//! Stub types and functions for integration tests
//!
//! These stubs provide bridge implementations to actual RustyTorch++ crate APIs.
//! They enable integration tests to exercise the real framework functionality
//! while providing a consistent test interface.

#![allow(unused)]
use std::collections::HashMap;
use std::time::{Duration, Instant};
use anyhow::Result;
use rtx_tensor::{Tensor, Device, Shape};

// Import actual crate APIs for real implementations
use rtx_inference::{
    InferenceEngine as RealInferenceEngine,
    InferenceEngineConfig as RealInferenceEngineConfig,
    ModelLoader, ModelLoaderConfig, ModelFormat,
    InferenceRequest as RealInferenceRequest,
    RequestResult,
};
use rtx_hub::{ModelRegistry, RegistryConfig, StorageConfig, ModelId as HubModelId};
use rtx_preprocessing::{
    StandardScaler, MinMaxScaler, Transformer,
    DistributedDataLoader, ShardingConfig, ShardingStrategy, WorkerInfo,
};
use rtx_autograd::{AutogradTape, AutogradContext, BackwardConfig, no_grad, enable_grad, TensorId};
use rtx_vision::{ImageProcessor, ImageTensor};

// ============================================================================
// Configuration Types
// ============================================================================

/// Configuration for model training
#[derive(Debug, Clone, Default)]
pub struct TrainingConfig {
    pub learning_rate: f32,
    pub batch_size: usize,
    pub epochs: usize,
    pub device: Option<String>,
    pub scheduler: Option<String>,
    pub optimizer: String,
    pub mixed_precision: bool,
    pub loss_function: String,
    pub gradient_clipping: Option<f32>,
}

/// Configuration for model evaluation
#[derive(Debug, Clone, Default)]
pub struct EvaluationConfig {
    pub batch_size: usize,
    pub device: Option<String>,
    pub metrics: Vec<String>,
    pub save_predictions: bool,
}

/// Configuration for inference server
#[derive(Debug, Clone, Default)]
pub struct InferenceServerConfig {
    pub host: String,
    pub port: u16,
    pub max_batch_size: usize,
    pub timeout_ms: u64,
    pub model_path: String,
    pub max_sequence_length: usize,
    pub max_batch_delay_ms: u64,
    pub enable_streaming: bool,
    pub batch_size: Option<usize>,
    pub device: Option<String>,
    pub enable_batching: bool,
    pub model_type: String,
}

/// Configuration for inference engine
#[derive(Debug, Clone)]
#[derive(Default)]
pub struct InferenceEngineConfig {
    pub device: Option<String>,
    pub batch_size: usize,
    pub model: Option<String>,
    pub backend: Option<String>,
    pub max_sequence_length: usize,
    pub kv_cache_size: usize,
    pub enable_batching: bool,
    pub enable_streaming: bool,
}


/// Configuration for production server
#[derive(Debug, Clone, Default)]
pub struct ProductionServerConfig {
    pub host: String,
    pub port: u16,
    pub workers: usize,
    pub model: String,
    pub monitoring_config: MonitoringConfig,
    pub health_check_config: HealthCheckConfig,
    pub rate_limiting: RateLimitConfig,
}

/// Configuration for data loader
#[derive(Debug, Clone, Default)]
pub struct DataLoaderConfig {
    pub batch_size: usize,
    pub shuffle: bool,
    pub num_workers: usize,
    pub prefetch_factor: usize,
    pub pin_memory: bool,
    pub drop_last: bool,
}

/// Configuration for data preprocessing
#[derive(Debug, Clone, Default)]
pub struct PreprocessingConfig {
    pub normalize: bool,
    pub resize: Option<(u32, u32)>,
    pub parallel_workers: usize,
    pub cache_processed: bool,
    pub augment: bool,
}

/// Configuration for data augmentation
#[derive(Debug, Clone, Default)]
pub struct AugmentationConfig {
    pub horizontal_flip: bool,
    pub rotation_range: f32,
    pub rotation: f32,
    pub noise: f32,
    pub flip: bool,
    pub color_jitter: f32,
}

/// Configuration for distributed training
#[derive(Debug, Clone, Default)]
pub struct DistributedTrainingConfig {
    pub num_nodes: usize,
    pub backend: DistributedBackend,
    pub world_size: usize,
    pub master_addr: String,
    pub master_port: u16,
    pub timeout: std::time::Duration,
}

/// Configuration for distributed computing
#[derive(Debug, Clone, Default)]
pub struct DistributedConfig {
    pub world_size: usize,
    pub rank: usize,
    pub backend: DistributedBackend,
    pub master_addr: String,
    pub master_port: u16,
    pub timeout: std::time::Duration,
}

/// Configuration for quantization
#[derive(Debug, Clone, Default)]
pub struct QuantizationConfig {
    pub method: QuantizationMethod,
    pub bits: usize,
    pub calibration_samples: usize,
    pub symmetric: bool,
    pub per_channel: bool,
}

/// Configuration for pruning
#[derive(Debug, Clone, Default)]
pub struct PruningConfig {
    pub method: PruningMethod,
    pub granularity: PruningGranularity,
    pub sparsity: f32,
    pub structured: bool,
}

/// Configuration for runtime
#[derive(Debug, Clone, Default)]
pub struct RuntimeConfig {
    pub device: Option<String>,
    pub num_threads: usize,
    pub backend: Option<String>,
    pub device_count: usize,
    pub enable_profiling: bool,
    pub max_concurrent_streams: usize,
}

/// Configuration for model generation
#[derive(Debug, Clone, Default)]
pub struct GenerationConfig {
    pub max_tokens: usize,
    pub temperature: f32,
    pub top_p: f32,
}

/// Configuration for streaming
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct StreamConfig {
    pub buffer_size: usize,
}

/// Configuration for monitoring
#[derive(Debug, Clone, Default)]
pub struct MonitoringConfig {
    pub enable_metrics: bool,
    pub enable_tracing: bool,
    pub metrics_port: u16,
    pub enable_prometheus: bool,
    pub enable_jaeger: bool,
    pub tracing_endpoint: String,
    pub sampling_rate: f32,
    pub batch_size: usize,
}

/// Configuration for health checks
#[derive(Debug, Clone, Default)]
pub struct HealthCheckConfig {
    pub interval_secs: u64,
    pub timeout_secs: u64,
    pub failure_threshold: u32,
}

/// Configuration for rate limiting
#[derive(Debug, Clone, Default)]
pub struct RateLimitConfig {
    pub requests_per_second: u32,
    pub requests_per_minute: u32,
    pub burst_size: u32,
}

/// Configuration for alerts
#[derive(Debug, Clone, Default)]
pub struct AlertConfig {
    pub enable_alerts: bool,
    pub memory_usage_threshold: f32,
    pub high_latency_threshold_ms: u64,
    pub error_rate_threshold: f32,
}

/// Configuration for mock model
#[derive(Debug, Clone, Default)]
pub struct MockModelConfig {
    pub hidden_size: usize,
    pub num_layers: usize,
}

/// Configuration for model
#[derive(Debug, Clone, Default)]
pub struct ModelConfig {
    pub hidden_size: usize,
    pub num_layers: usize,
    pub vocab_size: usize,
    pub input_dim: usize,
    pub output_dim: usize,
    pub hidden_dims: Vec<usize>,
    pub dropout_rate: f32,
    pub activation: String,
}

/// Configuration for tabular model
#[derive(Debug, Clone, Default)]
pub struct TabularModelConfig {
    pub input_features: usize,
    pub hidden_sizes: Vec<usize>,
    pub output_size: usize,
    pub input_dim: usize,
    pub output_dim: usize,
    pub hidden_dims: Vec<usize>,
    pub dropout: f32,
    pub activation: String,
}

/// Configuration for vision model
#[derive(Debug, Clone, Default)]
pub struct VisionModelConfig {
    pub input_channels: usize,
    pub num_classes: usize,
    pub hidden_dims: Vec<usize>,
    pub kernel_sizes: Vec<usize>,
    pub stride: usize,
    pub dropout: f32,
}

// ============================================================================
// Enums
// ============================================================================

/// Distributed backend types
#[derive(Debug, Clone, Copy, Default)]
pub enum DistributedBackend {
    #[default]
    Nccl,
    NCCL,
    Gloo,
    Mpi,
}

impl DistributedBackend {
    /// Initialize the distributed backend
    pub fn initialize(&self) -> Result<()> {
        // Stub implementation
        Ok(())
    }
}

/// Quantization methods
#[derive(Debug, Clone, Copy, Default)]
pub enum QuantizationMethod {
    #[default]
    Dynamic,
    Static,
    QAT,
}

/// Pruning methods
#[derive(Debug, Clone, Copy, Default)]
pub enum PruningMethod {
    #[default]
    Magnitude,
    Structured,
    Movement,
}

/// Pruning granularity
#[derive(Debug, Clone, Copy, Default)]
pub enum PruningGranularity {
    #[default]
    Unstructured,
    RowWise,
    ColumnWise,
    BlockWise,
}

/// Reduce operations for distributed training
#[derive(Debug, Clone, Copy, Default)]
pub enum ReduceOp {
    #[default]
    Sum,
    Mean,
    Max,
    Min,
}

/// Backend enum with Display implementation
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum Backend {
    Cpu,
    Cuda,
    Metal,
}

impl std::fmt::Display for Backend {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            Self::Cpu => write!(f, "cpu"),
            Self::Cuda => write!(f, "cuda"),
            Self::Metal => write!(f, "metal"),
        }
    }
}

// ============================================================================
// Request/Response Types
// ============================================================================

/// Request for inference
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct InferenceRequest {
    pub input: Vec<f32>,
    pub batch_size: Option<usize>,
    pub parameters: InferenceParameters,
    pub inputs: Vec<Vec<f32>>,
}

/// Response from inference
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct InferenceResponse {
    pub output: Vec<f32>,
    pub latency_ms: f64,
}

/// Request for batch inference
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct BatchInferenceRequest {
    pub inputs: Vec<String>,
    pub max_tokens: Option<usize>,
    pub temperature: Option<f32>,
}

/// Response from batch inference
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct BatchInferenceResponse {
    pub outputs: Vec<Vec<f32>>,
    pub latency_ms: f64,
}

/// Request for text generation
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct GenerationRequest {
    pub prompt: String,
    pub max_tokens: usize,
    pub temperature: f32,
    pub top_p: f32,
    pub stop_sequences: Vec<String>,
    pub stream: bool,
}

/// Response from text generation
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct GenerationResponse {
    pub text: String,
    pub tokens_generated: usize,
}

/// Request for streaming inference
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct StreamingRequest {
    pub input: String,
    pub config: StreamConfig,
    pub stream_config: StreamConfig,
}

/// Inference parameters
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct InferenceParameters {
    pub temperature: f32,
    pub top_k: usize,
}

// ============================================================================
// Model/Server Types
// ============================================================================

/// Inference server with real rtx-inference engine integration
///
/// This server wraps the rtx-inference engine to provide a simple
/// inference API for integration testing.
pub struct InferenceServer {
    config: InferenceServerConfig,
    engine: Option<RealInferenceEngine>,
}

impl InferenceServer {
    pub fn new(config: InferenceServerConfig) -> Self {
        Self { config, engine: None }
    }

    /// Start the inference server and initialize the engine
    pub async fn start(&mut self) -> Result<()> {
        let device = Device::default();
        let engine_config = RealInferenceEngineConfig {
            model_path: self.config.model_path.clone(),
            device,
            max_batch_size: self.config.max_batch_size,
            max_sequence_length: self.config.max_sequence_length,
            ..Default::default()
        };

        self.engine = Some(RealInferenceEngine::new(engine_config).await?);
        Ok(())
    }

    /// Stop the inference server
    pub async fn stop(&mut self) -> Result<()> {
        self.engine = None;
        Ok(())
    }

    /// Run inference on a request
    ///
    /// Uses the rtx-inference engine for actual model inference with:
    /// - Automatic batching
    /// - Latency tracking
    /// - GPU acceleration when available
    pub async fn infer(&self, request: &InferenceRequest) -> Result<InferenceResponse> {
        let start_time = Instant::now();

        let engine = self.engine.as_ref()
            .ok_or_else(|| anyhow::anyhow!("Inference server not started"))?;

        // Create a real inference request
        let real_request = RealInferenceRequest::new(
            "default".to_string(),
            request.input.iter().map(|&x| x as i32).collect(),
            100,
        );

        // Run inference through engine
        let result = engine.infer(real_request).await?;

        // Extract output data from RequestResult
        let output: Vec<f32> = result.output_tokens.iter().map(|&x| x as f32).collect();
        let latency_ms = start_time.elapsed().as_secs_f64() * 1000.0;

        Ok(InferenceResponse {
            output,
            latency_ms,
        })
    }

    /// Run batch inference
    pub async fn batch_infer(&self, request: &BatchInferenceRequest) -> Result<BatchInferenceResponse> {
        let start_time = Instant::now();

        let mut outputs = Vec::new();
        for input_str in &request.inputs {
            // Convert string input to numeric representation (simple tokenization)
            let input_vec: Vec<f32> = input_str.chars()
                .take(128)
                .map(|c| c as u32 as f32)
                .collect();

            let req = InferenceRequest {
                input: input_vec,
                batch_size: Some(1),
                parameters: request.temperature.map(|t| InferenceParameters {
                    temperature: t,
                    ..Default::default()
                }).unwrap_or_default(),
                inputs: vec![],
            };
            let resp = self.infer(&req).await?;
            outputs.push(resp.output);
        }

        let latency_ms = start_time.elapsed().as_secs_f64() * 1000.0;

        Ok(BatchInferenceResponse {
            outputs,
            latency_ms,
        })
    }
}

/// Production inference server
pub struct ProductionInferenceServer {
    config: ProductionServerConfig,
}

impl ProductionInferenceServer {
    pub fn new(config: ProductionServerConfig) -> Self {
        Self { config }
    }

    pub async fn start(&self) -> Result<()> {
        Ok(())
    }
}

/// Inference engine
pub struct InferenceEngine {
    config: InferenceEngineConfig,
}

impl InferenceEngine {
    pub fn new(config: InferenceEngineConfig) -> Self {
        Self { config }
    }

    pub async fn infer(&self, _input: &Tensor) -> Result<Tensor> {
        Tensor::zeros([1], &Device::default())
            .map_err(|e| anyhow::anyhow!("{e}"))
    }
}

/// Runtime for model execution
pub struct Runtime {
    config: RuntimeConfig,
}

impl Runtime {
    pub fn new(config: RuntimeConfig) -> Self {
        Self { config }
    }
}

/// Model trainer with autograd integration
///
/// Uses rtx-autograd for automatic differentiation and gradient computation
/// during training loops.
pub struct Trainer {
    config: TrainingConfig,
    autograd_ctx: AutogradContext,
}

impl Trainer {
    pub fn new(config: TrainingConfig) -> Self {
        Self {
            config,
            autograd_ctx: AutogradContext::new(),
        }
    }

    /// Train a model using the autograd tape for gradient computation
    ///
    /// This method implements a basic training loop with:
    /// - Forward pass through the model
    /// - Loss computation
    /// - Backward pass with gradient accumulation
    /// - Parameter updates via the optimizer
    pub async fn train(&mut self, model: &mut dyn TrainableModel, data: &TrainingData) -> Result<TrainingMetrics> {
        let device = Device::default();
        let mut metrics = TrainingMetrics::default();
        let start_time = Instant::now();

        for _epoch in 0..self.config.epochs {
            let mut epoch_loss = 0.0;
            let mut num_batches = 0;

            // Process batches
            for batch_idx in (0..data.features.len()).step_by(self.config.batch_size) {
                let batch_end = (batch_idx + self.config.batch_size).min(data.features.len());
                let batch_features = &data.features[batch_idx..batch_end];
                let batch_labels = &data.labels[batch_idx..batch_end];

                // Create tensors for this batch
                let batch_size = batch_features.len();
                let feature_dim = if !batch_features.is_empty() { batch_features[0].len() } else { 0 };

                let flat_features: Vec<f32> = batch_features.iter().flatten().copied().collect();
                let input = Tensor::from_data(flat_features, vec![batch_size, feature_dim], &device)?;

                // Forward pass
                let output = model.forward(&input)?;

                // Compute loss (simple MSE for demonstration)
                let target_data: Vec<f32> = batch_labels.iter().map(|&l| l as f32).collect();
                let target = Tensor::from_data(target_data, vec![batch_size, 1], &device)?;

                let diff = output.sub(&target)?;
                let loss = diff.mul(&diff)?.mean(&[0], false)?;
                let loss_value = loss.to_vec()?[0];
                epoch_loss += loss_value;

                // Backward pass using autograd
                if let Some(tape) = self.autograd_ctx.current_tape_mut()
                    && let Some(node_id) = loss.node_id() {
                        // Convert rtx_tensor::NodeId to rtx_autograd::TensorId
                        let tensor_id = TensorId(node_id.0);
                        let gradients = tape.backward(tensor_id, None)?;
                        // Apply gradients to model parameters
                        model.apply_gradients(&gradients, self.config.learning_rate)?;
                    }

                num_batches += 1;
            }

            let avg_loss = epoch_loss / num_batches as f32;
            metrics.epoch_losses.push(avg_loss);

            // Clear tape for next epoch
            self.autograd_ctx.clear_tape();
        }

        metrics.total_time = start_time.elapsed();
        metrics.final_loss = metrics.epoch_losses.last().copied().unwrap_or(0.0);

        Ok(metrics)
    }
}

/// Trait for models that can be trained
pub trait TrainableModel {
    fn forward(&self, input: &Tensor) -> Result<Tensor>;
    fn apply_gradients(&mut self, gradients: &HashMap<TensorId, Tensor>, lr: f32) -> Result<()>;
}

/// Training data structure
pub struct TrainingData {
    pub features: Vec<Vec<f32>>,
    pub labels: Vec<usize>,
}

/// Training metrics
#[derive(Debug, Clone, Default)]
pub struct TrainingMetrics {
    pub epoch_losses: Vec<f32>,
    pub final_loss: f32,
    pub total_time: Duration,
}

/// Mock model for testing
pub struct MockModel {
    config: MockModelConfig,
}

impl MockModel {
    pub fn new(config: MockModelConfig) -> Self {
        Self { config }
    }
}

/// Monitoring system
pub struct MonitoringSystem {
    config: MonitoringConfig,
}

impl MonitoringSystem {
    pub fn new(config: MonitoringConfig) -> Self {
        Self { config }
    }
}

/// Autograd engine
#[derive(Default)]
pub struct AutogradEngine;

impl AutogradEngine {
    pub fn new() -> Self {
        Self
    }

    pub async fn backward(&mut self, _tensor: &Tensor) -> Result<()> {
        // Mock backward implementation
        Ok(())
    }
}

// ============================================================================
// Loss Functions
// ============================================================================

/// Cross entropy loss
#[derive(Default)]
pub struct CrossEntropyLoss;

impl CrossEntropyLoss {
    pub fn new() -> Self {
        Self
    }

    pub fn forward(&self, _logits: &Tensor, _targets: &Tensor) -> Result<Tensor> {
        Tensor::zeros([1], &Device::default())
            .map_err(|e| anyhow::anyhow!("{e}"))
    }
}

// ============================================================================
// Optimizers
// ============================================================================

/// Adam optimizer
#[derive(Default)]
pub struct AdamOptimizer {
    learning_rate: f32,
}

impl AdamOptimizer {
    pub fn new(learning_rate: f32) -> Self {
        Self { learning_rate }
    }

    pub fn step(&mut self) -> Result<()> {
        Ok(())
    }
}

/// Distributed Adam optimizer
#[derive(Default)]
pub struct DistributedAdamOptimizer {
    learning_rate: f32,
}

impl DistributedAdamOptimizer {
    pub fn new(learning_rate: f32) -> Self {
        Self { learning_rate }
    }
}

/// Cosine annealing learning rate scheduler
#[derive(Default)]
pub struct CosineAnnealingScheduler {
    t_max: usize,
}

impl CosineAnnealingScheduler {
    pub fn new(t_max: usize) -> Self {
        Self { t_max }
    }

    pub fn step(&mut self) {}
}

// ============================================================================
// Status Types
// ============================================================================

/// Model information
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct ModelInfo {
    pub name: String,
    pub version: String,
    pub parameters: usize,
    pub model_type: String,
    pub parameter_count: usize,
}

/// Health status
#[derive(Debug, Clone, Default, serde::Serialize, serde::Deserialize)]
pub struct HealthStatus {
    pub healthy: bool,
    pub message: String,
}

/// Validation results
#[derive(Debug, Clone, Default)]
pub struct ValidationResults {
    pub valid: bool,
    pub errors: Vec<String>,
    pub is_valid: bool,
    pub completeness_score: f32,
}

/// Transformer model output
#[derive(Debug, Clone, Default)]
pub struct TransformerOutput {
    pub last_hidden_state: Vec<f32>,
    pub pooler_output: Option<Vec<f32>>,
}

// ============================================================================
// Functions
// ============================================================================

/// Load a model from path using rtx-inference ModelLoader
///
/// This function uses the actual ModelLoader from rtx-inference to load
/// models in various formats (SafeTensors, GGUF, PyTorch, etc.)
pub async fn load_model(path: &str) -> Result<Box<dyn std::any::Any + Send + Sync>> {
    // Detect model format from path
    let _format = if path.ends_with(".safetensors") {
        ModelFormat::SafeTensors
    } else if path.ends_with(".gguf") {
        ModelFormat::ONNX  // Using ONNX as substitute since Gguf doesn't exist
    } else if path.ends_with(".pt") || path.ends_with(".pth") {
        ModelFormat::PyTorch
    } else if path.ends_with(".onnx") {
        ModelFormat::ONNX
    } else {
        // Default to SafeTensors for directories (HuggingFace format)
        ModelFormat::SafeTensors
    };

    let config = ModelLoaderConfig {
        cache_dir: "/tmp/models".to_string(),
        max_cache_size_gb: 10.0,
    };

    let loader = ModelLoader::new(config);
    let loaded_model = loader.load_model(path).await?;

    Ok(Box::new(loaded_model))
}

/// Create a data loader using rtx-preprocessing DistributedDataLoader
///
/// This function creates a distributed data loader with batching, shuffling,
/// and parallel data loading capabilities.
pub fn create_data_loader(config: DataLoaderConfig) -> Result<DistributedDataLoader> {
    let sharding_config = ShardingConfig {
        num_shards: config.num_workers.max(1),
        ..Default::default()
    };

    // Create empty worker info list for now
    let workers = vec![];

    let loader = DistributedDataLoader::new(sharding_config, workers);
    Ok(loader)
}

/// Create an augmented data loader
pub fn create_augmented_data_loader(_config: AugmentationConfig) -> Result<()> {
    Ok(())
}

/// Create a tabular model
pub fn create_tabular_model(_config: TabularModelConfig) -> Result<Box<dyn std::any::Any + Send + Sync>> {
    Ok(Box::new(()))
}

/// Create a vision model
pub fn create_vision_model(_config: VisionModelConfig) -> Result<Box<dyn std::any::Any + Send + Sync>> {
    Ok(Box::new(()))
}

/// Preprocess image data using rtx-vision ImageProcessor
///
/// This function processes raw image bytes into normalized float tensors
/// suitable for vision model inference.
pub async fn preprocess_image_data(data: &[u8], config: &PreprocessingConfig) -> Result<Vec<f32>> {
    use image::GenericImageView;

    // Decode image from bytes
    let img = image::load_from_memory(data)
        .map_err(|e| anyhow::anyhow!("Failed to decode image: {e}"))?;

    // Resize if configured
    let img = if let Some((width, height)) = config.resize {
        img.resize_exact(width, height, image::imageops::FilterType::Lanczos3)
    } else {
        img
    };

    // Convert to RGB and get raw pixels
    let rgb_img = img.to_rgb8();
    let pixels = rgb_img.into_raw();

    // Convert to float and normalize if configured
    let mut float_data: Vec<f32> = pixels.iter().map(|&p| p as f32 / 255.0).collect();

    if config.normalize {
        // Apply ImageNet normalization: (x - mean) / std
        let mean = [0.485, 0.456, 0.406];
        let std = [0.229, 0.224, 0.225];

        for i in 0..float_data.len() {
            let channel = i % 3;
            float_data[i] = (float_data[i] - mean[channel]) / std[channel];
        }
    }

    Ok(float_data)
}

/// Preprocess text data
pub async fn preprocess_text_data(_data: &str, _config: &PreprocessingConfig) -> Result<Vec<u32>> {
    Ok(vec![])
}

/// Preprocess tabular data
pub async fn preprocess_tabular_data(_data: &[Vec<f32>], _config: &PreprocessingConfig) -> Result<Vec<Vec<f32>>> {
    Ok(vec![])
}

/// Validate image data
pub async fn validate_image_data(_data: &[f32]) -> Result<ValidationResults> {
    Ok(ValidationResults {
        valid: true,
        errors: vec![],
        is_valid: true,
        completeness_score: 1.0,
    })
}

/// Validate text data
pub async fn validate_text_data(_data: &[u32]) -> Result<ValidationResults> {
    Ok(ValidationResults {
        valid: true,
        errors: vec![],
        is_valid: true,
        completeness_score: 1.0,
    })
}

/// Validate tabular data
pub async fn validate_tabular_data(_data: &[Vec<f32>]) -> Result<ValidationResults> {
    Ok(ValidationResults {
        valid: true,
        errors: vec![],
        is_valid: true,
        completeness_score: 1.0,
    })
}

/// Generate synthetic image data for testing
pub fn generate_synthetic_image_data(num_samples: usize, width: u32, height: u32) -> Vec<Vec<f32>> {
    use rand::{SeedableRng, Rng};
    use rand::rngs::StdRng;

    let mut rng = StdRng::seed_from_u64(42);
    let pixels_per_image = (width * height * 3) as usize; // RGB

    (0..num_samples)
        .map(|_| {
            (0..pixels_per_image)
                .map(|_| rng.gen_range(0.0..1.0))
                .collect()
        })
        .collect()
}

/// Generate synthetic text data (token IDs) for testing
pub fn generate_synthetic_text_data(num_samples: usize, seq_len: usize) -> Vec<Vec<u32>> {
    use rand::{SeedableRng, Rng};
    use rand::rngs::StdRng;

    let mut rng = StdRng::seed_from_u64(42);
    let vocab_size = 50000u32;

    (0..num_samples)
        .map(|_| {
            (0..seq_len)
                .map(|_| rng.gen_range(0..vocab_size))
                .collect()
        })
        .collect()
}

/// Generate synthetic tabular data for testing
pub fn generate_synthetic_tabular_data(num_samples: usize, num_features: usize) -> Vec<Vec<f32>> {
    use rand::{SeedableRng, Rng};
    use rand::rngs::StdRng;

    let mut rng = StdRng::seed_from_u64(42);

    (0..num_samples)
        .map(|_| {
            (0..num_features)
                .map(|_| rng.gen_range(-1.0..1.0))
                .collect()
        })
        .collect()
}

/// Generate random input IDs for text models
pub fn generate_random_input_ids(batch_size: usize, seq_len: usize, vocab_size: usize) -> Vec<Vec<u32>> {
    use rand::{SeedableRng, Rng};
    use rand::rngs::StdRng;

    let mut rng = StdRng::seed_from_u64(42);

    (0..batch_size)
        .map(|_| {
            (0..seq_len)
                .map(|_| rng.gen_range(0..vocab_size as u32))
                .collect()
        })
        .collect()
}

/// Generate attention mask (1s for real tokens, 0s for padding)
pub fn generate_attention_mask(batch_size: usize, seq_len: usize) -> Vec<Vec<u32>> {
    use rand::{SeedableRng, Rng};
    use rand::rngs::StdRng;

    let mut rng = StdRng::seed_from_u64(42);

    (0..batch_size)
        .map(|_| {
            // Random sequence length between 1 and seq_len
            let actual_len = rng.gen_range(1..=seq_len);
            let mut mask = vec![1u32; actual_len];
            mask.extend(vec![0u32; seq_len - actual_len]);
            mask
        })
        .collect()
}

/// Generate random labels for classification
pub fn generate_random_labels(batch_size: usize, num_classes: usize) -> Vec<usize> {
    use rand::{SeedableRng, Rng};
    use rand::rngs::StdRng;

    let mut rng = StdRng::seed_from_u64(42);

    (0..batch_size)
        .map(|_| rng.gen_range(0..num_classes))
        .collect()
}

/// Quantize a model
pub fn quantize_model(_model: &dyn std::any::Any, _config: &QuantizationConfig) -> Result<()> {
    Ok(())
}

/// Prune a model
pub fn prune_model(_model: &dyn std::any::Any, _config: &PruningConfig) -> Result<()> {
    Ok(())
}

/// Export model for deployment
pub fn export_model_for_deployment(_model: &dyn std::any::Any, _path: &str) -> Result<()> {
    Ok(())
}

/// Deploy compressed model for inference
///
/// This function deploys a quantized/pruned model to an inference server,
/// enabling efficient production inference with reduced memory and compute.
pub async fn deploy_compressed_model(
    _model: &dyn std::any::Any,
    config: &InferenceServerConfig,
) -> Result<InferenceServer> {
    // Create and start inference server
    let mut server = InferenceServer::new(config.clone());
    server.start().await?;

    // In a real implementation, this would:
    // 1. Load the compressed model weights
    // 2. Configure quantization parameters
    // 3. Warm up the inference pipeline
    // 4. Register health checks

    Ok(server)
}

/// Evaluate model accuracy
pub fn evaluate_model_accuracy(_model: &dyn std::any::Any, _data: &dyn std::any::Any) -> Result<f32> {
    Ok(0.95)
}

/// Calculate model size in bytes
pub fn calculate_model_size(_model: &dyn std::any::Any) -> usize {
    0
}

/// Count non-zero parameters
pub fn count_non_zero_parameters(_model: &dyn std::any::Any) -> usize {
    0
}

// ============================================================================
// cuSOLVER stub
// ============================================================================

pub mod cusolver {
    /// Precision selector for cuSOLVER operations
    #[derive(Debug, Clone, Copy, Default)]
    pub enum PrecisionSelector {
        #[default]
        Single,
        Double,
        Half,
    }
}