ocr 0.1.1

//! Vision Transformer (ViT) model implementation for OCR
//!
//! This module provides a Vision Transformer model specifically designed
//! for optical character recognition tasks.

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 Vision Transformer OCR models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ViTConfig {
    /// Model architecture type
    pub architecture: ViTArchitecture,
    /// 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),
    /// Patch size for image segmentation
    pub patch_size: (u32, u32),
    /// Number of transformer layers
    pub num_layers: usize,
    /// Hidden dimension size
    pub hidden_size: usize,
    /// Number of attention heads
    pub num_attention_heads: usize,
    /// MLP hidden dimension multiplier
    pub mlp_ratio: f32,
    /// Dropout rate
    pub dropout_rate: f32,
    /// Attention dropout rate
    pub attention_dropout_rate: f32,
    /// Layer normalization epsilon
    pub layer_norm_eps: f32,
    /// Maximum sequence length
    pub max_sequence_length: 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 learned positional embeddings
    pub use_learned_pos_embedding: bool,
    /// Whether to use class token
    pub use_class_token: bool,
    /// Whether to use distillation token
    pub use_distillation_token: bool,
}

/// Vision Transformer architecture variants
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum ViTArchitecture {
    /// Standard Vision Transformer
    ViT,
    /// ViT with hybrid architecture (CNN + Transformer)
    HybridViT,
    /// ViT with hierarchical structure
    HierarchicalViT,
    /// ViT with deformable attention
    DeformableViT,
    /// Custom ViT architecture
    Custom(String),
}

/// Vision Transformer OCR model implementation
pub struct ViTModel {
    config: ViTConfig,
    model_loaded: bool,
    patch_embedding: PatchEmbedding,
    position_embedding: PositionEmbedding,
    transformer_blocks: Vec<TransformerBlock>,
    class_token: Option<ClassToken>,
    distillation_token: Option<DistillationToken>,
    head: ClassificationHead,
}

/// Patch embedding layer
pub struct PatchEmbedding {
    patch_size: (u32, u32),
    hidden_size: usize,
    projection: LinearLayer,
    norm: LayerNorm,
}

/// Position embedding layer
pub struct PositionEmbedding {
    num_patches: usize,
    hidden_size: usize,
    embeddings: Vec<Vec<f32>>,
    dropout: Dropout,
}

/// Transformer block for ViT
pub struct TransformerBlock {
    attention: MultiHeadSelfAttention,
    mlp: MLP,
    norm1: LayerNorm,
    norm2: LayerNorm,
    dropout: Dropout,
}

/// Multi-head self-attention for ViT
pub struct MultiHeadSelfAttention {
    num_heads: usize,
    head_dim: usize,
    scale: f32,
    query: LinearLayer,
    key: LinearLayer,
    value: LinearLayer,
    output: LinearLayer,
    dropout: Dropout,
}

/// MLP (Multi-Layer Perceptron) for ViT
pub struct MLP {
    fc1: LinearLayer,
    fc2: LinearLayer,
    activation: Activation,
    dropout: Dropout,
}

/// Class token for classification
pub struct ClassToken {
    token: Vec<f32>,
    hidden_size: usize,
}

/// Distillation token for knowledge distillation
pub struct DistillationToken {
    token: Vec<f32>,
    hidden_size: usize,
}

/// Classification head
pub struct ClassificationHead {
    pre_logits: Option<LinearLayer>,
    head: LinearLayer,
    dropout: Dropout,
}

/// Linear layer implementation
pub struct LinearLayer {
    weight: Vec<Vec<f32>>,
    bias: Option<Vec<f32>>,
}

/// Layer normalization
pub struct LayerNorm {
    weight: Vec<f32>,
    bias: Vec<f32>,
    eps: f32,
}

/// Dropout layer
pub struct Dropout {
    rate: f32,
    training: bool,
}

/// Activation functions
#[derive(Debug, Clone)]
pub enum Activation {
    GELU,
    ReLU,
    Swish,
}

impl ViTModel {
    /// Create a new ViT model
    pub fn new(config: ViTConfig) -> Self {
        let num_patches = Self::calculate_num_patches(config.input_shape, config.patch_size);

        let patch_embedding = PatchEmbedding::new(
            config.patch_size,
            config.hidden_size,
            config.input_shape.2 as usize,
        );

        let position_embedding = PositionEmbedding::new(
            num_patches,
            config.hidden_size,
            config.use_learned_pos_embedding,
        );

        let transformer_blocks = (0..config.num_layers)
            .map(|_| {
                TransformerBlock::new(
                    config.hidden_size,
                    config.num_attention_heads,
                    config.mlp_ratio,
                    config.dropout_rate,
                    config.attention_dropout_rate,
                    config.layer_norm_eps,
                )
            })
            .collect();

        let class_token = if config.use_class_token {
            Some(ClassToken::new(config.hidden_size))
        } else {
            None
        };

        let distillation_token = if config.use_distillation_token {
            Some(DistillationToken::new(config.hidden_size))
        } else {
            None
        };

        let head = ClassificationHead::new(
            config.hidden_size,
            config.max_sequence_length,
            config.dropout_rate,
        );

        Self {
            config,
            model_loaded: false,
            patch_embedding,
            position_embedding,
            transformer_blocks,
            class_token,
            distillation_token,
            head,
        }
    }

    /// Calculate number of patches
    fn calculate_num_patches(input_shape: (u32, u32, u32), patch_size: (u32, u32)) -> usize {
        let (height, width, _) = input_shape;
        let (patch_height, patch_width) = patch_size;
        ((height / patch_height) * (width / patch_width)) as usize
    }

    /// 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) -> &ViTConfig {
        &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) -> &ViTArchitecture {
        &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 ViT input
    fn preprocess_image(&self, image: &OcrImage) -> Result<Vec<f32>> {
        // Convert image to patches
        let (height, width, channels) = self.config.input_shape;
        let (patch_height, patch_width) = self.config.patch_size;

        let num_patches =
            Self::calculate_num_patches(self.config.input_shape, self.config.patch_size);

        // For now, return a placeholder - this would be implemented with actual image processing
        let input_size = num_patches * self.config.hidden_size;
        Ok(vec![0.0; input_size])
    }

    /// Forward pass through the model
    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Patch embedding
        let mut x = self.patch_embedding.forward(input)?;

        // Add class token if used
        if let Some(ref class_token) = self.class_token {
            let class_token_vec = class_token.get_token();
            x = self.prepend_token(&x, &class_token_vec)?;
        }

        // Add distillation token if used
        if let Some(ref dist_token) = self.distillation_token {
            let dist_token_vec = dist_token.get_token();
            x = self.prepend_token(&x, &dist_token_vec)?;
        }

        // Add positional embedding
        x = self.position_embedding.add_positional_encoding(&x)?;

        // Apply transformer blocks
        for block in &self.transformer_blocks {
            x = block.forward(&x)?;
        }

        // Apply classification head
        let output = self.head.forward(&x)?;

        Ok(output)
    }

    /// Prepend a token to the sequence
    fn prepend_token(&self, sequence: &[f32], token: &[f32]) -> Result<Vec<f32>> {
        let mut result = Vec::with_capacity(sequence.len() + token.len());
        result.extend_from_slice(token);
        result.extend_from_slice(sequence);
        Ok(result)
    }
}

impl OcrModel for ViTModel {
    fn model_type(&self) -> ModelType {
        ModelType::VisionTransformer
    }

    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 ViT model
        // 3. Postprocess the output

        let result = RecognitionResult {
            text: "ViT OCR Result".to_string(),
            confidence: 0.92,
            bounding_boxes: vec![],
            character_results: vec![],
            word_results: vec![],
            line_results: vec![],
            language: Some("en".to_string()),
            model_type: ModelType::VisionTransformer,
            processing_time_ms: 200,
        };

        Ok(result)
    }
}

impl PatchEmbedding {
    fn new(patch_size: (u32, u32), hidden_size: usize, channels: usize) -> Self {
        let patch_dim = (patch_size.0 * patch_size.1 * channels as u32) as usize;
        Self {
            patch_size,
            hidden_size,
            projection: LinearLayer::new(patch_dim, hidden_size),
            norm: LayerNorm::new(hidden_size),
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Apply linear projection
        let mut output = self.projection.forward(input)?;

        // Apply layer normalization
        output = self.norm.normalize(&output)?;

        Ok(output)
    }
}

impl PositionEmbedding {
    fn new(num_patches: usize, hidden_size: usize, learned: bool) -> Self {
        let mut embeddings = vec![vec![0.0; hidden_size]; num_patches];

        if learned {
            // Initialize with random values (in practice, these would be learned)
            for i in 0..num_patches {
                for j in 0..hidden_size {
                    embeddings[i][j] = fastrand::f32() - 0.5;
                }
            }
        } else {
            // Use sinusoidal positional encoding
            for pos in 0..num_patches {
                for i in 0..hidden_size {
                    if i % 2 == 0 {
                        embeddings[pos][i] = (pos as f32
                            / 10000.0_f32.powf(2.0 * (i / 2) as f32 / hidden_size as f32))
                        .sin();
                    } else {
                        embeddings[pos][i] = (pos as f32
                            / 10000.0_f32.powf(2.0 * (i / 2) as f32 / hidden_size as f32))
                        .cos();
                    }
                }
            }
        }

        Self {
            num_patches,
            hidden_size,
            embeddings,
            dropout: Dropout::new(0.1),
        }
    }

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

        // Add positional embeddings
        for i in 0..input.len().min(self.embeddings.len() * self.hidden_size) {
            let patch_idx = i / self.hidden_size;
            let feature_idx = i % self.hidden_size;

            if patch_idx < self.embeddings.len() && feature_idx < self.embeddings[patch_idx].len() {
                output[i] += self.embeddings[patch_idx][feature_idx];
            }
        }

        // Apply dropout
        output = self.dropout.apply(&output)?;

        Ok(output)
    }
}

impl TransformerBlock {
    fn new(
        hidden_size: usize,
        num_attention_heads: usize,
        mlp_ratio: f32,
        dropout_rate: f32,
        attention_dropout_rate: f32,
        layer_norm_eps: f32,
    ) -> Self {
        let mlp_hidden_size = (hidden_size as f32 * mlp_ratio) as usize;

        Self {
            attention: MultiHeadSelfAttention::new(
                hidden_size,
                num_attention_heads,
                attention_dropout_rate,
            ),
            mlp: MLP::new(hidden_size, mlp_hidden_size, dropout_rate),
            norm1: LayerNorm::new_with_eps(hidden_size, layer_norm_eps),
            norm2: LayerNorm::new_with_eps(hidden_size, layer_norm_eps),
            dropout: Dropout::new(dropout_rate),
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Self-attention with residual connection
        let attn_output = self.attention.forward(input)?;
        let mut output = self.add_residual(input, &attn_output)?;
        output = self.norm1.normalize(&output)?;

        // MLP with residual connection
        let mlp_output = self.mlp.forward(&output)?;
        output = self.add_residual(&output, &mlp_output)?;
        output = self.norm2.normalize(&output)?;

        Ok(output)
    }

    fn add_residual(&self, input: &[f32], residual: &[f32]) -> Result<Vec<f32>> {
        let mut output = Vec::with_capacity(input.len());
        for i in 0..input.len() {
            if i < residual.len() {
                output.push(input[i] + residual[i]);
            } else {
                output.push(input[i]);
            }
        }
        Ok(output)
    }
}

impl MultiHeadSelfAttention {
    fn new(hidden_size: usize, num_heads: usize, dropout_rate: f32) -> Self {
        let head_dim = hidden_size / num_heads;
        let scale = 1.0 / (head_dim as f32).sqrt();

        Self {
            num_heads,
            head_dim,
            scale,
            query: LinearLayer::new(hidden_size, hidden_size),
            key: LinearLayer::new(hidden_size, hidden_size),
            value: LinearLayer::new(hidden_size, hidden_size),
            output: LinearLayer::new(hidden_size, hidden_size),
            dropout: Dropout::new(dropout_rate),
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Compute Q, K, V
        let q = self.query.forward(input)?;
        let k = self.key.forward(input)?;
        let v = self.value.forward(input)?;

        // Reshape for multi-head attention
        let seq_len = input.len() / self.head_dim;
        let q_reshaped = self.reshape_for_heads(&q, seq_len)?;
        let k_reshaped = self.reshape_for_heads(&k, seq_len)?;
        let v_reshaped = self.reshape_for_heads(&v, seq_len)?;

        // Compute attention scores
        let attention_scores = self.compute_attention_scores(&q_reshaped, &k_reshaped)?;
        let attention_weights = self.apply_softmax(&attention_scores)?;
        let attention_weights = self.dropout.apply(&attention_weights)?;

        // Apply attention to values
        let attended_values = self.apply_attention(&attention_weights, &v_reshaped)?;

        // Reshape back and apply output projection
        let output_reshaped = self.reshape_from_heads(&attended_values, seq_len)?;
        self.output.forward(&output_reshaped)
    }

    fn reshape_for_heads(&self, input: &[f32], seq_len: usize) -> Result<Vec<f32>> {
        // Simplified reshaping - in practice, this would be more complex
        Ok(input.to_vec())
    }

    fn reshape_from_heads(&self, input: &[f32], seq_len: usize) -> Result<Vec<f32>> {
        // Simplified reshaping - in practice, this would be more complex
        Ok(input.to_vec())
    }

    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()] * self.scale;
        }
        Ok(scores)
    }

    fn apply_softmax(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Simplified softmax implementation
        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();

        let mut output = Vec::with_capacity(input.len());
        for &x in input {
            output.push((x - max_val).exp() / exp_sum);
        }

        Ok(output)
    }

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

impl MLP {
    fn new(input_size: usize, hidden_size: usize, dropout_rate: f32) -> Self {
        Self {
            fc1: LinearLayer::new(input_size, hidden_size),
            fc2: LinearLayer::new(hidden_size, input_size),
            activation: Activation::GELU,
            dropout: Dropout::new(dropout_rate),
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mut output = self.fc1.forward(input)?;
        output = self.apply_activation(&output)?;
        output = self.dropout.apply(&output)?;
        output = self.fc2.forward(&output)?;
        Ok(output)
    }

    fn apply_activation(&self, input: &[f32]) -> Result<Vec<f32>> {
        match self.activation {
            Activation::GELU => Ok(input
                .iter()
                .map(|&x| 0.5 * x * (1.0 + (x * 0.79788456).tanh()))
                .collect()),
            Activation::ReLU => Ok(input.iter().map(|&x| x.max(0.0)).collect()),
            Activation::Swish => Ok(input
                .iter()
                .map(|&x| x * (1.0 + (-x).exp()).recip())
                .collect()),
        }
    }
}

impl ClassToken {
    fn new(hidden_size: usize) -> Self {
        Self {
            token: vec![0.0; hidden_size],
            hidden_size,
        }
    }

    fn get_token(&self) -> Vec<f32> {
        self.token.clone()
    }
}

impl DistillationToken {
    fn new(hidden_size: usize) -> Self {
        Self {
            token: vec![0.0; hidden_size],
            hidden_size,
        }
    }

    fn get_token(&self) -> Vec<f32> {
        self.token.clone()
    }
}

impl ClassificationHead {
    fn new(input_size: usize, num_classes: usize, dropout_rate: f32) -> Self {
        Self {
            pre_logits: Some(LinearLayer::new(input_size, input_size)),
            head: LinearLayer::new(input_size, num_classes),
            dropout: Dropout::new(dropout_rate),
        }
    }

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

        // Apply pre-logits layer if present
        if let Some(ref pre_logits) = self.pre_logits {
            output = pre_logits.forward(&output)?;
        }

        // Apply dropout
        output = self.dropout.apply(&output)?;

        // Apply final classification head
        self.head.forward(&output)
    }
}

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 LayerNorm {
    fn new(size: usize) -> Self {
        Self::new_with_eps(size, 1e-5)
    }

    fn new_with_eps(size: usize, eps: f32) -> Self {
        Self {
            weight: vec![1.0; size],
            bias: vec![0.0; size],
            eps,
        }
    }

    fn normalize(&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 Dropout {
    fn new(rate: f32) -> Self {
        Self {
            rate,
            training: true,
        }
    }

    fn apply(&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 ViT models
pub struct ViTModelBuilder {
    config: Option<ViTConfig>,
}

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

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

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

        Ok(ViTModel::new(config))
    }
}

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