ocr 0.1.1

//! End-to-End OCR model implementation
//!
//! This module provides end-to-end OCR models that perform both text detection
//! and recognition in a single unified architecture.

use super::engine::*;
use crate::core::geometry::TBox;
use crate::core::ModelType;
use crate::utils::{OcrError, Result};
use serde::{Deserialize, Serialize};
use std::path::Path;

/// Configuration for End-to-End OCR models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EndToEndConfig {
    /// Model architecture type
    pub architecture: EndToEndArchitecture,
    /// Path to the model file
    pub model_path: String,
    /// Supported languages
    pub supported_languages: Vec<LanguageVariant>,
    /// Input image size (height, width, channels)
    pub input_shape: (u32, u32, u32),
    /// Confidence threshold for predictions
    pub confidence_threshold: f32,
    /// Device to run inference on
    pub device: DeviceType,
    /// Quantization type
    pub quantization: Option<QuantizationType>,
    /// Whether to use attention mechanism
    pub use_attention: bool,
    /// Whether to use multi-scale features
    pub use_multi_scale: bool,
    /// Whether to use character-level detection
    pub use_character_level: bool,
    /// Whether to use word-level detection
    pub use_word_level: bool,
    /// Whether to use line-level detection
    pub use_line_level: bool,
    /// Maximum sequence length
    pub max_sequence_length: usize,
    /// Number of detection heads
    pub num_detection_heads: usize,
    /// Number of recognition heads
    pub num_recognition_heads: usize,
}

/// End-to-End architecture variants
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum EndToEndArchitecture {
    /// FOTS (Fully Convolutional Text Spotting)
    FOTS,
    /// TextSnake
    TextSnake,
    /// Mask TextSpotter
    MaskTextSpotter,
    /// ABCNet
    ABCNet,
    /// PGNet
    PGNet,
    /// DBNet++
    DBNetPlusPlus,
    /// Custom end-to-end architecture
    Custom(String),
}

/// End-to-End OCR model implementation
pub struct EndToEndModel {
    config: EndToEndConfig,
    model_loaded: bool,
    backbone: BackboneNetwork,
    detection_head: DetectionHead,
    recognition_head: RecognitionHead,
    fusion_module: FusionModule,
    post_processor: PostProcessor,
}

/// Backbone network for feature extraction
pub struct BackboneNetwork {
    layers: Vec<BackboneLayer>,
    architecture: EndToEndArchitecture,
    output_features: Vec<FeatureMap>,
}

/// Individual backbone layer
#[derive(Debug, Clone)]
pub struct BackboneLayer {
    layer_type: BackboneLayerType,
    input_shape: (u32, u32, u32),
    output_shape: (u32, u32, u32),
    parameters: BackboneParameters,
}

/// Backbone layer types
#[derive(Debug, Clone)]
pub enum BackboneLayerType {
    Convolutional(ConvBlock),
    Residual(ResidualBlock),
    Dense(DenseBlock),
    Attention(AttentionBlock),
    Pooling(PoolBlock),
}

/// Convolutional layer
#[derive(Debug, Clone)]
pub struct ConvLayer {
    input_channels: usize,
    output_channels: usize,
    kernel_size: (u32, u32),
    stride: (u32, u32),
    padding: (u32, u32),
    dilation: (u32, u32),
    groups: usize,
    bias: bool,
    weights: Vec<Vec<Vec<Vec<f32>>>>, // [filters, channels, height, width]
    bias_weights: Option<Vec<f32>>,
}

/// Convolutional block
#[derive(Debug, Clone)]
pub struct ConvBlock {
    conv: ConvLayer,
    batch_norm: Option<BatchNormLayer>,
    activation: ActivationLayer,
    dropout: Option<DropoutLayer>,
}

/// Residual block
#[derive(Debug, Clone)]
pub struct ResidualBlock {
    main_path: Vec<BackboneLayer>,
    shortcut: Option<ShortcutConnection>,
    activation: ActivationLayer,
}

/// Dense block
#[derive(Debug, Clone)]
pub struct DenseBlock {
    layers: Vec<BackboneLayer>,
    growth_rate: usize,
    bottleneck: bool,
}

/// Attention block
#[derive(Debug, Clone)]
pub struct AttentionBlock {
    attention_type: AttentionType,
    query_projection: LinearLayer,
    key_projection: LinearLayer,
    value_projection: LinearLayer,
    output_projection: LinearLayer,
}

/// Pooling block
#[derive(Debug, Clone)]
pub struct PoolBlock {
    pool_type: PoolType,
    kernel_size: (u32, u32),
    stride: (u32, u32),
    padding: (u32, u32),
}

/// Detection head for text detection
#[derive(Debug, Clone)]
pub struct DetectionHead {
    layers: Vec<DetectionLayer>,
    output_channels: usize,
    use_fpn: bool,
    use_attention: bool,
}

/// Recognition head for text recognition
#[derive(Debug, Clone)]
pub struct RecognitionHead {
    layers: Vec<RecognitionLayer>,
    output_vocab_size: usize,
    use_attention: bool,
    use_lstm: bool,
}

/// Fusion module for combining detection and recognition
#[derive(Debug, Clone)]
pub struct FusionModule {
    fusion_type: FusionType,
    attention_weights: Option<AttentionWeights>,
    gating_weights: Option<GatingWeights>,
}

/// Post-processor for final output
#[derive(Debug, Clone)]
pub struct PostProcessor {
    nms_threshold: f32,
    confidence_threshold: f32,
    max_candidates: usize,
    use_polygon_nms: bool,
}

/// Feature map
#[derive(Debug, Clone)]
pub struct FeatureMap {
    height: u32,
    width: u32,
    channels: u32,
    features: Vec<f32>,
}

/// Detection layer
#[derive(Debug, Clone)]
pub struct DetectionLayer {
    layer_type: DetectionLayerType,
    input_channels: usize,
    output_channels: usize,
    kernel_size: (u32, u32),
}

/// Recognition layer
#[derive(Debug, Clone)]
pub struct RecognitionLayer {
    layer_type: RecognitionLayerType,
    input_size: usize,
    output_size: usize,
    hidden_size: usize,
}

/// Layer types
#[derive(Debug, Clone)]
pub enum DetectionLayerType {
    Convolutional,
    Deconvolutional,
    Upsampling,
    Attention,
}

#[derive(Debug, Clone)]
pub enum RecognitionLayerType {
    LSTM,
    GRU,
    Transformer,
    Convolutional,
    Attention,
}

/// Attention types
#[derive(Debug, Clone)]
pub enum AttentionType {
    SelfAttention,
    CrossAttention,
    MultiHeadAttention,
    SpatialAttention,
    ChannelAttention,
}

/// Pool types
#[derive(Debug, Clone)]
pub enum PoolType {
    Max,
    Average,
    GlobalAverage,
    GlobalMax,
    AdaptiveAverage,
    AdaptiveMax,
}

/// Fusion types
#[derive(Debug, Clone)]
pub enum FusionType {
    Concatenation,
    Addition,
    Multiplication,
    Attention,
    Gating,
}

/// Shortcut connection
#[derive(Debug, Clone)]
pub struct ShortcutConnection {
    connection_type: ShortcutType,
    projection: Option<LinearLayer>,
}

/// Shortcut types
#[derive(Debug, Clone)]
pub enum ShortcutType {
    Identity,
    Projection,
    Convolution,
}

/// Attention weights
#[derive(Debug, Clone)]
pub struct AttentionWeights {
    query_weights: Vec<Vec<f32>>,
    key_weights: Vec<Vec<f32>>,
    value_weights: Vec<Vec<f32>>,
    output_weights: Vec<Vec<f32>>,
}

/// Gating weights
#[derive(Debug, Clone)]
pub struct GatingWeights {
    gate_weights: Vec<Vec<f32>>,
    gate_bias: Vec<f32>,
}

/// Linear layer
#[derive(Debug, Clone)]
pub struct LinearLayer {
    weight: Vec<Vec<f32>>,
    bias: Option<Vec<f32>>,
}

/// Batch normalization layer
#[derive(Debug, Clone)]
pub struct BatchNormLayer {
    num_features: usize,
    eps: f32,
    momentum: f32,
    weight: Vec<f32>,
    bias: Vec<f32>,
    running_mean: Vec<f32>,
    running_var: Vec<f32>,
}

/// Activation layer
#[derive(Debug, Clone)]
pub struct ActivationLayer {
    activation: Activation,
}

/// Dropout layer
#[derive(Debug, Clone)]
pub struct DropoutLayer {
    rate: f32,
    training: bool,
}

/// Activation functions
#[derive(Debug, Clone)]
pub enum Activation {
    ReLU,
    LeakyReLU(f32),
    ELU(f32),
    GELU,
    Swish,
    Sigmoid,
    Tanh,
    Softmax,
}

/// Backbone parameters
#[derive(Debug, Clone)]
pub struct BackboneParameters {
    input_channels: usize,
    output_channels: usize,
    kernel_size: (u32, u32),
    stride: (u32, u32),
    padding: (u32, u32),
    dilation: (u32, u32),
    groups: usize,
}

/// Output layer
#[derive(Debug, Clone)]
pub struct OutputLayer {
    layer_type: OutputLayerType,
    input_size: usize,
    output_size: usize,
    weights: Vec<Vec<f32>>,
    bias: Option<Vec<f32>>,
}

/// Output layer types
#[derive(Debug, Clone)]
pub enum OutputLayerType {
    Dense,
    Attention,
    Convolution,
    LSTM,
}

impl EndToEndModel {
    /// Create a new End-to-End model
    pub fn new(config: EndToEndConfig) -> Self {
        let backbone = BackboneNetwork::new(&config);
        let detection_head = DetectionHead::new(&config);
        let recognition_head = RecognitionHead::new(&config);
        let fusion_module = FusionModule::new(&config);
        let post_processor = PostProcessor::new(&config);

        Self {
            config,
            model_loaded: false,
            backbone,
            detection_head,
            recognition_head,
            fusion_module,
            post_processor,
        }
    }

    /// Load the model from file
    pub fn load_from_file<P: AsRef<Path>>(&mut self, path: P) -> Result<()> {
        let path = path.as_ref();

        if !path.exists() {
            return Err(OcrError::ModelNotFound(format!(
                "Model file not found: {}",
                path.display()
            ))
            .into());
        }

        // In a real implementation, this would load the actual model weights
        // For now, we'll just mark the model as loaded
        self.model_loaded = true;
        Ok(())
    }

    /// Get the model configuration
    pub fn config(&self) -> &EndToEndConfig {
        &self.config
    }

    /// Check if the model is loaded
    pub fn is_loaded(&self) -> bool {
        self.model_loaded
    }

    /// Get the model architecture
    pub fn architecture(&self) -> &EndToEndArchitecture {
        &self.config.architecture
    }

    /// Get supported languages
    pub fn supported_languages(&self) -> &[LanguageVariant] {
        &self.config.supported_languages
    }

    /// Check if a language is supported
    pub fn supports_language(&self, language: &LanguageVariant) -> bool {
        self.config.supported_languages.contains(language)
    }

    /// Forward pass through the model
    fn forward(&self, input: &[f32]) -> Result<EndToEndOutput> {
        // Extract features using backbone
        let features = self.backbone.forward(input)?;

        // Detect text regions
        let detections = self.detection_head.forward(&features)?;

        // Recognize text in detected regions
        let recognitions = self.recognition_head.forward(&features, &detections)?;

        // Fuse detection and recognition results
        let fused_output = self.fusion_module.fuse(&detections, &recognitions)?;

        // Post-process the results
        let final_output = self.post_processor.process(&fused_output)?;

        Ok(final_output)
    }
}

/// End-to-End model output
#[derive(Debug, Clone)]
pub struct EndToEndOutput {
    pub detections: Vec<TextDetection>,
    pub recognitions: Vec<TextRecognition>,
    pub fused_results: Vec<FusedResult>,
    pub confidence: f32,
    pub processing_time_ms: u64,
}

/// Text detection result
#[derive(Debug, Clone)]
pub struct TextDetection {
    pub bounding_box: TBox,
    pub confidence: f32,
    pub polygon: Vec<(f32, f32)>,
    pub text_direction: TextDirection,
}

/// Text recognition result
#[derive(Debug, Clone)]
pub struct TextRecognition {
    pub text: String,
    pub confidence: f32,
    pub character_scores: Vec<f32>,
    pub language: Option<String>,
}

/// Fused result combining detection and recognition
#[derive(Debug, Clone)]
pub struct FusedResult {
    pub detection: TextDetection,
    pub recognition: TextRecognition,
    pub combined_confidence: f32,
}

/// Text direction
#[derive(Debug, Clone)]
pub enum TextDirection {
    Horizontal,
    Vertical,
    Rotated(f32),
}

impl OcrModel for EndToEndModel {
    fn model_type(&self) -> ModelType {
        ModelType::EndToEnd
    }

    fn supported_languages(&self) -> Vec<LanguageVariant> {
        self.config.supported_languages.clone()
    }

    fn supports_language(&self, language: &LanguageVariant) -> bool {
        self.supports_language(language)
    }

    fn input_shape(&self) -> (usize, usize, usize) {
        let (h, w, c) = self.config.input_shape;
        (h as usize, w as usize, c as usize)
    }

    fn config(&self) -> &ModelConfig {
        todo!("Implement proper config storage")
    }

    fn predict(&self, input: &[u8]) -> Result<RecognitionResult> {
        if !self.model_loaded {
            return Err(OcrError::ModelNotFound("Model not loaded".to_string()).into());
        }

        // For now, return a placeholder result
        // In a real implementation, this would:
        // 1. Preprocess the input image
        // 2. Run through the end-to-end model
        // 3. Postprocess the output

        let result = RecognitionResult {
            text: "End-to-End OCR Result".to_string(),
            confidence: 0.94,
            bounding_boxes: vec![],
            character_results: vec![],
            word_results: vec![],
            line_results: vec![],
            language: Some("en".to_string()),
            model_type: ModelType::EndToEnd,
            processing_time_ms: 300,
        };

        Ok(result)
    }
}

impl BackboneNetwork {
    fn new(config: &EndToEndConfig) -> Self {
        let layers = Self::create_layers(config);
        let output_features = Vec::new(); // Placeholder

        Self {
            layers,
            architecture: config.architecture.clone(),
            output_features,
        }
    }

    fn create_layers(config: &EndToEndConfig) -> Vec<BackboneLayer> {
        let mut layers = Vec::new();

        match config.architecture {
            EndToEndArchitecture::FOTS => {
                // FOTS backbone layers
                layers.push(BackboneLayer::conv_block(3, 64, (7, 7), (2, 2), (3, 3)));
                layers.push(BackboneLayer::max_pool((3, 3), (2, 2), (1, 1)));
                layers.push(BackboneLayer::residual_block(64, 64, 2));
                layers.push(BackboneLayer::residual_block(64, 128, 2));
                layers.push(BackboneLayer::residual_block(128, 256, 2));
                layers.push(BackboneLayer::residual_block(256, 512, 2));
            }
            EndToEndArchitecture::TextSnake => {
                // TextSnake backbone layers
                layers.push(BackboneLayer::conv_block(3, 64, (3, 3), (1, 1), (1, 1)));
                layers.push(BackboneLayer::conv_block(64, 128, (3, 3), (2, 2), (1, 1)));
                layers.push(BackboneLayer::conv_block(128, 256, (3, 3), (2, 2), (1, 1)));
                layers.push(BackboneLayer::conv_block(256, 512, (3, 3), (2, 2), (1, 1)));
            }
            _ => {
                // Default backbone
                layers.push(BackboneLayer::conv_block(3, 64, (3, 3), (1, 1), (1, 1)));
                layers.push(BackboneLayer::max_pool((2, 2), (2, 2), (0, 0)));
                layers.push(BackboneLayer::conv_block(64, 128, (3, 3), (1, 1), (1, 1)));
                layers.push(BackboneLayer::max_pool((2, 2), (2, 2), (0, 0)));
                layers.push(BackboneLayer::conv_block(128, 256, (3, 3), (1, 1), (1, 1)));
            }
        }

        layers
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<FeatureMap>> {
        let mut features = input.to_vec();
        let mut feature_maps = Vec::new();

        for layer in &self.layers {
            features = layer.forward(&features)?;
            // Create feature map (simplified)
            feature_maps.push(FeatureMap {
                height: 32,
                width: 32,
                channels: 256,
                features: features.clone(),
            });
        }

        Ok(feature_maps)
    }
}

impl BackboneLayer {
    fn conv_block(
        input_channels: usize,
        output_channels: usize,
        kernel_size: (u32, u32),
        stride: (u32, u32),
        padding: (u32, u32),
    ) -> Self {
        let conv = ConvBlock {
            conv: ConvLayer::new(
                input_channels,
                output_channels,
                kernel_size,
                stride,
                padding,
            ),
            batch_norm: Some(BatchNormLayer::new(output_channels)),
            activation: ActivationLayer::new(Activation::ReLU),
            dropout: None,
        };

        Self {
            layer_type: BackboneLayerType::Convolutional(conv),
            input_shape: (0, 0, input_channels as u32),
            output_shape: (0, 0, output_channels as u32),
            parameters: BackboneParameters {
                input_channels,
                output_channels,
                kernel_size,
                stride,
                padding,
                dilation: (1, 1),
                groups: 1,
            },
        }
    }

    fn max_pool(kernel_size: (u32, u32), stride: (u32, u32), padding: (u32, u32)) -> Self {
        let pool = PoolBlock {
            pool_type: PoolType::Max,
            kernel_size,
            stride,
            padding,
        };

        Self {
            layer_type: BackboneLayerType::Pooling(pool),
            input_shape: (0, 0, 0),
            output_shape: (0, 0, 0),
            parameters: BackboneParameters {
                input_channels: 0,
                output_channels: 0,
                kernel_size,
                stride,
                padding,
                dilation: (1, 1),
                groups: 1,
            },
        }
    }

    fn residual_block(input_channels: usize, output_channels: usize, num_blocks: usize) -> Self {
        let mut main_path = Vec::new();
        for _ in 0..num_blocks {
            main_path.push(BackboneLayer::conv_block(
                input_channels,
                output_channels,
                (3, 3),
                (1, 1),
                (1, 1),
            ));
        }

        let residual = ResidualBlock {
            main_path,
            shortcut: Some(ShortcutConnection {
                connection_type: ShortcutType::Identity,
                projection: None,
            }),
            activation: ActivationLayer::new(Activation::ReLU),
        };

        Self {
            layer_type: BackboneLayerType::Residual(residual),
            input_shape: (0, 0, input_channels as u32),
            output_shape: (0, 0, output_channels as u32),
            parameters: BackboneParameters {
                input_channels,
                output_channels,
                kernel_size: (3, 3),
                stride: (1, 1),
                padding: (1, 1),
                dilation: (1, 1),
                groups: 1,
            },
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        match &self.layer_type {
            BackboneLayerType::Convolutional(conv) => conv.forward(input),
            BackboneLayerType::Residual(residual) => residual.forward(input),
            BackboneLayerType::Dense(dense) => dense.forward(input),
            BackboneLayerType::Attention(attention) => attention.forward(input),
            BackboneLayerType::Pooling(pool) => pool.forward(input),
        }
    }
}

impl ConvBlock {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mut output = self.conv.forward(input)?;

        if let Some(ref bn) = self.batch_norm {
            output = bn.forward(&output)?;
        }

        output = self.activation.forward(&output)?;

        if let Some(ref dropout) = self.dropout {
            output = dropout.forward(&output)?;
        }

        Ok(output)
    }
}

impl ResidualBlock {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mut output = input.to_vec();

        // Main path
        for layer in &self.main_path {
            output = layer.forward(&output)?;
        }

        // Shortcut connection
        if let Some(ref shortcut) = self.shortcut {
            let shortcut_output = shortcut.forward(input)?;
            output = self.add_tensors(&output, &shortcut_output)?;
        }

        // Activation
        output = self.activation.forward(&output)?;

        Ok(output)
    }

    fn add_tensors(&self, a: &[f32], b: &[f32]) -> Result<Vec<f32>> {
        let mut result = Vec::with_capacity(a.len());
        for i in 0..a.len() {
            result.push(a[i] + b.get(i).unwrap_or(&0.0));
        }
        Ok(result)
    }
}

impl DenseBlock {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mut output = input.to_vec();

        for layer in &self.layers {
            output = layer.forward(&output)?;
        }

        Ok(output)
    }
}

impl AttentionBlock {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Simplified attention implementation
        let query = self.query_projection.forward(input)?;
        let key = self.key_projection.forward(input)?;
        let value = self.value_projection.forward(input)?;

        // Compute attention scores
        let attention_scores = self.compute_attention_scores(&query, &key)?;
        let attention_output = self.apply_attention(&attention_scores, &value)?;

        self.output_projection.forward(&attention_output)
    }

    fn compute_attention_scores(&self, query: &[f32], key: &[f32]) -> Result<Vec<f32>> {
        // Simplified attention score computation
        let mut scores = vec![0.0; query.len()];
        for i in 0..query.len() {
            scores[i] = query[i] * key[i % key.len()];
        }
        Ok(scores)
    }

    fn apply_attention(&self, scores: &[f32], value: &[f32]) -> Result<Vec<f32>> {
        // Simplified attention application
        let mut output = vec![0.0; value.len()];
        for i in 0..value.len() {
            output[i] = scores[i % scores.len()] * value[i];
        }
        Ok(output)
    }
}

impl PoolBlock {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        match self.pool_type {
            PoolType::Max => {
                let mut output = Vec::new();
                for chunk in input.chunks(4) {
                    output.push(chunk.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b)));
                }
                Ok(output)
            }
            PoolType::Average => {
                let mut output = Vec::new();
                for chunk in input.chunks(4) {
                    output.push(chunk.iter().sum::<f32>() / chunk.len() as f32);
                }
                Ok(output)
            }
            _ => Ok(input.to_vec()),
        }
    }
}

impl DetectionHead {
    fn new(config: &EndToEndConfig) -> Self {
        let mut layers = Vec::new();

        // Add detection layers
        for _ in 0..config.num_detection_heads {
            layers.push(DetectionLayer {
                layer_type: DetectionLayerType::Convolutional,
                input_channels: 256,
                output_channels: 1,
                kernel_size: (3, 3),
            });
        }

        Self {
            layers,
            output_channels: 1,
            use_fpn: config.use_multi_scale,
            use_attention: config.use_attention,
        }
    }

    fn forward(&self, features: &[FeatureMap]) -> Result<Vec<TextDetection>> {
        // Simplified detection - return placeholder results
        Ok(vec![TextDetection {
            bounding_box: TBox::new(0, 0, 100, 50),
            confidence: 0.9,
            polygon: vec![(0.0, 0.0), (100.0, 0.0), (100.0, 50.0), (0.0, 50.0)],
            text_direction: TextDirection::Horizontal,
        }])
    }
}

impl RecognitionHead {
    fn new(config: &EndToEndConfig) -> Self {
        let mut layers = Vec::new();

        // Add recognition layers
        for _ in 0..config.num_recognition_heads {
            layers.push(RecognitionLayer {
                layer_type: RecognitionLayerType::LSTM,
                input_size: 256,
                output_size: config.max_sequence_length,
                hidden_size: 128,
            });
        }

        Self {
            layers,
            output_vocab_size: 1000, // Placeholder
            use_attention: config.use_attention,
            use_lstm: true,
        }
    }

    fn forward(
        &self,
        features: &[FeatureMap],
        detections: &[TextDetection],
    ) -> Result<Vec<TextRecognition>> {
        // Simplified recognition - return placeholder results
        Ok(vec![TextRecognition {
            text: "End-to-End Recognition".to_string(),
            confidence: 0.9,
            character_scores: vec![0.9; 10],
            language: Some("en".to_string()),
        }])
    }
}

impl FusionModule {
    fn new(config: &EndToEndConfig) -> Self {
        let attention_weights = if config.use_attention {
            Some(AttentionWeights::new())
        } else {
            None
        };

        let gating_weights = if config.use_attention {
            Some(GatingWeights::new())
        } else {
            None
        };

        Self {
            fusion_type: FusionType::Concatenation,
            attention_weights,
            gating_weights,
        }
    }

    fn fuse(
        &self,
        detections: &[TextDetection],
        recognitions: &[TextRecognition],
    ) -> Result<Vec<FusedResult>> {
        let mut fused_results = Vec::new();

        for (i, detection) in detections.iter().enumerate() {
            if let Some(recognition) = recognitions.get(i) {
                fused_results.push(FusedResult {
                    detection: detection.clone(),
                    recognition: recognition.clone(),
                    combined_confidence: (detection.confidence + recognition.confidence) / 2.0,
                });
            }
        }

        Ok(fused_results)
    }
}

impl PostProcessor {
    fn new(config: &EndToEndConfig) -> Self {
        Self {
            nms_threshold: 0.5,
            confidence_threshold: config.confidence_threshold,
            max_candidates: 1000,
            use_polygon_nms: true,
        }
    }

    fn process(&self, fused_results: &[FusedResult]) -> Result<EndToEndOutput> {
        // Simplified post-processing
        let detections: Vec<TextDetection> =
            fused_results.iter().map(|r| r.detection.clone()).collect();
        let recognitions: Vec<TextRecognition> = fused_results
            .iter()
            .map(|r| r.recognition.clone())
            .collect();

        Ok(EndToEndOutput {
            detections,
            recognitions,
            fused_results: fused_results.to_vec(),
            confidence: 0.9,
            processing_time_ms: 300,
        })
    }
}

impl ShortcutConnection {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        match self.connection_type {
            ShortcutType::Identity => Ok(input.to_vec()),
            ShortcutType::Projection => {
                if let Some(ref projection) = self.projection {
                    projection.forward(input)
                } else {
                    Ok(input.to_vec())
                }
            }
            ShortcutType::Convolution => Ok(input.to_vec()), // Placeholder
        }
    }
}

impl AttentionWeights {
    fn new() -> Self {
        Self {
            query_weights: vec![vec![0.0; 128]; 128],
            key_weights: vec![vec![0.0; 128]; 128],
            value_weights: vec![vec![0.0; 128]; 128],
            output_weights: vec![vec![0.0; 128]; 128],
        }
    }
}

impl GatingWeights {
    fn new() -> Self {
        Self {
            gate_weights: vec![vec![0.0; 128]; 128],
            gate_bias: vec![0.0; 128],
        }
    }
}

impl LinearLayer {
    fn new(input_size: usize, output_size: usize) -> Self {
        Self {
            weight: vec![vec![0.0; input_size]; output_size],
            bias: Some(vec![0.0; output_size]),
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mut output = vec![0.0; self.weight.len()];

        for (i, row) in self.weight.iter().enumerate() {
            for (j, &weight) in row.iter().enumerate() {
                if j < input.len() {
                    output[i] += weight * input[j];
                }
            }
            if let Some(ref bias) = self.bias {
                if i < bias.len() {
                    output[i] += bias[i];
                }
            }
        }

        Ok(output)
    }
}

impl ConvLayer {
    fn new(
        input_channels: usize,
        output_channels: usize,
        kernel_size: (u32, u32),
        stride: (u32, u32),
        padding: (u32, u32),
    ) -> Self {
        Self {
            input_channels,
            output_channels,
            kernel_size,
            stride,
            padding,
            dilation: (1, 1),
            groups: 1,
            bias: true,
            weights: vec![
                vec![
                    vec![vec![0.0; kernel_size.1 as usize]; kernel_size.0 as usize];
                    input_channels
                ];
                output_channels
            ],
            bias_weights: Some(vec![0.0; output_channels]),
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Simplified convolution implementation
        let mut output = vec![0.0; input.len()];
        for i in 0..input.len() {
            output[i] = input[i] * 0.5; // Simplified operation
        }
        Ok(output)
    }
}

impl BatchNormLayer {
    fn new(num_features: usize) -> Self {
        Self {
            num_features,
            eps: 1e-5,
            momentum: 0.1,
            weight: vec![1.0; num_features],
            bias: vec![0.0; num_features],
            running_mean: vec![0.0; num_features],
            running_var: vec![1.0; num_features],
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mean = input.iter().sum::<f32>() / input.len() as f32;
        let variance = input.iter().map(|&x| (x - mean).powi(2)).sum::<f32>() / input.len() as f32;
        let std = (variance + self.eps).sqrt();

        let mut output = vec![0.0; input.len()];
        for i in 0..input.len() {
            let normalized = (input[i] - mean) / std;
            output[i] =
                normalized * self.weight[i % self.weight.len()] + self.bias[i % self.bias.len()];
        }

        Ok(output)
    }
}

impl ActivationLayer {
    fn new(activation: Activation) -> Self {
        Self { activation }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        match self.activation {
            Activation::ReLU => Ok(input.iter().map(|&x| x.max(0.0)).collect()),
            Activation::LeakyReLU(alpha) => Ok(input
                .iter()
                .map(|&x| if x > 0.0 { x } else { alpha * x })
                .collect()),
            Activation::ELU(alpha) => Ok(input
                .iter()
                .map(|&x| if x > 0.0 { x } else { alpha * (x.exp() - 1.0) })
                .collect()),
            Activation::GELU => Ok(input
                .iter()
                .map(|&x| 0.5 * x * (1.0 + (x * 0.79788456).tanh()))
                .collect()),
            Activation::Swish => Ok(input
                .iter()
                .map(|&x| x * (1.0 + (-x).exp()).recip())
                .collect()),
            Activation::Sigmoid => Ok(input.iter().map(|&x| 1.0 / (1.0 + (-x).exp())).collect()),
            Activation::Tanh => Ok(input.iter().map(|&x| x.tanh()).collect()),
            Activation::Softmax => {
                let max_val = input.iter().fold(f32::NEG_INFINITY, |a, &b| a.max(b));
                let exp_sum: f32 = input.iter().map(|&x| (x - max_val).exp()).sum();
                Ok(input
                    .iter()
                    .map(|&x| (x - max_val).exp() / exp_sum)
                    .collect())
            }
        }
    }
}

impl DropoutLayer {
    fn new(rate: f32) -> Self {
        Self {
            rate,
            training: true,
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        if self.training {
            Ok(input
                .iter()
                .map(|&x| {
                    if fastrand::f32() < self.rate {
                        0.0
                    } else {
                        x / (1.0 - self.rate)
                    }
                })
                .collect())
        } else {
            Ok(input.to_vec())
        }
    }
}

/// Builder for End-to-End models
pub struct EndToEndModelBuilder {
    config: Option<EndToEndConfig>,
}

impl EndToEndModelBuilder {
    /// Create a new builder
    pub fn new() -> Self {
        Self { config: None }
    }

    /// Set the model configuration
    pub fn with_config(mut self, config: EndToEndConfig) -> Self {
        self.config = Some(config);
        self
    }

    /// Build the model
    pub fn build(self) -> Result<EndToEndModel> {
        let config = self
            .config
            .ok_or_else(|| OcrError::ModelNotFound("Configuration not provided".to_string()))?;

        Ok(EndToEndModel::new(config))
    }
}

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