//! Hybrid OCR model implementation
//!
//! This module provides hybrid models that combine different neural network
//! architectures for optimal OCR performance.

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

/// Configuration for Hybrid OCR models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HybridConfig {
    /// Model architecture type
    pub architecture: HybridArchitecture,
    /// 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>,
    /// Fusion strategy
    pub fusion_strategy: FusionStrategy,
    /// Ensemble weights
    pub ensemble_weights: Vec<f32>,
    /// Whether to use attention fusion
    pub use_attention_fusion: bool,
    /// Whether to use late fusion
    pub use_late_fusion: bool,
    /// Whether to use early fusion
    pub use_early_fusion: bool,
}

/// Hybrid architecture variants
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum HybridArchitecture {
    /// CNN + LSTM hybrid
    CNNLSTM,
    /// CNN + Transformer hybrid
    CNNTransformer,
    /// ViT + LSTM hybrid
    ViTLSTM,
    /// CNN + ViT + LSTM hybrid
    CNNViTLSTM,
    /// Multi-scale CNN + Transformer
    MultiScaleCNNTransformer,
    /// Custom hybrid architecture
    Custom(String),
}

/// Fusion strategies
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum FusionStrategy {
    /// Simple concatenation
    Concatenation,
    /// Weighted average
    WeightedAverage,
    /// Attention-based fusion
    Attention,
    /// Gating mechanism
    Gating,
    /// Mixture of experts
    MixtureOfExperts,
    /// Custom fusion
    Custom(String),
}

/// Hybrid OCR model implementation
pub struct HybridModel {
    config: HybridConfig,
    model_loaded: bool,
    components: Vec<ModelComponent>,
    fusion_layer: FusionLayer,
    output_head: OutputHead,
}

/// Individual model component
pub struct ModelComponent {
    component_type: ComponentType,
    model: Box<dyn OcrModel>,
    weight: f32,
    is_active: bool,
}

/// Component types
#[derive(Debug, Clone)]
pub enum ComponentType {
    CNN,
    LSTM,
    Transformer,
    VisionTransformer,
    Custom(String),
}

/// Fusion layer for combining model outputs
pub struct FusionLayer {
    strategy: FusionStrategy,
    attention_weights: Option<AttentionWeights>,
    gating_weights: Option<GatingWeights>,
    mixture_weights: Option<MixtureWeights>,
}

/// Attention weights for attention-based fusion
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 for gating mechanism
pub struct GatingWeights {
    gate_weights: Vec<Vec<f32>>,
    gate_bias: Vec<f32>,
}

/// Mixture weights for mixture of experts
pub struct MixtureWeights {
    expert_weights: Vec<Vec<f32>>,
    gating_network: Vec<Vec<f32>>,
}

/// Output head for final prediction
pub struct OutputHead {
    layers: Vec<OutputLayer>,
    num_classes: usize,
}

/// Output layer
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 HybridModel {
    /// Create a new Hybrid model
    pub fn new(config: HybridConfig) -> Self {
        let components = Self::create_components(&config);
        let fusion_layer = FusionLayer::new(&config);
        let output_head = OutputHead::new(config.supported_languages.len());

        Self {
            config,
            model_loaded: false,
            components,
            fusion_layer,
            output_head,
        }
    }

    /// Create model components based on architecture
    fn create_components(config: &HybridConfig) -> Vec<ModelComponent> {
        let mut components = Vec::new();

        match config.architecture {
            HybridArchitecture::CNNLSTM => {
                // Add CNN component
                components.push(ModelComponent {
                    component_type: ComponentType::CNN,
                    model: Box::new(create_dummy_model(ModelType::CNN)),
                    weight: 0.5,
                    is_active: true,
                });

                // Add LSTM component
                components.push(ModelComponent {
                    component_type: ComponentType::LSTM,
                    model: Box::new(create_dummy_model(ModelType::LSTM)),
                    weight: 0.5,
                    is_active: true,
                });
            }
            HybridArchitecture::CNNTransformer => {
                // Add CNN component
                components.push(ModelComponent {
                    component_type: ComponentType::CNN,
                    model: Box::new(create_dummy_model(ModelType::CNN)),
                    weight: 0.4,
                    is_active: true,
                });

                // Add Transformer component
                components.push(ModelComponent {
                    component_type: ComponentType::Transformer,
                    model: Box::new(create_dummy_model(ModelType::Transformer)),
                    weight: 0.6,
                    is_active: true,
                });
            }
            HybridArchitecture::ViTLSTM => {
                // Add ViT component
                components.push(ModelComponent {
                    component_type: ComponentType::VisionTransformer,
                    model: Box::new(create_dummy_model(ModelType::VisionTransformer)),
                    weight: 0.6,
                    is_active: true,
                });

                // Add LSTM component
                components.push(ModelComponent {
                    component_type: ComponentType::LSTM,
                    model: Box::new(create_dummy_model(ModelType::LSTM)),
                    weight: 0.4,
                    is_active: true,
                });
            }
            HybridArchitecture::CNNViTLSTM => {
                // Add CNN component
                components.push(ModelComponent {
                    component_type: ComponentType::CNN,
                    model: Box::new(create_dummy_model(ModelType::CNN)),
                    weight: 0.3,
                    is_active: true,
                });

                // Add ViT component
                components.push(ModelComponent {
                    component_type: ComponentType::VisionTransformer,
                    model: Box::new(create_dummy_model(ModelType::VisionTransformer)),
                    weight: 0.4,
                    is_active: true,
                });

                // Add LSTM component
                components.push(ModelComponent {
                    component_type: ComponentType::LSTM,
                    model: Box::new(create_dummy_model(ModelType::LSTM)),
                    weight: 0.3,
                    is_active: true,
                });
            }
            _ => {
                // Default: CNN + LSTM
                components.push(ModelComponent {
                    component_type: ComponentType::CNN,
                    model: Box::new(create_dummy_model(ModelType::CNN)),
                    weight: 0.5,
                    is_active: true,
                });

                components.push(ModelComponent {
                    component_type: ComponentType::LSTM,
                    model: Box::new(create_dummy_model(ModelType::LSTM)),
                    weight: 0.5,
                    is_active: true,
                });
            }
        }

        components
    }

    /// 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) -> &HybridConfig {
        &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) -> &HybridArchitecture {
        &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 all components
    fn forward_components(&self, input: &[u8]) -> Result<Vec<RecognitionResult>> {
        let mut results = Vec::new();

        for component in &self.components {
            if component.is_active {
                let result = component.model.predict(input)?;
                results.push(result);
            }
        }

        Ok(results)
    }

    /// Fuse component outputs
    fn fuse_outputs(&self, outputs: &[RecognitionResult]) -> Result<RecognitionResult> {
        self.fusion_layer.fuse(outputs)
    }
}

impl OcrModel for HybridModel {
    fn model_type(&self) -> ModelType {
        ModelType::Hybrid
    }

    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());
        }

        // Forward pass through all components
        let component_outputs = self.forward_components(input)?;

        // Fuse the outputs
        let fused_output = self.fuse_outputs(&component_outputs)?;

        // Apply output head
        let final_output = self.output_head.process(&fused_output)?;

        Ok(final_output)
    }
}

impl FusionLayer {
    fn new(config: &HybridConfig) -> Self {
        let attention_weights = if config.use_attention_fusion {
            Some(AttentionWeights::new())
        } else {
            None
        };

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

        let mixture_weights = if config.fusion_strategy == FusionStrategy::MixtureOfExperts {
            Some(MixtureWeights::new())
        } else {
            None
        };

        Self {
            strategy: config.fusion_strategy.clone(),
            attention_weights,
            gating_weights,
            mixture_weights,
        }
    }

    fn fuse(&self, outputs: &[RecognitionResult]) -> Result<RecognitionResult> {
        match self.strategy {
            FusionStrategy::Concatenation => self.concatenate_outputs(outputs),
            FusionStrategy::WeightedAverage => self.weighted_average_outputs(outputs),
            FusionStrategy::Attention => self.attention_fuse_outputs(outputs),
            FusionStrategy::Gating => self.gating_fuse_outputs(outputs),
            FusionStrategy::MixtureOfExperts => self.mixture_fuse_outputs(outputs),
            FusionStrategy::Custom(_) => self.custom_fuse_outputs(outputs),
        }
    }

    fn concatenate_outputs(&self, outputs: &[RecognitionResult]) -> Result<RecognitionResult> {
        if outputs.is_empty() {
            return Err(OcrError::ModelNotFound("No outputs to fuse".to_string()).into());
        }

        // Simple concatenation of text outputs
        let mut fused_text = String::new();
        let mut total_confidence = 0.0;
        let mut total_processing_time = 0;

        for (i, output) in outputs.iter().enumerate() {
            if i > 0 {
                fused_text.push(' ');
            }
            fused_text.push_str(&output.text);
            total_confidence += output.confidence;
            total_processing_time += output.processing_time_ms;
        }

        let avg_confidence = total_confidence / outputs.len() as f32;

        Ok(RecognitionResult {
            text: fused_text,
            confidence: avg_confidence,
            bounding_boxes: outputs[0].bounding_boxes.clone(),
            character_results: outputs[0].character_results.clone(),
            word_results: outputs[0].word_results.clone(),
            line_results: outputs[0].line_results.clone(),
            language: outputs[0].language.clone(),
            model_type: ModelType::Hybrid,
            processing_time_ms: total_processing_time,
        })
    }

    fn weighted_average_outputs(&self, outputs: &[RecognitionResult]) -> Result<RecognitionResult> {
        if outputs.is_empty() {
            return Err(OcrError::ModelNotFound("No outputs to fuse".to_string()).into());
        }

        // Weighted average based on confidence
        let total_weight: f32 = outputs.iter().map(|o| o.confidence).sum();
        let mut weighted_text = String::new();
        let mut total_processing_time = 0;

        for output in outputs {
            let weight = output.confidence / total_weight;
            if !weighted_text.is_empty() {
                weighted_text.push(' ');
            }
            weighted_text.push_str(&output.text);
            total_processing_time += output.processing_time_ms;
        }

        Ok(RecognitionResult {
            text: weighted_text,
            confidence: total_weight / outputs.len() as f32,
            bounding_boxes: outputs[0].bounding_boxes.clone(),
            character_results: outputs[0].character_results.clone(),
            word_results: outputs[0].word_results.clone(),
            line_results: outputs[0].line_results.clone(),
            language: outputs[0].language.clone(),
            model_type: ModelType::Hybrid,
            processing_time_ms: total_processing_time,
        })
    }

    fn attention_fuse_outputs(&self, outputs: &[RecognitionResult]) -> Result<RecognitionResult> {
        // Simplified attention-based fusion
        self.weighted_average_outputs(outputs)
    }

    fn gating_fuse_outputs(&self, outputs: &[RecognitionResult]) -> Result<RecognitionResult> {
        // Simplified gating-based fusion
        self.weighted_average_outputs(outputs)
    }

    fn mixture_fuse_outputs(&self, outputs: &[RecognitionResult]) -> Result<RecognitionResult> {
        // Simplified mixture of experts fusion
        self.weighted_average_outputs(outputs)
    }

    fn custom_fuse_outputs(&self, outputs: &[RecognitionResult]) -> Result<RecognitionResult> {
        // Custom fusion strategy
        self.weighted_average_outputs(outputs)
    }
}

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 MixtureWeights {
    fn new() -> Self {
        Self {
            expert_weights: vec![vec![0.0; 128]; 128],
            gating_network: vec![vec![0.0; 128]; 128],
        }
    }
}

impl OutputHead {
    fn new(num_classes: usize) -> Self {
        let mut layers = Vec::new();

        // Add dense layer
        layers.push(OutputLayer {
            layer_type: OutputLayerType::Dense,
            input_size: 128,
            output_size: 64,
            weights: vec![vec![0.0; 128]; 64],
            bias: Some(vec![0.0; 64]),
        });

        // Add final classification layer
        layers.push(OutputLayer {
            layer_type: OutputLayerType::Dense,
            input_size: 64,
            output_size: num_classes,
            weights: vec![vec![0.0; 64]; num_classes],
            bias: Some(vec![0.0; num_classes]),
        });

        Self {
            layers,
            num_classes,
        }
    }

    fn process(&self, input: &RecognitionResult) -> Result<RecognitionResult> {
        // Simplified processing - just return the input with updated metadata
        let mut result = input.clone();
        result.model_type = ModelType::Hybrid;
        Ok(result)
    }
}

impl OutputLayer {
    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)
    }
}

/// Create a dummy model for testing
fn create_dummy_model(model_type: ModelType) -> impl OcrModel {
    struct DummyModel {
        model_type: ModelType,
    }

    impl OcrModel for DummyModel {
        fn model_type(&self) -> ModelType {
            self.model_type.clone()
        }

        fn supported_languages(&self) -> Vec<LanguageVariant> {
            vec![LanguageVariant::English]
        }

        fn supports_language(&self, _language: &LanguageVariant) -> bool {
            true
        }

        fn input_shape(&self) -> (usize, usize, usize) {
            (224, 224, 3)
        }

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

        fn predict(&self, _input: &[u8]) -> Result<RecognitionResult> {
            Ok(RecognitionResult {
                text: format!("Dummy {:?} Result", self.model_type),
                confidence: 0.8,
                bounding_boxes: vec![],
                character_results: vec![],
                word_results: vec![],
                line_results: vec![],
                language: Some("en".to_string()),
                model_type: self.model_type.clone(),
                processing_time_ms: 100,
            })
        }
    }

    DummyModel { model_type }
}

/// Builder for Hybrid models
pub struct HybridModelBuilder {
    config: Option<HybridConfig>,
}

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

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

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

        Ok(HybridModel::new(config))
    }
}

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