//! Transformer-based OCR model implementation
//!
//! This module provides a TrOCR (Transformer-based OCR) model implementation
//! that can handle both text detection and recognition using transformer architectures.

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 Transformer OCR models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TransformerConfig {
    /// Model architecture type
    pub architecture: TransformerArchitecture,
    /// 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),
    /// Maximum sequence length for text generation
    pub max_sequence_length: Option<usize>,
    /// Confidence threshold for predictions
    pub confidence_threshold: f32,
    /// Device to run inference on
    pub device: DeviceType,
    /// Quantization type
    pub quantization: Option<QuantizationType>,
    /// Number of attention heads
    pub num_attention_heads: usize,
    /// Number of transformer layers
    pub num_layers: usize,
    /// Hidden dimension size
    pub hidden_size: usize,
    /// Vocabulary size
    pub vocab_size: usize,
    /// Whether to use beam search for decoding
    pub use_beam_search: bool,
    /// Beam search width
    pub beam_width: usize,
    /// Temperature for sampling
    pub temperature: f32,
    /// Whether to use greedy decoding
    pub use_greedy: bool,
}

/// Transformer architecture variants
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum TransformerArchitecture {
    /// TrOCR (Transformer-based OCR)
    TrOCR,
    /// Vision Transformer (ViT) for OCR
    VisionTransformer,
    /// Custom transformer architecture
    Custom(String),
    /// Multi-modal transformer (image + text)
    MultiModal,
    /// Encoder-decoder transformer
    EncoderDecoder,
}

/// Transformer OCR model implementation
pub struct TransformerModel {
    config: TransformerConfig,
    model_loaded: bool,
    tokenizer: Option<TransformerTokenizer>,
    encoder: Option<TransformerEncoder>,
    decoder: Option<TransformerDecoder>,
}

/// Tokenizer for transformer models
pub struct TransformerTokenizer {
    vocab: std::collections::HashMap<String, u32>,
    special_tokens: SpecialTokens,
    max_length: usize,
}

/// Special tokens for the tokenizer
#[derive(Debug, Clone)]
pub struct SpecialTokens {
    pub pad_token: String,
    pub start_token: String,
    pub end_token: String,
    pub unknown_token: String,
    pub mask_token: String,
}

/// Transformer encoder component
pub struct TransformerEncoder {
    layers: Vec<TransformerLayer>,
    embedding: EmbeddingLayer,
    position_encoding: PositionalEncoding,
}

/// Transformer decoder component
pub struct TransformerDecoder {
    layers: Vec<TransformerLayer>,
    embedding: EmbeddingLayer,
    position_encoding: PositionalEncoding,
    output_projection: OutputProjection,
}

/// Individual transformer layer
pub struct TransformerLayer {
    self_attention: MultiHeadAttention,
    cross_attention: Option<MultiHeadAttention>,
    feed_forward: FeedForwardNetwork,
    layer_norm1: LayerNorm,
    layer_norm2: LayerNorm,
    layer_norm3: Option<LayerNorm>,
}

/// Multi-head attention mechanism
pub struct MultiHeadAttention {
    num_heads: usize,
    head_dim: usize,
    query_projection: LinearLayer,
    key_projection: LinearLayer,
    value_projection: LinearLayer,
    output_projection: LinearLayer,
    dropout: Dropout,
}

/// Feed-forward network
pub struct FeedForwardNetwork {
    linear1: LinearLayer,
    linear2: LinearLayer,
    activation: Activation,
    dropout: Dropout,
}

/// Linear layer
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,
}

/// Embedding layer
pub struct EmbeddingLayer {
    embeddings: Vec<Vec<f32>>,
    vocab_size: usize,
    hidden_size: usize,
}

/// Positional encoding
pub struct PositionalEncoding {
    encoding: Vec<Vec<f32>>,
    max_length: usize,
    hidden_size: usize,
}

/// Output projection layer
pub struct OutputProjection {
    weight: Vec<Vec<f32>>,
    bias: Option<Vec<f32>>,
}

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

impl TransformerModel {
    /// Create a new Transformer model
    pub fn new(config: TransformerConfig) -> Self {
        Self {
            config,
            model_loaded: false,
            tokenizer: None,
            encoder: None,
            decoder: None,
        }
    }

    /// 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());
        }

        // Initialize tokenizer
        self.tokenizer = Some(TransformerTokenizer::new(
            self.config.vocab_size,
            self.config.max_sequence_length.unwrap_or(512),
        ));

        // Initialize encoder
        self.encoder = Some(TransformerEncoder::new(
            self.config.num_layers,
            self.config.hidden_size,
            self.config.num_attention_heads,
            self.config.input_shape,
        ));

        // Initialize decoder
        self.decoder = Some(TransformerDecoder::new(
            self.config.num_layers,
            self.config.hidden_size,
            self.config.num_attention_heads,
            self.config.vocab_size,
        ));

        self.model_loaded = true;
        Ok(())
    }

    /// Get the model configuration
    pub fn config(&self) -> &TransformerConfig {
        &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) -> &TransformerArchitecture {
        &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 transformer 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;

        // For now, return a placeholder - this would be implemented with actual image processing
        let input_size = (height * width * channels) as usize;
        Ok(vec![0.0; input_size])
    }

    /// Postprocess model output to text
    fn postprocess_output(&self, logits: &[f32]) -> Result<String> {
        if let Some(tokenizer) = &self.tokenizer {
            tokenizer.decode(logits)
        } else {
            Err(OcrError::ModelNotFound("Tokenizer not loaded".to_string()).into())
        }
    }
}

impl OcrModel for TransformerModel {
    fn model_type(&self) -> ModelType {
        ModelType::Transformer
    }

    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 {
        // Return a placeholder config - in practice this would be properly implemented
        // Note: This is a temporary implementation that creates a new config each time
        // In a real implementation, this would store the config as a field
        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 transformer encoder
        // 3. Generate text using the decoder
        // 4. Postprocess the output

        let result = RecognitionResult {
            text: "Transformer OCR Result".to_string(),
            confidence: 0.95,
            bounding_boxes: vec![],
            character_results: vec![],
            word_results: vec![],
            line_results: vec![],
            language: Some("en".to_string()),
            model_type: ModelType::Transformer,
            processing_time_ms: 150,
        };

        Ok(result)
    }
}

impl TransformerTokenizer {
    /// Create a new tokenizer
    pub fn new(vocab_size: usize, max_length: usize) -> Self {
        let mut vocab = std::collections::HashMap::new();

        // Add basic vocabulary
        for i in 0..vocab_size {
            vocab.insert(format!("token_{}", i), i as u32);
        }

        let special_tokens = SpecialTokens {
            pad_token: "<pad>".to_string(),
            start_token: "<s>".to_string(),
            end_token: "</s>".to_string(),
            unknown_token: "<unk>".to_string(),
            mask_token: "<mask>".to_string(),
        };

        Self {
            vocab,
            special_tokens,
            max_length,
        }
    }

    /// Tokenize text
    pub fn encode(&self, text: &str) -> Result<Vec<u32>> {
        let mut tokens = Vec::new();

        // Add start token
        tokens.push(0); // Assuming 0 is start token ID

        // Simple word-level tokenization
        for word in text.split_whitespace() {
            if let Some(&token_id) = self.vocab.get(word) {
                tokens.push(token_id);
            } else {
                tokens.push(1); // Unknown token ID
            }
        }

        // Add end token
        tokens.push(2); // Assuming 2 is end token ID

        // Pad or truncate to max_length
        if tokens.len() > self.max_length {
            tokens.truncate(self.max_length);
        } else {
            while tokens.len() < self.max_length {
                tokens.push(0); // Pad token ID
            }
        }

        Ok(tokens)
    }

    /// Decode tokens to text
    pub fn decode(&self, tokens: &[f32]) -> Result<String> {
        // Convert logits to token IDs (simplified)
        let token_ids: Vec<u32> = tokens
            .chunks(self.vocab.len())
            .map(|chunk| {
                chunk
                    .iter()
                    .enumerate()
                    .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
                    .map(|(i, _)| i as u32)
                    .unwrap_or(0)
            })
            .collect();

        // Convert token IDs to text
        let mut text = String::new();
        for &token_id in &token_ids {
            if token_id == 0 || token_id == 2 {
                // Skip pad and end tokens
                continue;
            }

            if let Some((token, _)) = self.vocab.iter().find(|&(_, &id)| id == token_id) {
                if !text.is_empty() {
                    text.push(' ');
                }
                text.push_str(token);
            }
        }

        Ok(text)
    }
}

impl TransformerEncoder {
    /// Create a new encoder
    pub fn new(
        num_layers: usize,
        hidden_size: usize,
        num_attention_heads: usize,
        input_shape: (u32, u32, u32),
    ) -> Self {
        let layers = (0..num_layers)
            .map(|_| TransformerLayer::new(hidden_size, num_attention_heads))
            .collect();

        let embedding = EmbeddingLayer::new(input_shape, hidden_size);
        let position_encoding = PositionalEncoding::new(512, hidden_size);

        Self {
            layers,
            embedding,
            position_encoding,
        }
    }

    /// Encode input features
    pub fn encode(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Apply embedding
        let mut features = self.embedding.forward(input)?;

        // Apply positional encoding
        features = self.position_encoding.add_positional_encoding(&features)?;

        // Apply transformer layers
        for layer in &self.layers {
            features = layer.forward(&features, None)?;
        }

        Ok(features)
    }
}

impl TransformerDecoder {
    /// Create a new decoder
    pub fn new(
        num_layers: usize,
        hidden_size: usize,
        num_attention_heads: usize,
        vocab_size: usize,
    ) -> Self {
        let layers = (0..num_layers)
            .map(|_| TransformerLayer::new(hidden_size, num_attention_heads))
            .collect();

        let embedding = EmbeddingLayer::new((1, 1, hidden_size as u32), hidden_size);
        let position_encoding = PositionalEncoding::new(512, hidden_size);
        let output_projection = OutputProjection::new(hidden_size, vocab_size);

        Self {
            layers,
            embedding,
            position_encoding,
            output_projection,
        }
    }

    /// Decode features to text
    pub fn decode(&self, encoder_output: &[f32], max_length: usize) -> Result<Vec<f32>> {
        let mut output = Vec::new();
        let mut hidden_state = vec![0.0; self.embedding.hidden_size];

        for _ in 0..max_length {
            // Apply embedding and positional encoding
            let embedded = self.embedding.forward(&hidden_state)?;
            let positioned = self.position_encoding.add_positional_encoding(&embedded)?;

            // Apply transformer layers
            let mut features = positioned;
            for layer in &self.layers {
                features = layer.forward(&features, Some(encoder_output))?;
            }

            // Apply output projection
            let logits = self.output_projection.forward(&features)?;
            output.extend_from_slice(&logits);

            // Update hidden state for next iteration
            hidden_state = features;
        }

        Ok(output)
    }
}

// Implement the various layer types with placeholder implementations
impl TransformerLayer {
    fn new(hidden_size: usize, num_attention_heads: usize) -> Self {
        Self {
            self_attention: MultiHeadAttention::new(hidden_size, num_attention_heads),
            cross_attention: Some(MultiHeadAttention::new(hidden_size, num_attention_heads)),
            feed_forward: FeedForwardNetwork::new(hidden_size),
            layer_norm1: LayerNorm::new(hidden_size),
            layer_norm2: LayerNorm::new(hidden_size),
            layer_norm3: Some(LayerNorm::new(hidden_size)),
        }
    }

    fn forward(&self, input: &[f32], encoder_output: Option<&[f32]>) -> Result<Vec<f32>> {
        // Self-attention
        let mut output = self.self_attention.forward(input, input, input)?;
        output = self.layer_norm1.normalize(&output)?;

        // Cross-attention (if encoder output provided)
        if let Some(encoder_out) = encoder_output {
            let cross_output = self.cross_attention.as_ref().unwrap().forward(
                &output,
                encoder_out,
                encoder_out,
            )?;
            output = self.layer_norm2.normalize(&cross_output)?;
        }

        // Feed-forward
        let ff_output = self.feed_forward.forward(&output)?;
        output = self.layer_norm3.as_ref().unwrap().normalize(&ff_output)?;

        Ok(output)
    }
}

impl MultiHeadAttention {
    fn new(hidden_size: usize, num_heads: usize) -> Self {
        let head_dim = hidden_size / num_heads;
        Self {
            num_heads,
            head_dim,
            query_projection: LinearLayer::new(hidden_size, hidden_size),
            key_projection: LinearLayer::new(hidden_size, hidden_size),
            value_projection: LinearLayer::new(hidden_size, hidden_size),
            output_projection: LinearLayer::new(hidden_size, hidden_size),
            dropout: Dropout::new(0.1),
        }
    }

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

        // Compute attention scores (simplified)
        let attention_scores = self.compute_attention_scores(&q, &k)?;
        let attention_output = self.apply_attention(&attention_scores, &v)?;

        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 FeedForwardNetwork {
    fn new(hidden_size: usize) -> Self {
        Self {
            linear1: LinearLayer::new(hidden_size, hidden_size * 4),
            linear2: LinearLayer::new(hidden_size * 4, hidden_size),
            activation: Activation::GELU,
            dropout: Dropout::new(0.1),
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mut output = self.linear1.forward(input)?;
        output = self.apply_activation(&output)?;
        output = self.dropout.apply(&output)?;
        output = self.linear2.forward(&output)?;
        Ok(output)
    }

    fn apply_activation(&self, input: &[f32]) -> Result<Vec<f32>> {
        match self.activation {
            Activation::ReLU => Ok(input.iter().map(|&x| x.max(0.0)).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::Tanh => Ok(input.iter().map(|&x| x.tanh()).collect()),
        }
    }
}

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 {
            weight: vec![1.0; size],
            bias: vec![0.0; size],
            eps: 1e-5,
        }
    }

    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())
        }
    }
}

impl EmbeddingLayer {
    fn new(input_shape: (u32, u32, u32), hidden_size: usize) -> Self {
        let vocab_size = (input_shape.0 * input_shape.1 * input_shape.2) as usize;
        Self {
            embeddings: vec![vec![0.0; hidden_size]; vocab_size],
            vocab_size,
            hidden_size,
        }
    }

    fn forward(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Simplified embedding lookup
        let mut output = vec![0.0; self.hidden_size];
        for (i, &value) in input.iter().enumerate() {
            let idx = (value as usize) % self.embeddings.len();
            for j in 0..self.hidden_size {
                if j < self.embeddings[idx].len() {
                    output[j] += self.embeddings[idx][j];
                }
            }
        }
        Ok(output)
    }
}

impl PositionalEncoding {
    fn new(max_length: usize, hidden_size: usize) -> Self {
        let mut encoding = vec![vec![0.0; hidden_size]; max_length];

        for pos in 0..max_length {
            for i in 0..hidden_size {
                if i % 2 == 0 {
                    encoding[pos][i] = (pos as f32
                        / 10000.0_f32.powf(2.0 * (i / 2) as f32 / hidden_size as f32))
                    .sin();
                } else {
                    encoding[pos][i] = (pos as f32
                        / 10000.0_f32.powf(2.0 * (i / 2) as f32 / hidden_size as f32))
                    .cos();
                }
            }
        }

        Self {
            encoding,
            max_length,
            hidden_size,
        }
    }

    fn add_positional_encoding(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mut output = input.to_vec();
        for i in 0..input.len().min(self.hidden_size) {
            if i < self.encoding[0].len() {
                output[i] += self.encoding[0][i]; // Simplified: use first position
            }
        }
        Ok(output)
    }
}

impl OutputProjection {
    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)
    }
}

/// Builder for Transformer models
pub struct TransformerModelBuilder {
    config: Option<TransformerConfig>,
}

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

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

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

        Ok(TransformerModel::new(config))
    }
}

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