: OutputLayer<T>,

    /// Layer normalization
    layer_norm: LayerNorm<T>,

    /// Search space information
    search_space: Option<SearchSpaceConfig>,

    /// Controller statistics
    statistics: ControllerStatistics<T>,

    /// Generation count
    generation_count: usize,
}

/// Transformer configuration
#[derive(Debug, Clone)]
pub struct TransformerConfig {
    /// Model dimension
    pub _model_dim: usize,

    /// Number of attention heads
    pub numheads: usize,

    /// Number of layers
    pub numlayers: usize,

    /// Feed-forward dimension
    pub ffdim: usize,

    /// Dropout rate
    pub dropout_rate: f64,

    /// Learning rate
    pub learning_rate: f64,

    /// Temperature for sampling
    pub temperature: f64,

    /// Maximum sequence length
    pub max_sequence_length: usize,
}

/// Transformer layer
#[derive(Debug, Clone)]
pub struct TransformerLayer<T: Float + Debug + Send + Sync + 'static> {
    /// Multi-head attention
    attention: MultiHeadAttention<T>,

    /// Feed-forward network
    feed_forward: FeedForward<T>,

    /// Layer normalization for attention
    norm1: LayerNorm<T>,

    /// Layer normalization for feed-forward
    norm2: LayerNorm<T>,

    /// Dropout
    dropout: f64,
}

/// Multi-head attention
#[derive(Debug, Clone)]
pub struct MultiHeadAttention<T: Float + Debug + Send + Sync + 'static> {
    /// Number of heads
    numheads: usize,

    /// Head dimension
    head_dim: usize,

    /// Query projection
    query_proj: Array2<T>,

    /// Key projection
    key_proj: Array2<T>,

    /// Value projection
    value_proj: Array2<T>,

    /// Output projection
    output_proj: Array2<T>,

    /// Attention dropout
    dropout: f64,
}

/// Feed-forward network
#[derive(Debug, Clone)]
pub struct FeedForward<T: Float + Debug + Send + Sync + 'static> {
    /// First linear layer
    linear1: LinearLayer<T>,

    /// Second linear layer
    linear2: LinearLayer<T>,

    /// Activation function
    activation: ActivationType,

    /// Dropout
    dropout: f64,
}

/// Linear layer
#[derive(Debug, Clone)]
pub struct LinearLayer<T: Float + Debug + Send + Sync + 'static> {
    /// Weight matrix
    weight: Array2<T>,

    /// Bias vector
    bias: Array1<T>,
}

/// Positional encoding
#[derive(Debug, Clone)]
pub struct PositionalEncoding<T: Float + Debug + Send + Sync + 'static> {
    /// Encoding matrix
    encoding: Array2<T>,

    /// Maximum sequence length
    max_length: usize,

    /// Model dimension
    _model_dim: usize,
}

/// Random architecture controller (baseline)
pub struct RandomController<T: Float + Debug + Send + Sync + 'static> {
    /// Available component types
    component_types: Vec<ComponentType>,

    /// Search space
    search_space: Option<SearchSpaceConfig>,

    /// Controller statistics
    statistics: ControllerStatistics<T>,

    /// Random number generator seed
    seed: Option<u64>,

    /// Generation count
    generation_count: usize,
}

/// Evolutionary controller using genetic algorithms
pub struct EvolutionaryController<T: Float + Debug + Send + Sync + 'static> {
    /// Population of controllers
    population: Vec<ControllerGenome<T>>,

    /// Population size
    population_size: usize,

    /// Mutation rate
    mutation_rate: f64,

    /// Crossover rate
    crossover_rate: f64,

    /// Elite size
    elite_size: usize,

    /// Search space
    search_space: Option<SearchSpaceConfig>,

    /// Controller statistics
    statistics: ControllerStatistics<T>,

    /// Generation count
    generation_count: usize,
}

/// Controller genome for evolutionary approach
#[derive(Debug, Clone)]
pub struct ControllerGenome<T: Float + Debug + Send + Sync + 'static> {
    /// Genome encoding
    encoding: Vec<T>,

    /// Fitness score
    fitness: T,

    /// Age
    age: usize,

    /// Performance history
    performance_history: Vec<T>,
}

impl<
        T: Float
            + Default
            + Clone
            + Send
            + Sync
            + 'static
            + std::iter::Sum
            + for<'a> std::iter::Sum<&'a T>
            + scirs2_core::ndarray::ScalarOperand,
    > RNNController<T>
{
    /// Create new RNN controller
    pub fn new(hiddensize: usize, numlayers: usize, _vocab_size: usize) -> Result<Self> {
        let config = RNNControllerConfig {
            hiddensize,
            numlayers,
            rnn_type: RNNType::LSTM,
            dropout_rate: 0.1,
            learning_rate: 0.001,
            temperature: 1.0,
            use_attention: false,
            max_sequence_length: 50,
        };

        let mut rnn_layers = Vec::new();
        for i in 0..numlayers {
            let input_size = if i == 0 { hiddensize } else { hiddensize };
            rnn_layers.push(RNNLayer::new(RNNType::LSTM, input_size, hiddensize)?);
        }

        let output_layer = OutputLayer::new(hiddensize, _vocab_size, ActivationType::Softmax)?;
        let embedding_layer = EmbeddingLayer::new(_vocab_size, hiddensize)?;

        let hidden_states = vec![Array1::zeros(hiddensize); numlayers];
        let cell_states = vec![Array1::zeros(hiddensize); numlayers];

        Ok(Self {
            config,
            rnn_layers,
            output_layer,
            hidden_states,
            cell_states,
            embedding_layer,
            search_space: None,
            statistics: ControllerStatistics::default(),
            training_history: VecDeque::new(),
            action_space: ActionSpace::default(),
            generation_count: 0,
        })
    }

    /// Forward pass through RNN
    fn forward(&mut self, inputsequence: &[usize]) -> Result<Array2<T>> {
        let seq_len = inputsequence.len();
        let _vocab_size = self.action_space.component_types.len();
        let mut outputs = Array2::zeros((seq_len, _vocab_size));

        // Reset states at beginning of _sequence
        for state in &mut self.hidden_states {
            state.fill(T::zero());
        }
        for state in &mut self.cell_states {
            state.fill(T::zero());
        }

        for (t, &input_token) in inputsequence.iter().enumerate() {
            // Embed input token
            let embedded = self.embedding_layer.forward(input_token)?;

            // Forward through RNN layers
            let mut layer_input = embedded;
            for (layer_idx, layer) in self.rnn_layers.iter_mut().enumerate() {
                let (hidden_output, cell_output) = layer.forward(
                    &layer_input,
                    &self.hidden_states[layer_idx],
                    &self.cell_states[layer_idx],
                )?;

                self.hidden_states[layer_idx] = hidden_output.clone();
                self.cell_states[layer_idx] = cell_output;
                layer_input = hidden_output;
            }

            // Output projection
            let output = self.output_layer.forward(&layer_input)?;
            outputs.row_mut(t).assign(&output);
        }

        Ok(outputs)
    }

    /// Sample architecture from controller
    fn sample_architecture(&mut self) -> Result<OptimizerArchitecture<T>> {
        let mut components = Vec::new();
        let mut sequence = Vec::new();

        // Start token
        sequence.push(0);

        for _ in 0..self.config.max_sequence_length {
            let logits = self.forward(&sequence)?;
            let last_logits = logits.row(logits.nrows() - 1);

            // Sample from distribution
            let token = self.sample_from_logits(&last_logits.to_owned())?;

            // Check for end token
            if token == 1 {
                // Assuming 1 is end token
                break;
            }

            sequence.push(token);

            // Convert token to component if it represents a component type
            if token >= 2 && token < self.action_space.component_types.len() + 2 {
                let component_type = self.action_space.component_types[token - 2].clone();
                let component = self.create_component_from_type(component_type)?;
                components.push(component);
            }
        }

        if components.is_empty() {
            // Return default architecture if sampling failed
            let default_component = self.create_component_from_type(ComponentType::Adam)?;
            components.push(default_component);
        }

        Ok(OptimizerArchitecture {
            components,
            connections: Vec::new(),
            metadata: HashMap::new(),
        })
    }

    /// Sample from logits using temperature
    fn sample_from_logits(&self, logits: &Array1<T>) -> Result<usize> {
        // Apply temperature scaling
        let scaled_logits = logits.mapv(|x| x / scirs2_core::numeric::NumCast::from(self.config.temperature).unwrap_or_else(|| T::zero()));

        // Softmax
        let max_logit = scaled_logits
            .iter()
            .cloned()
            .fold(T::neg_infinity(), |a, b| if a > b { a } else { b });
        let exp_logits = scaled_logits.mapv(|x| (x - max_logit).exp());
        let sum_exp = exp_logits.sum();
        let probs = exp_logits / sum_exp;

        // Sample from categorical distribution
        let mut rng = scirs2_core::random::thread_rng();
        let rand_val = rng.random::<f64>();
        let mut cumulative = 0.0;

        for (i, &prob) in probs.iter().enumerate() {
            cumulative += prob.to_f64().unwrap_or(0.0);
            if cumulative >= rand_val {
                return Ok(i);
            }
        }

        Ok(probs.len() - 1) // Fallback to last token
    }

    /// Create component from component type
    fn create_component_from_type(
        &self,
        component_type: ComponentType,
    ) -> Result<OptimizerComponent<T>> {
        let mut hyperparameters = HashMap::new();

        // Set default hyperparameters based on component _type
        match component_type {
            ComponentType::Adam => {
                hyperparameters.insert("learning_rate".to_string(), scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()));
                hyperparameters.insert("beta1".to_string(), scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()));
                hyperparameters.insert("beta2".to_string(), scirs2_core::numeric::NumCast::from(0.999).unwrap_or_else(|| T::zero()));
                hyperparameters.insert("epsilon".to_string(), scirs2_core::numeric::NumCast::from(1e-8).unwrap_or_else(|| T::zero()));
            }
            ComponentType::SGD => {
                hyperparameters.insert("learning_rate".to_string(), scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero()));
                hyperparameters.insert("momentum".to_string(), scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()));
                hyperparameters.insert("weight_decay".to_string(), scirs2_core::numeric::NumCast::from(0.0001).unwrap_or_else(|| T::zero()));
            }
            _ => {
                hyperparameters.insert("learning_rate".to_string(), scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()));
            }
        }

        Ok(OptimizerComponent {
            component_type,
            hyperparameters,
            connections: Vec::new(),
        })
    }
}

impl<
        T: Float
            + Default
            + Clone
            + Send
            + Sync
            + 'static
            + std::iter::Sum
            + for<'a> std::iter::Sum<&'a T>
            + scirs2_core::ndarray::ScalarOperand,
    > ArchitectureController<T> for RNNController<T>
{
    fn initialize(&mut self, searchspace: &SearchSpaceConfig) -> Result<()> {
        self.search_space = Some(searchspace.clone());

        // Initialize action _space from search _space
        self.action_space = ActionSpace {
            component_types: searchspace
                .optimizer_components
                .iter()
                .map(|c| c.componenttype.clone())
                .collect(),
            connection_types: vec!["sequential".to_string(), "parallel".to_string()],
            hyperparameter_ranges: HashMap::new(),
            max_components: 10,
        };

        self.generation_count = 0;
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        context: &SearchContext<T>,
    ) -> Result<OptimizerArchitecture<T>> {
        let architecture = self.sample_architecture()?;
        self.generation_count += 1;
        self.statistics.total_generated += 1;
        Ok(architecture)
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        // Create training batch from results
        let mut batch = TrainingBatch {
            inputs: Vec::new(),
            targets: Vec::new(),
            rewards: Vec::new(),
            lengths: Vec::new(),
        };

        for result in results {
            // Extract performance as reward
            let reward = result
                .evaluation_results
                .metric_scores
                .get(&super::EvaluationMetric::FinalPerformance)
                .cloned()
                .unwrap_or(T::zero());

            batch.rewards.push(reward);

            // Simplified: create input sequence from architecture
            let input_seq = self.architecture_to_sequence(&result.architecture)?;
            let seq_len = input_seq.len();
            batch.inputs.push(Array1::from_vec(input_seq.clone()));
            batch.targets.push(Array1::from_vec(input_seq)); // Self-prediction for now
            batch.lengths.push(seq_len);
        }

        self.training_history.push_back(batch);

        // Keep limited history
        if self.training_history.len() > 100 {
            self.training_history.pop_front();
        }

        // Update statistics
        let avg_reward = if !results.is_empty() {
            results
                .iter()
                .filter_map(|r| {
                    r.evaluation_results
                        .metric_scores
                        .get(&super::EvaluationMetric::FinalPerformance)
                })
                .sum::<T>()
                / T::from(results.len()).unwrap()
        } else {
            T::zero()
        };

        self.statistics
            .controller_metrics
            .insert("avg_reward".to_string(), avg_reward);

        Ok(())
    }

    fn get_statistics(&self) -> ControllerStatistics<T> {
        self.statistics.clone()
    }

    fn name(&self) -> &str {
        "RNNController"
    }

    fn reset(&mut self) -> Result<()> {
        for state in &mut self.hidden_states {
            state.fill(T::zero());
        }
        for state in &mut self.cell_states {
            state.fill(T::zero());
        }
        self.generation_count = 0;
        Ok(())
    }
}

impl<
        T: Float
            + Default
            + Clone
            + Send
            + Sync
            + 'static
            + std::iter::Sum
            + for<'a> std::iter::Sum<&'a T>
            + scirs2_core::ndarray::ScalarOperand,
    > RNNController<T>
{
    fn architecture_to_sequence(
        &self,
        architecture: &OptimizerArchitecture<T>,
    ) -> Result<Vec<usize>> {
        let mut sequence = vec![0]; // Start token

        for component in &architecture.components {
            // Find component type index
            if let Some(pos) = self
                .action_space
                .component_types
                .iter()
                .position(|x| *x == component.component_type)
            {
                sequence.push(pos + 2); // Offset by 2 (start and end tokens)
            }
        }

        sequence.push(1); // End token
        Ok(sequence)
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + 'static + scirs2_core::ndarray::ScalarOperand>
    TransformerController<T>
{
    /// Create new Transformer controller
    pub fn new(_model_dim: usize, numheads: usize, numlayers: usize) -> Result<Self> {
        let config = TransformerConfig {
            _model_dim,
            numheads,
            numlayers,
            ffdim: _model_dim * 4,
            dropout_rate: 0.1,
            learning_rate: 0.001,
            temperature: 1.0,
            max_sequence_length: 50,
        };

        let mut layers = Vec::new();
        for _ in 0..numlayers {
            layers.push(TransformerLayer::new(_model_dim, numheads, _model_dim * 4)?);
        }

        let positional_encoding = PositionalEncoding::new(config.max_sequence_length, _model_dim)?;
        let input_embedding = EmbeddingLayer::new(1000, _model_dim)?; // Vocab size placeholder
        let output_projection = OutputLayer::new(_model_dim, 1000, ActivationType::Softmax)?;
        let layer_norm = LayerNorm::new(_model_dim)?;

        Ok(Self {
            config,
            layers,
            positional_encoding,
            input_embedding,
            output_projection,
            layer_norm,
            search_space: None,
            statistics: ControllerStatistics::default(),
            generation_count: 0,
        })
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::iter::Sum> ArchitectureController<T>
    for TransformerController<T>
{
    fn initialize(&mut self, searchspace: &SearchSpaceConfig) -> Result<()> {
        self.search_space = Some(searchspace.clone());
        self.generation_count = 0;
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        context: &SearchContext<T>,
    ) -> Result<OptimizerArchitecture<T>> {
        // Simplified architecture generation for Transformer
        // In practice, this would use the full transformer forward pass
        use super::architecture_space::{ComponentType, OptimizerComponent};

        let component = OptimizerComponent {
            component_type: ComponentType::Adam,
            hyperparameters: {
                let mut params = HashMap::new();
                params.insert("learning_rate".to_string(), scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()));
                params.insert("beta1".to_string(), scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()));
                params.insert("beta2".to_string(), scirs2_core::numeric::NumCast::from(0.999).unwrap_or_else(|| T::zero()));
                params
            },
            connections: Vec::new(),
        };

        self.generation_count += 1;
        self.statistics.total_generated += 1;

        Ok(OptimizerArchitecture {
            components: vec![component],
            connections: Vec::new(),
            metadata: HashMap::new(),
        })
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        // Simplified update for Transformer controller
        Ok(())
    }

    fn get_statistics(&self) -> ControllerStatistics<T> {
        self.statistics.clone()
    }

    fn name(&self) -> &str {
        "TransformerController"
    }

    fn reset(&mut self) -> Result<()> {
        self.generation_count = 0;
        Ok(())
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::iter::Sum> RandomController<T> {
    /// Create new random controller
    pub fn new(_vocabsize: usize) -> Result<Self> {
        Ok(Self {
            component_types: vec![
                ComponentType::Adam,
                ComponentType::SGD,
                ComponentType::AdaGrad,
            ],
            search_space: None,
            statistics: ControllerStatistics::default(),
            seed: None,
            generation_count: 0,
        })
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::iter::Sum> ArchitectureController<T>
    for RandomController<T>
{
    fn initialize(&mut self, searchspace: &SearchSpaceConfig) -> Result<()> {
        self.search_space = Some(searchspace.clone());
        self.component_types = searchspace
            .optimizer_components
            .iter()
            .map(|c| c.componenttype.clone())
            .collect();
        self.generation_count = 0;
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        context: &SearchContext<T>,
    ) -> Result<OptimizerArchitecture<T>> {
        use super::architecture_space::{ComponentType, OptimizerComponent};

        // Random selection of component type
        let mut rng = scirs2_core::random::thread_rng();
        let component_type =
            self.component_types[rng.gen_range(0..self.component_types.len())].clone();

        let mut hyperparameters = HashMap::new();
        match component_type {
            ComponentType::Adam => {
                hyperparameters.insert(
                    "learning_rate".to_string(),
                    T::from(rng.random::<f64>() * 0.01).unwrap(),
                );
                hyperparameters.insert(
                    "beta1".to_string(),
                    T::from(0.8 + rng.random::<f64>() * 0.19).unwrap(),
                );
                hyperparameters.insert(
                    "beta2".to_string(),
                    T::from(0.9 + rng.random::<f64>() * 0.099).unwrap(),
                );
            }
            ComponentType::SGD => {
                hyperparameters.insert(
                    "learning_rate".to_string(),
                    T::from(rng.random::<f64>() * 0.1).unwrap(),
                );
                hyperparameters.insert(
                    "momentum".to_string(),
                    T::from(rng.random::<f64>() * 0.99).unwrap(),
                );
            }
            _ => {
                hyperparameters.insert(
                    "learning_rate".to_string(),
                    T::from(rng.random::<f64>() * 0.01).unwrap(),
                );
            }
        }

        let component = OptimizerComponent {
            component_type,
            hyperparameters,
            connections: Vec::new(),
        };

        self.generation_count += 1;
        self.statistics.total_generated += 1;

        Ok(OptimizerArchitecture {
            components: vec![component],
            connections: Vec::new(),
            metadata: HashMap::new(),
        })
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        // Random controller doesn't learn from _results
        Ok(())
    }

    fn get_statistics(&self) -> ControllerStatistics<T> {
        self.statistics.clone()
    }

    fn name(&self) -> &str {
        "RandomController"
    }

    fn reset(&mut self) -> Result<()> {
        self.generation_count = 0;
        Ok(())
    }
}

// Implementation helpers for layers
impl<T: Float + Debug + Default + Clone + 'static + scirs2_core::ndarray::ScalarOperand + Send + Sync> RNNLayer<T> {
    fn new(layer_type: RNNType, input_size: usize, hiddensize: usize) -> Result<Self> {
        let gate_size = match layer_type {
            RNNType::LSTM => hiddensize * 4,
            RNNType::GRU => hiddensize * 3,
            RNNType::RNN => hiddensize,
        };

        Ok(Self {
            layer_type,
            weight_ih: Array2::zeros((gate_size, input_size)),
            weight_hh: Array2::zeros((gate_size, hiddensize)),
            bias_ih: Array1::zeros(gate_size),
            bias_hh: Array1::zeros(gate_size),
            layer_norm: None,
            dropout_mask: None,
        })
    }

    fn forward(
        &self,
        input: &Array1<T>,
        hidden: &Array1<T>,
        cell: &Array1<T>,
    ) -> Result<(Array1<T>, Array1<T>)> {
        match self.layer_type {
            RNNType::LSTM => self.lstm_forward(input, hidden, cell),
            RNNType::GRU => self.gru_forward(input, hidden),
            RNNType::RNN => self.rnn_forward(input, hidden),
        }
    }

    fn lstm_forward(
        &self,
        input: &Array1<T>,
        hidden: &Array1<T>,
        cell: &Array1<T>,
    ) -> Result<(Array1<T>, Array1<T>)> {
        let hiddensize = hidden.len();

        let gi = self.weight_ih.dot(input) + &self.bias_ih;
        let gh = self.weight_hh.dot(hidden) + &self.bias_hh;
        let combined = gi + gh;

        // Split gates
        let input_gate = Self::sigmoid(&combined.slice(s![0..hiddensize]).to_owned());
        let forget_gate = Self::sigmoid(&combined.slice(s![hiddensize..2 * hiddensize]).to_owned());
        let cell_gate = Self::tanh(
            &combined
                .slice(s![2 * hiddensize..3 * hiddensize])
                .to_owned(),
        );
        let output_gate = Self::sigmoid(
            &combined
                .slice(s![3 * hiddensize..4 * hiddensize])
                .to_owned(),
        );

        let new_cell = &forget_gate * cell + &input_gate * &cell_gate;
        let new_hidden = &output_gate * &Self::tanh(&new_cell);

        Ok((new_hidden, new_cell))
    }

    fn gru_forward(&self, input: &Array1<T>, hidden: &Array1<T>) -> Result<(Array1<T>, Array1<T>)> {
        let hiddensize = hidden.len();

        let gi = self.weight_ih.dot(input) + &self.bias_ih;
        let gh = self.weight_hh.dot(hidden) + &self.bias_hh;

        let reset_gate = Self::sigmoid(
            &(gi.slice(s![0..hiddensize]).to_owned() + gh.slice(s![0..hiddensize]).to_owned()),
        );
        let update_gate = Self::sigmoid(
            &(gi.slice(s![hiddensize..2 * hiddensize]).to_owned()
                + gh.slice(s![hiddensize..2 * hiddensize]).to_owned()),
        );

        let new_gate = Self::tanh(
            &(gi.slice(s![2 * hiddensize..3 * hiddensize]).to_owned()
                + &reset_gate * &gh.slice(s![2 * hiddensize..3 * hiddensize]).to_owned()),
        );

        let one_minus_update = update_gate.mapv(|x| T::one() - x);
        let new_hidden = &update_gate * hidden + &one_minus_update * &new_gate;

        Ok((new_hidden.clone(), new_hidden))
    }

    fn rnn_forward(&self, input: &Array1<T>, hidden: &Array1<T>) -> Result<(Array1<T>, Array1<T>)> {
        let gi = self.weight_ih.dot(input) + &self.bias_ih;
        let gh = self.weight_hh.dot(hidden) + &self.bias_hh;
        let new_hidden = Self::tanh(&(gi + gh));

        Ok((new_hidden.clone(), new_hidden))
    }

    fn sigmoid(x: &Array1<T>) -> Array1<T> {
        x.mapv(|xi| T::one() / (T::one() + (-xi).exp()))
    }

    fn tanh(x: &Array1<T>) -> Array1<T> {
        x.mapv(|xi| xi.tanh())
    }
}

impl<T: Float + Debug + Default + Clone + 'static + scirs2_core::ndarray::ScalarOperand + Send + Sync> OutputLayer<T> {
    fn new(input_size: usize, outputsize: usize, activation: ActivationType) -> Result<Self> {
        Ok(Self {
            weight: Array2::zeros((outputsize, input_size)),
            bias: Array1::zeros(outputsize),
            activation,
        })
    }

    fn forward(&self, input: &Array1<T>) -> Result<Array1<T>> {
        let output = self.weight.dot(input) + &self.bias;

        match self.activation {
            ActivationType::Linear => Ok(output),
            ActivationType::Softmax => Ok(Self::softmax(&output)),
            ActivationType::Sigmoid => Ok(Self::sigmoid(&output)),
            ActivationType::Tanh => Ok(output.mapv(|x| x.tanh())),
            ActivationType::ReLU => Ok(output.mapv(|x| if x > T::zero() { x } else { T::zero() })),
        }
    }

    fn softmax(x: &Array1<T>) -> Array1<T> {
        let max_val = x
            .iter()
            .cloned()
            .fold(T::neg_infinity(), |a, b| if a > b { a } else { b });
        let exp_x = x.mapv(|xi| (xi - max_val).exp());
        let sum_exp = exp_x.sum();
        exp_x / sum_exp
    }

    fn sigmoid(x: &Array1<T>) -> Array1<T> {
        x.mapv(|xi| T::one() / (T::one() + (-xi).exp()))
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> EmbeddingLayer<T> {
    fn new(_vocab_size: usize, embeddingdim: usize) -> Result<Self> {
        Ok(Self {
            embeddings: Array2::zeros((_vocab_size, embeddingdim)),
            _vocab_size,
            embeddingdim,
        })
    }

    fn forward(&self, token: usize) -> Result<Array1<T>> {
        if token >= self._vocab_size {
            return Err(OptimError::InvalidConfig(format!(
                "Token {} out of vocabulary",
                token
            )));
        }

        Ok(self.embeddings.row(token).to_owned())
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> TransformerLayer<T> {
    fn new(_model_dim: usize, numheads: usize, ffdim: usize) -> Result<Self> {
        Ok(Self {
            attention: MultiHeadAttention::new(_model_dim, numheads)?,
            feed_forward: FeedForward::new(_model_dim, ffdim)?,
            norm1: LayerNorm::new(_model_dim)?,
            norm2: LayerNorm::new(_model_dim)?,
            dropout: 0.1,
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> MultiHeadAttention<T> {
    fn new(_model_dim: usize, numheads: usize) -> Result<Self> {
        let head_dim = _model_dim / numheads;

        Ok(Self {
            numheads,
            head_dim,
            query_proj: Array2::zeros((_model_dim, _model_dim)),
            key_proj: Array2::zeros((_model_dim, _model_dim)),
            value_proj: Array2::zeros((_model_dim, _model_dim)),
            output_proj: Array2::zeros((_model_dim, _model_dim)),
            dropout: 0.1,
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> FeedForward<T> {
    fn new(input_dim: usize, ffdim: usize) -> Result<Self> {
        Ok(Self {
            linear1: LinearLayer::new(input_dim, ffdim)?,
            linear2: LinearLayer::new(ffdim, input_dim)?,
            activation: ActivationType::ReLU,
            dropout: 0.1,
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> LinearLayer<T> {
    fn new(input_dim: usize, outputdim: usize) -> Result<Self> {
        Ok(Self {
            weight: Array2::zeros((outputdim, input_dim)),
            bias: Array1::zeros(outputdim),
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> LayerNorm<T> {
    fn new(dim: usize) -> Result<Self> {
        Ok(Self {
            scale: Array1::ones(dim),
            shift: Array1::zeros(dim),
            eps: scirs2_core::numeric::NumCast::from(1e-6).unwrap_or_else(|| T::zero()),
        })
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> PositionalEncoding<T> {
    fn new(max_length: usize, _model_dim: usize) -> Result<Self> {
        let mut encoding = Array2::zeros((max_length, _model_dim));

        for pos in 0..max_length {
            for i in 0.._model_dim / 2 {
                let angle = pos as f64 / 10000_f64.powf(2.0 * i as f64 / _model_dim as f64);
                encoding[[pos, 2 * i]] = T::from(angle.sin()).unwrap();
                encoding[[pos, 2 * i + 1]] = T::from(angle.cos()).unwrap();
            }
        }

        Ok(Self {
            encoding,
            max_length,
            _model_dim,
        })
    }
}

impl Default for ActionSpace {
    fn default() -> Self {
        Self {
            component_types: vec![
                ComponentType::Adam,
                ComponentType::SGD,
                ComponentType::AdaGrad,
            ],
            connection_types: vec!["sequential".to_string(), "parallel".to_string()],
            hyperparameter_ranges: HashMap::new(),
            max_components: 10,
        }
    }
}

impl<T: Float + Debug + Default + Send + Sync> Default for ControllerStatistics<T> {
    fn default() -> Self {
        Self {
            total_generated: 0,
            success_rate: 0.0,
            avg_generation_time_ms: 0.0,
            diversity_score: T::zero(),
            controller_metrics: HashMap::new(),
        }
    }
}

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

    #[test]
    fn test_rnn_controller_creation() {
        let controller = RNNController::<f64>::new(256, 2, 100);
        assert!(controller.is_ok());

        let controller = controller.unwrap();
        assert_eq!(controller.name(), "RNNController");
    }

    #[test]
    fn test_transformer_controller_creation() {
        let controller = TransformerController::<f64>::new(512, 8, 4);
        assert!(controller.is_ok());

        let controller = controller.unwrap();
        assert_eq!(controller.name(), "TransformerController");
    }

    #[test]
    fn test_random_controller_creation() {
        let controller = RandomController::<f64>::new(100);
        assert!(controller.is_ok());

        let controller = controller.unwrap();
        assert_eq!(controller.name(), "RandomController");
    }
}
