//! Few-Shot Learning Enhancement for Optimization
//!
//! This module implements few-shot learning capabilities that allow optimizers
//! to quickly adapt to new optimization problems with minimal training data.
//! The system leverages meta-learning and rapid adaptation techniques.

use super::{
    ActivationType, LearnedOptimizationConfig, LearnedOptimizer, MetaOptimizerState,
    OptimizationProblem, TrainingTask,
};
use crate::error::OptimizeResult;
use crate::result::OptimizeResults;
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView1};
use scirs2_core::random::{Rng, RngExt};
use std::collections::{HashMap, VecDeque};

/// Few-Shot Learning Optimizer with Rapid Adaptation
#[derive(Debug, Clone)]
pub struct FewShotLearningOptimizer {
    /// Configuration
    config: LearnedOptimizationConfig,
    /// Meta-learner network
    meta_learner: MetaLearnerNetwork,
    /// Fast adaptation mechanism
    fast_adapter: FastAdaptationMechanism,
    /// Problem similarity matcher
    similarity_matcher: ProblemSimilarityMatcher,
    /// Experience memory
    experience_memory: ExperienceMemory,
    /// Meta-optimizer state
    meta_state: MetaOptimizerState,
    /// Adaptation statistics
    adaptation_stats: FewShotAdaptationStats,
    /// Current task context
    current_task_context: Option<TaskContext>,
}

/// Meta-learner network for few-shot optimization
#[derive(Debug, Clone)]
pub struct MetaLearnerNetwork {
    /// Feature extractor
    feature_extractor: FeatureExtractor,
    /// Context encoder
    context_encoder: ContextEncoder,
    /// Parameter generator
    parameter_generator: ParameterGenerator,
    /// Update network
    update_network: UpdateNetwork,
    /// Memory networks
    memory_networks: Vec<MemoryNetwork>,
}

/// Feature extractor for optimization problems
#[derive(Debug, Clone)]
pub struct FeatureExtractor {
    /// Convolutional layers for structured features
    conv_layers: Vec<ConvLayer>,
    /// Dense layers for feature processing
    dense_layers: Vec<DenseLayer>,
    /// Attention mechanism for feature selection
    attention_mechanism: FeatureAttention,
    /// Feature dimension
    feature_dim: usize,
}

/// Convolutional layer
#[derive(Debug, Clone)]
pub struct ConvLayer {
    /// Weights
    weights: Array3<f64>,
    /// Bias
    bias: Array1<f64>,
    /// Kernel size
    kernel_size: usize,
    /// Stride
    stride: usize,
    /// Activation
    activation: ActivationType,
}

/// Dense layer
#[derive(Debug, Clone)]
pub struct DenseLayer {
    /// Weights
    weights: Array2<f64>,
    /// Bias
    bias: Array1<f64>,
    /// Activation
    activation: ActivationType,
}

/// Feature attention mechanism
#[derive(Debug, Clone)]
pub struct FeatureAttention {
    /// Query weights
    query_weights: Array2<f64>,
    /// Key weights
    key_weights: Array2<f64>,
    /// Value weights
    value_weights: Array2<f64>,
    /// Attention scores
    attention_scores: Array1<f64>,
}

/// Context encoder for task understanding
#[derive(Debug, Clone)]
pub struct ContextEncoder {
    /// LSTM for sequential context
    lstm: LSTMCell,
    /// Embedding layer for discrete features
    embedding_layer: Array2<f64>,
    /// Context aggregation network
    aggregation_network: Array2<f64>,
    /// Context dimension
    context_dim: usize,
}

/// LSTM cell
#[derive(Debug, Clone)]
pub struct LSTMCell {
    /// Input gate weights
    w_i: Array2<f64>,
    /// Forget gate weights
    w_f: Array2<f64>,
    /// Cell gate weights
    w_c: Array2<f64>,
    /// Output gate weights
    w_o: Array2<f64>,
    /// Hidden state
    hidden_state: Array1<f64>,
    /// Cell state
    cell_state: Array1<f64>,
}

/// Parameter generator for optimization strategies
#[derive(Debug, Clone)]
pub struct ParameterGenerator {
    /// Generator network
    generator_network: Array2<f64>,
    /// Conditioning network
    conditioning_network: Array2<f64>,
    /// Output projection
    output_projection: Array2<f64>,
    /// Generated parameter dimension
    param_dim: usize,
}

/// Update network for parameter adaptation
#[derive(Debug, Clone)]
pub struct UpdateNetwork {
    /// Update computation network
    update_network: Array2<f64>,
    /// Meta-gradient network
    meta_gradient_network: Array2<f64>,
    /// Learning rate network
    lr_network: Array2<f64>,
    /// Update history
    update_history: VecDeque<Array1<f64>>,
}

/// Memory network for storing optimization patterns
#[derive(Debug, Clone)]
pub struct MemoryNetwork {
    /// Memory bank
    memory_bank: Array2<f64>,
    /// Memory keys
    memory_keys: Array2<f64>,
    /// Memory values
    memory_values: Array2<f64>,
    /// Access patterns
    access_patterns: Vec<Array1<f64>>,
    /// Memory size
    memory_size: usize,
}

/// Fast adaptation mechanism
#[derive(Debug, Clone)]
pub struct FastAdaptationMechanism {
    /// Gradient-based adaptation
    gradient_adapter: GradientBasedAdapter,
    /// Prototype-based adaptation
    prototype_adapter: PrototypeBasedAdapter,
    /// Model-agnostic meta-learning (MAML)
    maml_adapter: MAMLAdapter,
    /// Adaptation strategy selector
    strategy_selector: AdaptationStrategySelector,
}

/// Gradient-based adaptation
#[derive(Debug, Clone)]
pub struct GradientBasedAdapter {
    /// Meta-learning rate
    meta_lr: f64,
    /// Inner learning rate
    inner_lr: f64,
    /// Number of adaptation steps
    adaptation_steps: usize,
    /// Gradient accumulator
    gradient_accumulator: Array1<f64>,
}

/// Prototype-based adaptation
#[derive(Debug, Clone)]
pub struct PrototypeBasedAdapter {
    /// Prototype embeddings
    prototypes: Array2<f64>,
    /// Prototype labels
    prototype_labels: Vec<String>,
    /// Distance metric
    distance_metric: DistanceMetric,
    /// Adaptation weights
    adaptation_weights: Array1<f64>,
}

/// Distance metrics for prototype matching
#[derive(Debug, Clone)]
pub enum DistanceMetric {
    Euclidean,
    Cosine,
    Mahalanobis { covariance_inv: Array2<f64> },
    Learned { distance_network: Array2<f64> },
}

/// Model-Agnostic Meta-Learning adapter
#[derive(Debug, Clone)]
pub struct MAMLAdapter {
    /// Meta-parameters
    meta_parameters: Array1<f64>,
    /// Task-specific parameters
    task_parameters: HashMap<String, Array1<f64>>,
    /// Inner loop optimizer
    inner_optimizer: InnerLoopOptimizer,
    /// Meta-optimizer
    meta_optimizer: MetaOptimizer,
}

/// Inner loop optimizer for MAML
#[derive(Debug, Clone)]
pub struct InnerLoopOptimizer {
    /// Learning rate
    learning_rate: f64,
    /// Momentum
    momentum: f64,
    /// Velocity
    velocity: Array1<f64>,
}

/// Meta-optimizer for MAML
#[derive(Debug, Clone)]
pub struct MetaOptimizer {
    /// Meta-learning rate
    meta_learning_rate: f64,
    /// Meta-momentum
    meta_momentum: f64,
    /// Meta-velocity
    meta_velocity: Array1<f64>,
}

/// Strategy selector for adaptation methods
#[derive(Debug, Clone)]
pub struct AdaptationStrategySelector {
    /// Strategy scores
    strategy_scores: HashMap<String, f64>,
    /// Selection network
    selection_network: Array2<f64>,
    /// Current strategy
    current_strategy: AdaptationStrategy,
}

/// Types of adaptation strategies
#[derive(Debug, Clone)]
pub enum AdaptationStrategy {
    Gradient,
    Prototype,
    MAML,
    Hybrid { weights: Array1<f64> },
}

/// Problem similarity matcher
#[derive(Debug, Clone)]
pub struct ProblemSimilarityMatcher {
    /// Problem embeddings
    problem_embeddings: HashMap<String, Array1<f64>>,
    /// Similarity computation network
    similarity_network: Array2<f64>,
    /// Similarity threshold
    similarity_threshold: f64,
    /// Cached similarities
    similarity_cache: HashMap<(String, String), f64>,
}

/// Experience memory for few-shot learning
#[derive(Debug, Clone)]
pub struct ExperienceMemory {
    /// Support set examples
    support_set: Vec<SupportExample>,
    /// Query set examples
    query_set: Vec<QueryExample>,
    /// Memory capacity
    capacity: usize,
    /// Episodic memory
    episodic_memory: VecDeque<Episode>,
}

/// Support example for few-shot learning
#[derive(Debug, Clone)]
pub struct SupportExample {
    /// Problem encoding
    problem_encoding: Array1<f64>,
    /// Optimization trajectory
    trajectory: OptimizationTrajectory,
    /// Success indicator
    success: bool,
    /// Problem metadata
    metadata: HashMap<String, f64>,
}

/// Query example for evaluation
#[derive(Debug, Clone)]
pub struct QueryExample {
    /// Problem encoding
    problem_encoding: Array1<f64>,
    /// Target optimization strategy
    target_strategy: Array1<f64>,
    /// Expected performance
    expected_performance: f64,
}

/// Episode in episodic memory
#[derive(Debug, Clone)]
pub struct Episode {
    /// Task identifier
    task_id: String,
    /// Support examples
    support_examples: Vec<SupportExample>,
    /// Query examples
    query_examples: Vec<QueryExample>,
    /// Adaptation performance
    adaptation_performance: f64,
    /// Episode timestamp
    timestamp: usize,
}

/// Optimization trajectory
#[derive(Debug, Clone)]
pub struct OptimizationTrajectory {
    /// Parameter history
    parameter_history: Vec<Array1<f64>>,
    /// Objective history
    objective_history: Vec<f64>,
    /// Gradient history
    gradient_history: Vec<Array1<f64>>,
    /// Step size history
    step_size_history: Vec<f64>,
    /// Total steps
    total_steps: usize,
}

/// Few-shot adaptation statistics
#[derive(Debug, Clone)]
pub struct FewShotAdaptationStats {
    /// Adaptation speed (steps to convergence)
    adaptation_speed: f64,
    /// Transfer efficiency
    transfer_efficiency: f64,
    /// Number of support examples used
    support_examples_used: usize,
    /// Adaptation success rate
    adaptation_success_rate: f64,
    /// Meta-learning progress
    meta_learning_progress: f64,
}

/// Task context for current optimization
#[derive(Debug, Clone)]
pub struct TaskContext {
    /// Task description
    task_description: String,
    /// Problem characteristics
    problem_characteristics: Array1<f64>,
    /// Available support examples
    available_support: Vec<String>,
    /// Adaptation budget
    adaptation_budget: usize,
    /// Performance target
    performance_target: f64,
}

impl FewShotLearningOptimizer {
    /// Create new few-shot learning optimizer
    pub fn new(config: LearnedOptimizationConfig) -> Self {
        let feature_dim = config.hidden_size;
        let meta_learner = MetaLearnerNetwork::new(feature_dim);
        let fast_adapter = FastAdaptationMechanism::new(config.inner_learning_rate);
        let similarity_matcher = ProblemSimilarityMatcher::new(feature_dim);
        let experience_memory = ExperienceMemory::new(1000);

        Self {
            config,
            meta_learner,
            fast_adapter,
            similarity_matcher,
            experience_memory,
            meta_state: MetaOptimizerState {
                meta_params: Array1::zeros(feature_dim),
                network_weights: Array2::zeros((feature_dim, feature_dim)),
                performance_history: Vec::new(),
                adaptation_stats: super::AdaptationStatistics::default(),
                episode: 0,
            },
            adaptation_stats: FewShotAdaptationStats::default(),
            current_task_context: None,
        }
    }

    /// Perform few-shot adaptation to new problem
    pub fn few_shot_adapt(
        &mut self,
        support_examples: &[SupportExample],
        target_problem: &OptimizationProblem,
    ) -> OptimizeResult<()> {
        // Extract features from support _examples
        let support_features = self.extract_support_features(support_examples)?;

        // Find similar problems in memory
        let similar_problems = self.find_similar_problems(target_problem)?;

        // Select adaptation strategy
        let strategy = self.select_adaptation_strategy(&support_features, &similar_problems)?;

        // Perform adaptation based on selected strategy
        match strategy {
            AdaptationStrategy::Gradient => {
                self.gradient_based_adaptation(support_examples)?;
            }
            AdaptationStrategy::Prototype => {
                self.prototype_based_adaptation(support_examples)?;
            }
            AdaptationStrategy::MAML => {
                self.maml_adaptation(support_examples)?;
            }
            AdaptationStrategy::Hybrid { weights } => {
                self.hybrid_adaptation(support_examples, &weights)?;
            }
        }

        // Update adaptation statistics
        self.update_adaptation_stats(support_examples.len())?;

        Ok(())
    }

    /// Extract features from support examples
    fn extract_support_features(
        &self,
        support_examples: &[SupportExample],
    ) -> OptimizeResult<Array2<f64>> {
        let num_examples = support_examples.len();
        let feature_dim = self.meta_learner.feature_extractor.feature_dim;
        let mut features = Array2::zeros((num_examples, feature_dim));

        for (i, example) in support_examples.iter().enumerate() {
            let extracted_features = self
                .meta_learner
                .feature_extractor
                .extract(&example.problem_encoding)?;
            for j in 0..feature_dim.min(extracted_features.len()) {
                features[[i, j]] = extracted_features[j];
            }
        }

        Ok(features)
    }

    /// Find similar problems in experience memory
    fn find_similar_problems(
        &self,
        target_problem: &OptimizationProblem,
    ) -> OptimizeResult<Vec<String>> {
        let target_encoding = self.encode_problem_for_similarity(target_problem)?;
        let mut similarities = Vec::new();

        for (problem_id, problem_embedding) in &self.similarity_matcher.problem_embeddings {
            let similarity = self.compute_similarity(&target_encoding, problem_embedding)?;
            if similarity > self.similarity_matcher.similarity_threshold {
                similarities.push((problem_id.clone(), similarity));
            }
        }

        // Sort by similarity and return top matches
        similarities.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        Ok(similarities.into_iter().take(5).map(|(id, _)| id).collect())
    }

    /// Encode problem for similarity matching
    fn encode_problem_for_similarity(
        &self,
        problem: &OptimizationProblem,
    ) -> OptimizeResult<Array1<f64>> {
        let mut encoding = Array1::zeros(self.meta_learner.feature_extractor.feature_dim);

        // Basic encoding (in practice would be more sophisticated)
        encoding[0] = (problem.dimension as f64).ln();
        encoding[1] = (problem.max_evaluations as f64).ln();
        encoding[2] = problem.target_accuracy.ln().abs();

        // Encode problem class
        match problem.problem_class.as_str() {
            "quadratic" => encoding[3] = 1.0,
            "neural_network" => encoding[4] = 1.0,
            "sparse" => {
                encoding[5] = 1.0;
                encoding[6] = 1.0;
            }
            _ => {} // Default case for unknown problem classes
        }

        Ok(encoding)
    }

    /// Compute similarity between problem encodings
    fn compute_similarity(
        &self,
        encoding1: &Array1<f64>,
        encoding2: &Array1<f64>,
    ) -> OptimizeResult<f64> {
        // Cosine similarity
        let dot_product = encoding1
            .iter()
            .zip(encoding2.iter())
            .map(|(&a, &b)| a * b)
            .sum::<f64>();

        let norm1 = (encoding1.iter().map(|&x| x * x).sum::<f64>()).sqrt();
        let norm2 = (encoding2.iter().map(|&x| x * x).sum::<f64>()).sqrt();

        if norm1 > 0.0 && norm2 > 0.0 {
            Ok(dot_product / (norm1 * norm2))
        } else {
            Ok(0.0)
        }
    }

    /// Select adaptation strategy
    fn select_adaptation_strategy(
        &self,
        support_features: &Array2<f64>,
        similar_problems: &[String],
    ) -> OptimizeResult<AdaptationStrategy> {
        // Simple heuristic-based selection (in practice would use learned selector)
        let num_support = support_features.nrows();
        let num_similar = similar_problems.len();

        if num_support <= 2 {
            Ok(AdaptationStrategy::Prototype)
        } else if num_similar > 3 {
            Ok(AdaptationStrategy::MAML)
        } else if num_support > 10 {
            Ok(AdaptationStrategy::Gradient)
        } else {
            Ok(AdaptationStrategy::Hybrid {
                weights: Array1::from(vec![0.3, 0.4, 0.3]),
            })
        }
    }

    /// Gradient-based adaptation
    fn gradient_based_adaptation(
        &mut self,
        support_examples: &[SupportExample],
    ) -> OptimizeResult<()> {
        // First compute all meta-gradients
        let all_gradients: Result<Vec<_>, _> = support_examples
            .iter()
            .map(|example| self.compute_meta_gradients(example))
            .collect();
        let all_gradients = all_gradients?;

        // Now update the adapter with all computed gradients
        let adapter = &mut self.fast_adapter.gradient_adapter;
        for meta_gradients in all_gradients {
            // Update adaptation parameters
            for (i, &grad) in meta_gradients.iter().enumerate() {
                if i < adapter.gradient_accumulator.len() {
                    adapter.gradient_accumulator[i] += adapter.meta_lr * grad;
                }
            }
        }

        Ok(())
    }

    /// Prototype-based adaptation
    fn prototype_based_adaptation(
        &mut self,
        support_examples: &[SupportExample],
    ) -> OptimizeResult<()> {
        let adapter = &mut self.fast_adapter.prototype_adapter;

        // Update prototypes based on support _examples
        for (i, example) in support_examples.iter().enumerate() {
            if i < adapter.prototypes.nrows() {
                for (j, &feature) in example.problem_encoding.iter().enumerate() {
                    if j < adapter.prototypes.ncols() {
                        adapter.prototypes[[i, j]] =
                            0.9 * adapter.prototypes[[i, j]] + 0.1 * feature;
                    }
                }
            }
        }

        Ok(())
    }

    /// MAML adaptation
    fn maml_adaptation(&mut self, support_examples: &[SupportExample]) -> OptimizeResult<()> {
        let adapter = &mut self.fast_adapter.maml_adapter;

        for example in support_examples {
            // Inner loop update
            let inner_gradients = self.compute_inner_gradients(example)?;
            self.apply_inner_update(&inner_gradients)?;

            // Meta-gradients computation would happen during meta-training
        }

        Ok(())
    }

    /// Hybrid adaptation combining multiple strategies
    fn hybrid_adaptation(
        &mut self,
        support_examples: &[SupportExample],
        weights: &Array1<f64>,
    ) -> OptimizeResult<()> {
        if weights.len() >= 3 {
            // Apply weighted combination of strategies
            if weights[0] > 0.0 {
                self.gradient_based_adaptation(support_examples)?;
            }
            if weights[1] > 0.0 {
                self.prototype_based_adaptation(support_examples)?;
            }
            if weights[2] > 0.0 {
                self.maml_adaptation(support_examples)?;
            }
        }

        Ok(())
    }

    /// Compute meta-gradients from example
    fn compute_meta_gradients(&self, example: &SupportExample) -> OptimizeResult<Array1<f64>> {
        // Simplified meta-gradient computation
        let mut gradients = Array1::zeros(self.meta_state.meta_params.len());

        // Use trajectory information to estimate gradients
        if !example.trajectory.objective_history.is_empty() {
            let improvement = example
                .trajectory
                .objective_history
                .first()
                .copied()
                .unwrap_or(0.0)
                - example
                    .trajectory
                    .objective_history
                    .last()
                    .copied()
                    .unwrap_or(0.0);

            // Simple gradient estimation based on improvement
            for i in 0..gradients.len() {
                gradients[i] =
                    improvement * (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.01;
            }
        }

        Ok(gradients)
    }

    /// Compute inner gradients for MAML
    fn compute_inner_gradients(&self, example: &SupportExample) -> OptimizeResult<Array1<f64>> {
        // Simplified inner gradient computation
        let mut gradients = Array1::zeros(self.meta_state.meta_params.len());

        if !example.trajectory.gradient_history.is_empty() {
            if let Some(last_gradient) = example.trajectory.gradient_history.last() {
                for (i, &grad) in last_gradient.iter().enumerate() {
                    if i < gradients.len() {
                        gradients[i] = grad * 0.1; // Scale down
                    }
                }
            }
        }

        Ok(gradients)
    }

    /// Apply inner loop update
    fn apply_inner_update(&mut self, gradients: &Array1<f64>) -> OptimizeResult<()> {
        let lr = self.fast_adapter.maml_adapter.inner_optimizer.learning_rate;

        for (i, &grad) in gradients.iter().enumerate() {
            if i < self.meta_state.meta_params.len() {
                self.meta_state.meta_params[i] -= lr * grad;
            }
        }

        Ok(())
    }

    /// Update adaptation statistics
    fn update_adaptation_stats(&mut self, num_support_examples: usize) -> OptimizeResult<()> {
        self.adaptation_stats.support_examples_used = num_support_examples;
        self.adaptation_stats.adaptation_speed = 1.0 / (num_support_examples as f64 + 1.0);
        self.adaptation_stats.transfer_efficiency = if num_support_examples > 0 {
            1.0 / num_support_examples as f64
        } else {
            0.0
        };

        Ok(())
    }

    /// Generate optimization strategy from few-shot adaptation
    pub fn generate_optimization_strategy(
        &self,
        problem: &OptimizationProblem,
    ) -> OptimizeResult<OptimizationStrategy> {
        // Extract problem features
        let problem_encoding = self.encode_problem_for_similarity(problem)?;

        // Generate strategy using meta-learner
        let strategy_params = self
            .meta_learner
            .parameter_generator
            .generate(&problem_encoding)?;

        Ok(OptimizationStrategy {
            step_size_schedule: self.generate_step_size_schedule(&strategy_params)?,
            direction_computation: DirectionComputation::GradientBased {
                momentum: strategy_params.get(0).copied().unwrap_or(0.9),
            },
            convergence_criteria: ConvergenceCriteria {
                tolerance: strategy_params.get(1).copied().unwrap_or(1e-6),
                max_nit: problem.max_evaluations,
            },
            adaptation_rate: strategy_params.get(2).copied().unwrap_or(0.01),
        })
    }

    /// Generate step size schedule
    fn generate_step_size_schedule(
        &self,
        strategy_params: &Array1<f64>,
    ) -> OptimizeResult<StepSizeSchedule> {
        let initial_step = strategy_params.get(3).copied().unwrap_or(0.01);
        let decay_rate = strategy_params.get(4).copied().unwrap_or(0.99);

        Ok(StepSizeSchedule::Exponential {
            initial_step,
            decay_rate,
        })
    }

    /// Get adaptation statistics
    pub fn get_adaptation_stats(&self) -> &FewShotAdaptationStats {
        &self.adaptation_stats
    }

    /// Add experience to memory
    pub fn add_experience(
        &mut self,
        problem: &OptimizationProblem,
        trajectory: OptimizationTrajectory,
    ) {
        let problem_encoding = self
            .encode_problem_for_similarity(problem)
            .unwrap_or_default();

        let support_example = SupportExample {
            problem_encoding,
            trajectory,
            success: true, // Determine based on trajectory
            metadata: HashMap::new(),
        };

        self.experience_memory.add_support_example(support_example);
    }
}

/// Generated optimization strategy
#[derive(Debug, Clone)]
pub struct OptimizationStrategy {
    /// Step size schedule
    pub step_size_schedule: StepSizeSchedule,
    /// Direction computation method
    pub direction_computation: DirectionComputation,
    /// Convergence criteria
    pub convergence_criteria: ConvergenceCriteria,
    /// Adaptation rate for online learning
    pub adaptation_rate: f64,
}

/// Step size schedule types
#[derive(Debug, Clone)]
pub enum StepSizeSchedule {
    Constant {
        step_size: f64,
    },
    Exponential {
        initial_step: f64,
        decay_rate: f64,
    },
    Polynomial {
        initial_step: f64,
        power: f64,
    },
    Adaptive {
        base_step: f64,
        adaptation_factor: f64,
    },
}

/// Direction computation methods
#[derive(Debug, Clone)]
pub enum DirectionComputation {
    GradientBased { momentum: f64 },
    QuasiNewton { method: String },
    TrustRegion { radius: f64 },
    Adaptive { method_weights: Array1<f64> },
}

/// Convergence criteria
#[derive(Debug, Clone)]
pub struct ConvergenceCriteria {
    /// Tolerance for convergence
    pub tolerance: f64,
    /// Maximum iterations
    pub max_nit: usize,
}

impl MetaLearnerNetwork {
    /// Create new meta-learner network
    pub fn new(feature_dim: usize) -> Self {
        Self {
            feature_extractor: FeatureExtractor::new(feature_dim),
            context_encoder: ContextEncoder::new(feature_dim),
            parameter_generator: ParameterGenerator::new(feature_dim),
            update_network: UpdateNetwork::new(feature_dim),
            memory_networks: vec![MemoryNetwork::new(feature_dim, 100)],
        }
    }
}

impl FeatureExtractor {
    /// Create new feature extractor
    pub fn new(feature_dim: usize) -> Self {
        Self {
            conv_layers: vec![],
            dense_layers: vec![
                DenseLayer::new(feature_dim, feature_dim * 2, ActivationType::ReLU),
                DenseLayer::new(feature_dim * 2, feature_dim, ActivationType::ReLU),
            ],
            attention_mechanism: FeatureAttention::new(feature_dim),
            feature_dim,
        }
    }

    /// Extract features from problem encoding
    pub fn extract(&self, problem_encoding: &Array1<f64>) -> OptimizeResult<Array1<f64>> {
        let mut features = problem_encoding.clone();

        // Pass through dense layers
        for layer in &self.dense_layers {
            features = layer.forward(&features.view())?;
        }

        // Apply attention
        features = self.attention_mechanism.apply(&features)?;

        Ok(features)
    }
}

impl DenseLayer {
    /// Create new dense layer
    pub fn new(input_size: usize, output_size: usize, activation: ActivationType) -> Self {
        let weights = Array2::from_shape_fn((output_size, input_size), |_| {
            (scirs2_core::random::rng().random::<f64>() - 0.5) * (2.0 / input_size as f64).sqrt()
        });
        let bias = Array1::zeros(output_size);

        Self {
            weights,
            bias,
            activation,
        }
    }

    /// Forward pass through dense layer
    pub fn forward(&self, input: &ArrayView1<f64>) -> OptimizeResult<Array1<f64>> {
        let mut output = Array1::zeros(self.bias.len());

        for i in 0..output.len() {
            for j in 0..input.len().min(self.weights.ncols()) {
                output[i] += self.weights[[i, j]] * input[j];
            }
            output[i] += self.bias[i];
            output[i] = self.activation.apply(output[i]);
        }

        Ok(output)
    }
}

impl FeatureAttention {
    /// Create new feature attention
    pub fn new(feature_dim: usize) -> Self {
        Self {
            query_weights: Array2::from_shape_fn((feature_dim, feature_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            key_weights: Array2::from_shape_fn((feature_dim, feature_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            value_weights: Array2::from_shape_fn((feature_dim, feature_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            attention_scores: Array1::zeros(feature_dim),
        }
    }

    /// Apply attention to features
    pub fn apply(&self, features: &Array1<f64>) -> OptimizeResult<Array1<f64>> {
        // Simplified self-attention
        let mut attended_features = Array1::zeros(features.len());

        for i in 0..attended_features.len() {
            let attention_weight = (i as f64 / features.len() as f64).exp(); // Simple attention
            attended_features[i] = attention_weight * features.get(i).copied().unwrap_or(0.0);
        }

        // Normalize
        let sum = attended_features.sum();
        if sum > 0.0 {
            attended_features /= sum;
        }

        Ok(attended_features)
    }
}

impl ContextEncoder {
    /// Create new context encoder
    pub fn new(context_dim: usize) -> Self {
        Self {
            lstm: LSTMCell::new(context_dim),
            embedding_layer: Array2::from_shape_fn((context_dim, 100), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            aggregation_network: Array2::from_shape_fn((context_dim, context_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            context_dim,
        }
    }
}

impl LSTMCell {
    /// Create new LSTM cell
    pub fn new(hidden_size: usize) -> Self {
        Self {
            w_i: Array2::from_shape_fn((hidden_size, hidden_size * 2), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            w_f: Array2::from_shape_fn((hidden_size, hidden_size * 2), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            w_c: Array2::from_shape_fn((hidden_size, hidden_size * 2), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            w_o: Array2::from_shape_fn((hidden_size, hidden_size * 2), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            hidden_state: Array1::zeros(hidden_size),
            cell_state: Array1::zeros(hidden_size),
        }
    }
}

impl ParameterGenerator {
    /// Create new parameter generator
    pub fn new(param_dim: usize) -> Self {
        Self {
            generator_network: Array2::from_shape_fn((param_dim, param_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            conditioning_network: Array2::from_shape_fn((param_dim, param_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            output_projection: Array2::from_shape_fn((param_dim, param_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            param_dim,
        }
    }

    /// Generate parameters from encoding
    pub fn generate(&self, encoding: &Array1<f64>) -> OptimizeResult<Array1<f64>> {
        let mut params: Array1<f64> = Array1::zeros(self.param_dim);

        // Simple generation (in practice would be more sophisticated)
        for i in 0..params.len() {
            for j in 0..encoding.len().min(self.generator_network.ncols()) {
                params[i] += self.generator_network[[i, j]] * encoding[j];
            }
            params[i] = params[i].tanh(); // Normalize to [-1, 1]
        }

        Ok(params)
    }
}

impl UpdateNetwork {
    /// Create new update network
    pub fn new(param_dim: usize) -> Self {
        Self {
            update_network: Array2::from_shape_fn((param_dim, param_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            meta_gradient_network: Array2::from_shape_fn((param_dim, param_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            lr_network: Array2::from_shape_fn((1, param_dim), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            update_history: VecDeque::with_capacity(100),
        }
    }
}

impl MemoryNetwork {
    /// Create new memory network
    pub fn new(feature_dim: usize, memory_size: usize) -> Self {
        Self {
            memory_bank: Array2::zeros((memory_size, feature_dim)),
            memory_keys: Array2::zeros((memory_size, feature_dim)),
            memory_values: Array2::zeros((memory_size, feature_dim)),
            access_patterns: Vec::new(),
            memory_size,
        }
    }
}

impl FastAdaptationMechanism {
    /// Create new fast adaptation mechanism
    pub fn new(inner_lr: f64) -> Self {
        Self {
            gradient_adapter: GradientBasedAdapter::new(inner_lr),
            prototype_adapter: PrototypeBasedAdapter::new(),
            maml_adapter: MAMLAdapter::new(),
            strategy_selector: AdaptationStrategySelector::new(),
        }
    }
}

impl GradientBasedAdapter {
    /// Create new gradient-based adapter
    pub fn new(inner_lr: f64) -> Self {
        Self {
            meta_lr: 0.001,
            inner_lr,
            adaptation_steps: 5,
            gradient_accumulator: Array1::zeros(100),
        }
    }
}

impl Default for PrototypeBasedAdapter {
    fn default() -> Self {
        Self::new()
    }
}

impl PrototypeBasedAdapter {
    /// Create new prototype-based adapter
    pub fn new() -> Self {
        Self {
            prototypes: Array2::zeros((10, 100)),
            prototype_labels: vec!["default".to_string(); 10],
            distance_metric: DistanceMetric::Euclidean,
            adaptation_weights: Array1::ones(10),
        }
    }
}

impl Default for MAMLAdapter {
    fn default() -> Self {
        Self::new()
    }
}

impl MAMLAdapter {
    /// Create new MAML adapter
    pub fn new() -> Self {
        Self {
            meta_parameters: Array1::zeros(100),
            task_parameters: HashMap::new(),
            inner_optimizer: InnerLoopOptimizer::new(),
            meta_optimizer: MetaOptimizer::new(),
        }
    }
}

impl Default for InnerLoopOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

impl InnerLoopOptimizer {
    /// Create new inner loop optimizer
    pub fn new() -> Self {
        Self {
            learning_rate: 0.01,
            momentum: 0.9,
            velocity: Array1::zeros(100),
        }
    }
}

impl Default for MetaOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

impl MetaOptimizer {
    /// Create new meta-optimizer
    pub fn new() -> Self {
        Self {
            meta_learning_rate: 0.001,
            meta_momentum: 0.9,
            meta_velocity: Array1::zeros(100),
        }
    }
}

impl Default for AdaptationStrategySelector {
    fn default() -> Self {
        Self::new()
    }
}

impl AdaptationStrategySelector {
    /// Create new strategy selector
    pub fn new() -> Self {
        let mut strategy_scores = HashMap::new();
        strategy_scores.insert("gradient".to_string(), 0.5);
        strategy_scores.insert("prototype".to_string(), 0.5);
        strategy_scores.insert("maml".to_string(), 0.5);

        Self {
            strategy_scores,
            selection_network: Array2::from_shape_fn((4, 10), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            current_strategy: AdaptationStrategy::Gradient,
        }
    }
}

impl ProblemSimilarityMatcher {
    /// Create new problem similarity matcher
    pub fn new(feature_dim: usize) -> Self {
        Self {
            problem_embeddings: HashMap::new(),
            similarity_network: Array2::from_shape_fn((1, feature_dim * 2), |_| {
                (scirs2_core::random::rng().random::<f64>() - 0.5) * 0.1
            }),
            similarity_threshold: 0.7,
            similarity_cache: HashMap::new(),
        }
    }
}

impl ExperienceMemory {
    /// Create new experience memory
    pub fn new(capacity: usize) -> Self {
        Self {
            support_set: Vec::new(),
            query_set: Vec::new(),
            capacity,
            episodic_memory: VecDeque::with_capacity(capacity),
        }
    }

    /// Add support example
    pub fn add_support_example(&mut self, example: SupportExample) {
        if self.support_set.len() >= self.capacity {
            self.support_set.remove(0);
        }
        self.support_set.push(example);
    }
}

impl Default for FewShotAdaptationStats {
    fn default() -> Self {
        Self {
            adaptation_speed: 0.0,
            transfer_efficiency: 0.0,
            support_examples_used: 0,
            adaptation_success_rate: 0.0,
            meta_learning_progress: 0.0,
        }
    }
}

impl LearnedOptimizer for FewShotLearningOptimizer {
    fn meta_train(&mut self, training_tasks: &[TrainingTask]) -> OptimizeResult<()> {
        for task in training_tasks {
            // Create support examples from task
            let support_examples = self.create_support_examples_from_task(task)?;

            // Perform few-shot adaptation
            self.few_shot_adapt(&support_examples, &task.problem)?;

            // Update meta-learner based on adaptation performance
            self.update_meta_learner(&support_examples)?;
        }

        Ok(())
    }

    fn adapt_to_problem(
        &mut self,
        problem: &OptimizationProblem,
        initial_params: &ArrayView1<f64>,
    ) -> OptimizeResult<()> {
        // Find similar problems for support examples
        let similar_problems = self.find_similar_problems(problem)?;

        // Create support examples from similar problems
        let support_examples = self.create_support_examples_from_similar(&similar_problems)?;

        // Perform adaptation
        self.few_shot_adapt(&support_examples, problem)?;

        Ok(())
    }

    fn optimize<F>(
        &mut self,
        objective: F,
        initial_params: &ArrayView1<f64>,
    ) -> OptimizeResult<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        // Create default problem for strategy generation
        let default_problem = OptimizationProblem {
            name: "few_shot".to_string(),
            dimension: initial_params.len(),
            problem_class: "general".to_string(),
            metadata: HashMap::new(),
            max_evaluations: 1000,
            target_accuracy: 1e-6,
        };

        // Generate optimization strategy
        let strategy = self.generate_optimization_strategy(&default_problem)?;

        // Apply strategy to optimize
        let mut current_params = initial_params.to_owned();
        let mut best_value = objective(initial_params);
        let mut iterations = 0;

        for iter in 0..strategy.convergence_criteria.max_nit {
            iterations = iter;

            // Compute step size based on schedule
            let step_size = match &strategy.step_size_schedule {
                StepSizeSchedule::Constant { step_size } => *step_size,
                StepSizeSchedule::Exponential {
                    initial_step,
                    decay_rate,
                } => initial_step * decay_rate.powi(iter as i32),
                StepSizeSchedule::Polynomial {
                    initial_step,
                    power,
                } => initial_step / (1.0 + iter as f64).powf(*power),
                StepSizeSchedule::Adaptive {
                    base_step,
                    adaptation_factor,
                } => base_step * (1.0 + adaptation_factor * iter as f64 / 100.0),
            };

            // Compute direction
            let direction = self.compute_direction(
                &objective,
                &current_params,
                &strategy.direction_computation,
            )?;

            // Update parameters
            for i in 0..current_params.len().min(direction.len()) {
                current_params[i] -= step_size * direction[i];
            }

            let current_value = objective(&current_params.view());

            if current_value < best_value {
                best_value = current_value;
            }

            // Check convergence
            if (best_value - current_value).abs() < strategy.convergence_criteria.tolerance {
                break;
            }
        }

        Ok(OptimizeResults::<f64> {
            x: current_params,
            fun: best_value,
            success: true,
            nit: iterations,
            message: "Few-shot learning optimization completed".to_string(),
            ..OptimizeResults::default()
        })
    }

    fn get_state(&self) -> &MetaOptimizerState {
        &self.meta_state
    }

    fn reset(&mut self) {
        self.experience_memory = ExperienceMemory::new(1000);
        self.adaptation_stats = FewShotAdaptationStats::default();
        self.current_task_context = None;
    }
}

impl FewShotLearningOptimizer {
    fn create_support_examples_from_task(
        &self,
        task: &TrainingTask,
    ) -> OptimizeResult<Vec<SupportExample>> {
        // Simplified creation of support examples
        let problem_encoding = self.encode_problem_for_similarity(&task.problem)?;

        let trajectory = OptimizationTrajectory {
            parameter_history: vec![Array1::zeros(task.problem.dimension)],
            objective_history: vec![1.0],
            gradient_history: vec![Array1::zeros(task.problem.dimension)],
            step_size_history: vec![0.01],
            total_steps: 1,
        };

        Ok(vec![SupportExample {
            problem_encoding,
            trajectory,
            success: true,
            metadata: HashMap::new(),
        }])
    }

    fn update_meta_learner(&mut self, _support_examples: &[SupportExample]) -> OptimizeResult<()> {
        // Simplified meta-learner update
        self.meta_state.episode += 1;
        Ok(())
    }

    fn create_support_examples_from_similar(
        &self,
        _similar_problems: &[String],
    ) -> OptimizeResult<Vec<SupportExample>> {
        // Simplified creation from similar _problems
        Ok(vec![])
    }

    fn compute_direction<F>(
        &self,
        objective: &F,
        params: &Array1<f64>,
        direction_method: &DirectionComputation,
    ) -> OptimizeResult<Array1<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        match direction_method {
            DirectionComputation::GradientBased { momentum: _ } => {
                // Compute finite difference gradient
                let h = 1e-6;
                let f0 = objective(&params.view());
                let mut gradient = Array1::zeros(params.len());

                for i in 0..params.len() {
                    let mut params_plus = params.clone();
                    params_plus[i] += h;
                    let f_plus = objective(&params_plus.view());
                    gradient[i] = (f_plus - f0) / h;
                }

                Ok(gradient)
            }
            _ => {
                // Default to gradient
                let h = 1e-6;
                let f0 = objective(&params.view());
                let mut gradient = Array1::zeros(params.len());

                for i in 0..params.len() {
                    let mut params_plus = params.clone();
                    params_plus[i] += h;
                    let f_plus = objective(&params_plus.view());
                    gradient[i] = (f_plus - f0) / h;
                }

                Ok(gradient)
            }
        }
    }
}

/// Convenience function for few-shot learning optimization
#[allow(dead_code)]
pub fn few_shot_optimize<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    support_examples: &[SupportExample],
    config: Option<LearnedOptimizationConfig>,
) -> super::Result<OptimizeResults<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64,
{
    let config = config.unwrap_or_default();
    let mut optimizer = FewShotLearningOptimizer::new(config);

    // Create default problem
    let problem = OptimizationProblem {
        name: "few_shot_target".to_string(),
        dimension: initial_params.len(),
        problem_class: "general".to_string(),
        metadata: HashMap::new(),
        max_evaluations: 1000,
        target_accuracy: 1e-6,
    };

    // Perform few-shot adaptation
    optimizer.few_shot_adapt(support_examples, &problem)?;

    // Optimize
    optimizer.optimize(objective, initial_params)
}

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

    #[test]
    fn test_few_shot_optimizer_creation() {
        let config = LearnedOptimizationConfig::default();
        let optimizer = FewShotLearningOptimizer::new(config);

        assert_eq!(optimizer.adaptation_stats.support_examples_used, 0);
    }

    #[test]
    fn test_feature_extractor() {
        let extractor = FeatureExtractor::new(32);
        let encoding = Array1::from(vec![1.0, 2.0, 3.0]);

        let features = extractor.extract(&encoding).expect("Operation failed");
        assert_eq!(features.len(), 32);
    }

    #[test]
    fn test_parameter_generator() {
        let generator = ParameterGenerator::new(16);
        let encoding = Array1::from(vec![0.5, -0.3, 0.8]);

        let params = generator.generate(&encoding).expect("Operation failed");
        assert_eq!(params.len(), 16);
        assert!(params.iter().all(|&x| x >= -1.0 && x <= 1.0));
    }

    #[test]
    fn test_similarity_computation() {
        let config = LearnedOptimizationConfig::default();
        let optimizer = FewShotLearningOptimizer::new(config);

        let encoding1 = Array1::from(vec![1.0, 0.0, 0.0]);
        let encoding2 = Array1::from(vec![0.0, 1.0, 0.0]);

        let similarity = optimizer
            .compute_similarity(&encoding1, &encoding2)
            .expect("Operation failed");
        assert!(similarity >= 0.0 && similarity <= 1.0);
    }

    #[test]
    fn test_few_shot_optimization() {
        let objective = |x: &ArrayView1<f64>| x[0].powi(2) + x[1].powi(2);
        let initial = Array1::from(vec![2.0, 2.0]);

        // Create a simple support example
        let support_example = SupportExample {
            problem_encoding: Array1::from(vec![1.0, 1.0, 0.0]),
            trajectory: OptimizationTrajectory {
                parameter_history: vec![Array1::from(vec![1.0, 1.0])],
                objective_history: vec![2.0, 1.0],
                gradient_history: vec![Array1::from(vec![0.5, 0.5])],
                step_size_history: vec![0.01],
                total_steps: 1,
            },
            success: true,
            metadata: HashMap::new(),
        };

        let result = few_shot_optimize(objective, &initial.view(), &[support_example], None)
            .expect("Operation failed");

        assert!(result.fun >= 0.0);
        assert_eq!(result.x.len(), 2);
        assert!(result.success);
    }

    #[test]
    fn test_adaptation_strategy_selection() {
        let config = LearnedOptimizationConfig::default();
        let optimizer = FewShotLearningOptimizer::new(config);

        let support_features =
            Array2::from_shape_fn((2, 10), |_| scirs2_core::random::rng().random::<f64>());
        let similar_problems = vec!["problem1".to_string(), "problem2".to_string()];

        let strategy = optimizer
            .select_adaptation_strategy(&support_features, &similar_problems)
            .expect("Operation failed");

        match strategy {
            AdaptationStrategy::Prototype => {}
            AdaptationStrategy::MAML => {}
            AdaptationStrategy::Gradient => {}
            AdaptationStrategy::Hybrid { .. } => {}
        }
    }
}

#[allow(dead_code)]
pub fn placeholder() {
    // Placeholder function to prevent unused module warnings
}
