ocr 0.1.1

//! Convolutional Neural Network (CNN) model implementation for OCR
//!
//! This module provides CNN-based models for optical character recognition,
//! including traditional CNN architectures and modern CNN variants.

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

/// Configuration for CNN OCR models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CNNConfig {
    /// Model architecture type
    pub architecture: CNNArchitecture,
    /// 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),
    /// Number of output classes
    pub num_classes: usize,
    /// 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 batch normalization
    pub use_batch_norm: bool,
    /// Whether to use dropout
    pub use_dropout: bool,
    /// Dropout rate
    pub dropout_rate: f32,
    /// Whether to use data augmentation
    pub use_data_augmentation: bool,
    /// Learning rate
    pub learning_rate: f32,
    /// Weight decay
    pub weight_decay: f32,
}

/// CNN architecture variants
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum CNNArchitecture {
    /// LeNet-5 architecture
    LeNet5,
    /// AlexNet architecture
    AlexNet,
    /// VGG architecture
    VGG,
    /// ResNet architecture
    ResNet,
    /// DenseNet architecture
    DenseNet,
    /// EfficientNet architecture
    EfficientNet,
    /// MobileNet architecture
    MobileNet,
    /// Custom CNN architecture
    Custom(String),
}

/// CNN OCR model implementation
pub struct CNNModel {
    config: CNNConfig,
    model_loaded: bool,
    backbone: CNNBackbone,
    classifier: CNNClassifier,
    feature_extractor: FeatureExtractor,
}

/// CNN backbone network
pub struct CNNBackbone {
    layers: Vec<CNNLayer>,
    architecture: CNNArchitecture,
}

/// Individual CNN layer
pub struct CNNLayer {
    layer_type: CNNLayerType,
    input_shape: (u32, u32, u32),
    output_shape: (u32, u32, u32),
    parameters: LayerParameters,
}

/// CNN layer types
#[derive(Debug, Clone)]
pub enum CNNLayerType {
    Convolutional(ConvLayer),
    Pooling(PoolLayer),
    BatchNorm(BatchNormLayer),
    Dropout(DropoutLayer),
    Activation(ActivationLayer),
    Dense(DenseLayer),
}

/// Convolutional layer
#[derive(Debug, Clone)]
pub struct ConvLayer {
    filters: 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>>,
}

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

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

/// 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>,
}

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

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

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

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

/// Layer parameters
#[derive(Debug, Clone)]
pub struct LayerParameters {
    input_channels: usize,
    output_channels: usize,
    kernel_size: (u32, u32),
    stride: (u32, u32),
    padding: (u32, u32),
}

/// CNN classifier head
pub struct CNNClassifier {
    layers: Vec<CNNLayer>,
    num_classes: usize,
}

/// Feature extractor
pub struct FeatureExtractor {
    layers: Vec<CNNLayer>,
    output_dim: usize,
}

impl CNNModel {
    /// Create a new CNN model
    pub fn new(config: CNNConfig) -> Self {
        let backbone = CNNBackbone::new(&config);
        let classifier = CNNClassifier::new(config.num_classes);
        let feature_extractor = FeatureExtractor::new(config.input_shape);

        Self {
            config,
            model_loaded: false,
            backbone,
            classifier,
            feature_extractor,
        }
    }

    /// 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) -> &CNNConfig {
        &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) -> &CNNArchitecture {
        &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)
    }

    /// Preprocess image for CNN input
    fn preprocess_image(&self, image: &OcrImage) -> Result<Vec<f32>> {
        // Convert image to the expected input format
        let (height, width, channels) = self.config.input_shape;
        let input_size = (height * width * channels) as usize;

        // For now, return a placeholder - this would be implemented with actual image processing
        Ok(vec![0.0; input_size])
    }

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

        // Classify using classifier head
        let output = self.classifier.forward(&features)?;

        Ok(output)
    }
}

impl OcrModel for CNNModel {
    fn model_type(&self) -> ModelType {
        ModelType::CNN
    }

    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 CNN model
        // 3. Postprocess the output

        let result = RecognitionResult {
            text: "CNN OCR Result".to_string(),
            confidence: 0.88,
            bounding_boxes: vec![],
            character_results: vec![],
            word_results: vec![],
            line_results: vec![],
            language: Some("en".to_string()),
            model_type: ModelType::CNN,
            processing_time_ms: 100,
        };

        Ok(result)
    }
}

impl CNNBackbone {
    fn new(config: &CNNConfig) -> Self {
        let layers = Self::create_layers(config);
        Self {
            layers,
            architecture: config.architecture.clone(),
        }
    }

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

        match config.architecture {
            CNNArchitecture::LeNet5 => {
                // LeNet-5 architecture
                layers.push(CNNLayer::conv2d(1, 6, (5, 5), (1, 1), (0, 0)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::max_pool((2, 2), (2, 2), (0, 0)));
                layers.push(CNNLayer::conv2d(6, 16, (5, 5), (1, 1), (0, 0)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::max_pool((2, 2), (2, 2), (0, 0)));
            }
            CNNArchitecture::AlexNet => {
                // AlexNet architecture
                layers.push(CNNLayer::conv2d(3, 96, (11, 11), (4, 4), (0, 0)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::max_pool((3, 3), (2, 2), (0, 0)));
                layers.push(CNNLayer::conv2d(96, 256, (5, 5), (1, 1), (2, 2)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::max_pool((3, 3), (2, 2), (0, 0)));
                layers.push(CNNLayer::conv2d(256, 384, (3, 3), (1, 1), (1, 1)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::conv2d(384, 384, (3, 3), (1, 1), (1, 1)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::conv2d(384, 256, (3, 3), (1, 1), (1, 1)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::max_pool((3, 3), (2, 2), (0, 0)));
            }
            _ => {
                // Default architecture
                layers.push(CNNLayer::conv2d(3, 32, (3, 3), (1, 1), (1, 1)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::max_pool((2, 2), (2, 2), (0, 0)));
                layers.push(CNNLayer::conv2d(32, 64, (3, 3), (1, 1), (1, 1)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::max_pool((2, 2), (2, 2), (0, 0)));
                layers.push(CNNLayer::conv2d(64, 128, (3, 3), (1, 1), (1, 1)));
                layers.push(CNNLayer::activation(Activation::ReLU));
                layers.push(CNNLayer::max_pool((2, 2), (2, 2), (0, 0)));
            }
        }

        layers
    }

    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 CNNLayer {
    fn conv2d(
        input_channels: usize,
        output_channels: usize,
        kernel_size: (u32, u32),
        stride: (u32, u32),
        padding: (u32, u32),
    ) -> Self {
        let conv_layer = ConvLayer {
            filters: 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]),
        };

        Self {
            layer_type: CNNLayerType::Convolutional(conv_layer),
            input_shape: (0, 0, input_channels as u32),
            output_shape: (0, 0, output_channels as u32),
            parameters: LayerParameters {
                input_channels,
                output_channels,
                kernel_size,
                stride,
                padding,
            },
        }
    }

    fn activation(activation: Activation) -> Self {
        Self {
            layer_type: CNNLayerType::Activation(ActivationLayer { activation }),
            input_shape: (0, 0, 0),
            output_shape: (0, 0, 0),
            parameters: LayerParameters {
                input_channels: 0,
                output_channels: 0,
                kernel_size: (0, 0),
                stride: (0, 0),
                padding: (0, 0),
            },
        }
    }

    fn max_pool(kernel_size: (u32, u32), stride: (u32, u32), padding: (u32, u32)) -> Self {
        Self {
            layer_type: CNNLayerType::Pooling(PoolLayer {
                pool_type: PoolType::Max,
                kernel_size,
                stride,
                padding,
            }),
            input_shape: (0, 0, 0),
            output_shape: (0, 0, 0),
            parameters: LayerParameters {
                input_channels: 0,
                output_channels: 0,
                kernel_size,
                stride,
                padding,
            },
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        match &self.layer_type {
            CNNLayerType::Convolutional(conv) => conv.forward(input),
            CNNLayerType::Pooling(pool) => pool.forward(input),
            CNNLayerType::BatchNorm(bn) => bn.forward(input),
            CNNLayerType::Dropout(dropout) => dropout.forward(input),
            CNNLayerType::Activation(activation) => activation.forward(input),
            CNNLayerType::Dense(dense) => dense.forward(input),
        }
    }
}

impl ConvLayer {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Simplified convolution implementation
        // In practice, this would be much more complex with proper convolution
        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 PoolLayer {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Simplified pooling implementation
        match self.pool_type {
            PoolType::Max => {
                // Simplified max pooling
                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 => {
                // Simplified average pooling
                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 BatchNormLayer {
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Simplified batch normalization
        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 DropoutLayer {
    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())
        }
    }
}

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

        for (i, row) in self.weights.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 CNNClassifier {
    fn new(num_classes: usize) -> Self {
        let mut layers = Vec::new();

        // Add dense layers for classification
        layers.push(CNNLayer {
            layer_type: CNNLayerType::Dense(DenseLayer {
                input_size: 128, // Placeholder
                output_size: 64,
                weights: vec![vec![0.0; 128]; 64],
                bias: Some(vec![0.0; 64]),
            }),
            input_shape: (0, 0, 0),
            output_shape: (0, 0, 0),
            parameters: LayerParameters {
                input_channels: 0,
                output_channels: 0,
                kernel_size: (0, 0),
                stride: (0, 0),
                padding: (0, 0),
            },
        });

        layers.push(CNNLayer::activation(Activation::ReLU));

        layers.push(CNNLayer {
            layer_type: CNNLayerType::Dense(DenseLayer {
                input_size: 64,
                output_size: num_classes,
                weights: vec![vec![0.0; 64]; num_classes],
                bias: Some(vec![0.0; num_classes]),
            }),
            input_shape: (0, 0, 0),
            output_shape: (0, 0, 0),
            parameters: LayerParameters {
                input_channels: 0,
                output_channels: 0,
                kernel_size: (0, 0),
                stride: (0, 0),
                padding: (0, 0),
            },
        });

        Self {
            layers,
            num_classes,
        }
    }

    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 FeatureExtractor {
    fn new(input_shape: (u32, u32, u32)) -> Self {
        let layers = Vec::new(); // Placeholder
        Self {
            layers,
            output_dim: 128, // Placeholder
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Simplified feature extraction
        Ok(input.to_vec())
    }
}

/// Builder for CNN models
pub struct CNNModelBuilder {
    config: Option<CNNConfig>,
}

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

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

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

        Ok(CNNModel::new(config))
    }
}

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