//! Transformer-based translation implementation

use crate::{Result, translation::AttentionConfig, translation::AttentionLayer};
use rtx_tensor::{Device, Tensor};
use serde::{Deserialize, Serialize};

/// Transformer configuration for translation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TransformerConfig {
    pub num_encoder_layers: usize,
    pub num_decoder_layers: usize,
    pub hidden_size: usize,
    pub num_attention_heads: usize,
    pub intermediate_size: usize,
    pub dropout_rate: f32,
    pub max_position_embeddings: usize,
    pub vocab_size: usize,
}

impl Default for TransformerConfig {
    fn default() -> Self {
        Self {
            num_encoder_layers: 6,
            num_decoder_layers: 6,
            hidden_size: 512,
            num_attention_heads: 8,
            intermediate_size: 2048,
            dropout_rate: 0.1,
            max_position_embeddings: 512,
            vocab_size: 50000,
        }
    }
}

/// Transformer encoder layer
pub struct TransformerEncoderLayer {
    self_attention: AttentionLayer,
    feed_forward: FeedForward,
    config: TransformerConfig,
}

impl TransformerEncoderLayer {
    pub fn new(config: TransformerConfig) -> Self {
        let attention_config = AttentionConfig {
            attention_type: crate::translation::attention::AttentionType::MultiHead {
                num_heads: config.num_attention_heads,
            },
            hidden_size: config.hidden_size,
            dropout_rate: config.dropout_rate,
            temperature: 1.0,
        };

        Self {
            self_attention: AttentionLayer::new(attention_config),
            feed_forward: FeedForward::new(config.hidden_size, config.intermediate_size),
            config,
        }
    }

    pub fn forward(
        &self,
        hidden_states: &Tensor,
        attention_mask: Option<&Tensor>,
    ) -> Result<Tensor> {
        // Self-attention
        let (attention_output, _) = self.self_attention.forward(
            hidden_states,
            hidden_states,
            hidden_states,
            attention_mask,
        )?;

        // Residual connection and layer norm
        let normed_attention = self.layer_norm(&(hidden_states + &attention_output)?)?;

        // Feed-forward
        let ff_output = self.feed_forward.forward(&normed_attention)?;

        // Residual connection and layer norm
        let output = self.layer_norm(&(&normed_attention + &ff_output)?)?;

        Ok(output)
    }

    fn layer_norm(&self, input: &Tensor) -> Result<Tensor> {
        // Mock layer normalization
        Ok(input.clone())
    }
}

/// Transformer decoder layer
pub struct TransformerDecoderLayer {
    self_attention: AttentionLayer,
    cross_attention: AttentionLayer,
    feed_forward: FeedForward,
    config: TransformerConfig,
}

impl TransformerDecoderLayer {
    pub fn new(config: TransformerConfig) -> Self {
        let attention_config = AttentionConfig {
            attention_type: crate::translation::attention::AttentionType::MultiHead {
                num_heads: config.num_attention_heads,
            },
            hidden_size: config.hidden_size,
            dropout_rate: config.dropout_rate,
            temperature: 1.0,
        };

        Self {
            self_attention: AttentionLayer::new(attention_config.clone()),
            cross_attention: AttentionLayer::new(attention_config),
            feed_forward: FeedForward::new(config.hidden_size, config.intermediate_size),
            config,
        }
    }

    pub fn forward(
        &self,
        hidden_states: &Tensor,
        encoder_hidden_states: &Tensor,
        attention_mask: Option<&Tensor>,
        causal_mask: Option<&Tensor>,
    ) -> Result<(Tensor, Tensor)> {
        // Self-attention with causal mask
        let (self_attn_output, _) = self.self_attention.forward(
            hidden_states,
            hidden_states,
            hidden_states,
            causal_mask,
        )?;

        let normed_self_attn = self.layer_norm(&(hidden_states + &self_attn_output)?)?;

        // Cross-attention
        let (cross_attn_output, cross_attn_weights) = self.cross_attention.forward(
            &normed_self_attn,
            encoder_hidden_states,
            encoder_hidden_states,
            attention_mask,
        )?;

        let normed_cross_attn = self.layer_norm(&(&normed_self_attn + &cross_attn_output)?)?;

        // Feed-forward
        let ff_output = self.feed_forward.forward(&normed_cross_attn)?;
        let output = self.layer_norm(&(&normed_cross_attn + &ff_output)?)?;

        Ok((output, cross_attn_weights))
    }

    fn layer_norm(&self, input: &Tensor) -> Result<Tensor> {
        // Mock layer normalization
        Ok(input.clone())
    }
}

/// Feed-forward network
pub struct FeedForward {
    hidden_size: usize,
    intermediate_size: usize,
}

impl FeedForward {
    pub fn new(hidden_size: usize, intermediate_size: usize) -> Self {
        Self {
            hidden_size,
            intermediate_size,
        }
    }

    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        // Mock feed-forward implementation
        // Would typically be: linear -> activation -> linear
        Ok(input.clone())
    }
}

/// Complete transformer model for translation
pub struct TransformerTranslator {
    encoder_layers: Vec<TransformerEncoderLayer>,
    decoder_layers: Vec<TransformerDecoderLayer>,
    config: TransformerConfig,
}

impl TransformerTranslator {
    pub fn new(config: TransformerConfig) -> Self {
        let encoder_layers: Vec<_> = (0..config.num_encoder_layers)
            .map(|_| TransformerEncoderLayer::new(config.clone()))
            .collect();

        let decoder_layers: Vec<_> = (0..config.num_decoder_layers)
            .map(|_| TransformerDecoderLayer::new(config.clone()))
            .collect();

        Self {
            encoder_layers,
            decoder_layers,
            config,
        }
    }

    pub fn encode(&self, input_ids: &Tensor, attention_mask: Option<&Tensor>) -> Result<Tensor> {
        let mut hidden_states = self.embed_tokens(input_ids)?;

        for layer in &self.encoder_layers {
            hidden_states = layer.forward(&hidden_states, attention_mask)?;
        }

        Ok(hidden_states)
    }

    pub fn decode(
        &self,
        target_ids: &Tensor,
        encoder_hidden_states: &Tensor,
        attention_mask: Option<&Tensor>,
    ) -> Result<(Tensor, Vec<Tensor>)> {
        let mut hidden_states = self.embed_tokens(target_ids)?;
        let causal_mask = self.create_causal_mask(target_ids.shape()[1])?;
        let mut cross_attention_weights = Vec::new();

        for layer in &self.decoder_layers {
            let (new_hidden_states, cross_attn_weights) = layer.forward(
                &hidden_states,
                encoder_hidden_states,
                attention_mask,
                Some(&causal_mask),
            )?;
            hidden_states = new_hidden_states;
            cross_attention_weights.push(cross_attn_weights);
        }

        Ok((hidden_states, cross_attention_weights))
    }

    fn embed_tokens(&self, input_ids: &Tensor) -> Result<Tensor> {
        // Mock token embedding + positional encoding
        let batch_size = input_ids.shape()[0];
        let seq_length = input_ids.shape()[1];
        let embeddings = Tensor::randn(
            &[batch_size, seq_length, self.config.hidden_size],
            input_ids.device(),
        )?;
        Ok(embeddings)
    }

    fn create_causal_mask(&self, seq_length: usize) -> Result<Tensor> {
        // Create lower triangular mask for causal attention
        let mut mask_data = vec![0.0f32; seq_length * seq_length];
        for i in 0..seq_length {
            for j in 0..=i {
                mask_data[i * seq_length + j] = 1.0;
            }
        }

        let mask = Tensor::from_data(mask_data, [seq_length, seq_length], &Device::default())?;
        Ok(mask)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use rtx_tensor::Device;

    #[test]
    fn test_transformer_config() {
        let config = TransformerConfig::default();
        assert_eq!(config.num_encoder_layers, 6);
        assert_eq!(config.num_decoder_layers, 6);
        assert_eq!(config.hidden_size, 512);
    }

    #[test]
    fn test_feed_forward() -> Result<()> {
        let ff = FeedForward::new(512, 2048);
        let device = Device::cuda(0).unwrap_or(Device::default());
        let input = Tensor::randn(&[1, 10, 512], &device)?;
        let output = ff.forward(&input)?;
        assert_eq!(output.shape(), input.shape());
        Ok(())
    }

    #[test]
    fn test_transformer_translator() -> Result<()> {
        let config = TransformerConfig {
            hidden_size: 64,
            num_attention_heads: 4,
            ..Default::default()
        };

        let translator = TransformerTranslator::new(config);
        let input_ids = Tensor::from_data(
            vec![1.0f32, 2.0, 3.0, 4.0, 5.0],
            &[1, 5],
            &Device::default(),
        )?;

        let encoder_output = translator.encode(&input_ids, None)?;
        assert_eq!(encoder_output.shape(), &[1, 5, 64]);

        let target_ids = Tensor::from_data(vec![1.0f32, 2.0, 3.0], &[1, 3], &Device::default())?;
        let (decoder_output, attention_weights) =
            translator.decode(&target_ids, &encoder_output, None)?;

        assert_eq!(decoder_output.shape(), &[1, 3, 64]);
        assert!(!attention_weights.is_empty());

        Ok(())
    }
}