ghostflow-ml 1.0.0

//! Multi-Modal Architectures - CLIP, ALIGN, ViLT, etc.

use ghostflow_core::Tensor;
use crate::deep::layers::{Conv2d, Dense, BatchNorm2d};
use crate::deep::activations::ReLU;

/// CLIP (Contrastive Language-Image Pre-training)
pub struct CLIP {
    image_encoder: CLIPImageEncoder,
    text_encoder: CLIPTextEncoder,
    temperature: f32,
}

struct CLIPImageEncoder {
    conv_layers: Vec<Conv2d>,
    bn_layers: Vec<BatchNorm2d>,
    projection: Dense,
}

impl CLIPImageEncoder {
    fn new(embed_dim: usize) -> Self {
        CLIPImageEncoder {
            conv_layers: vec![
                Conv2d::new(3, 64, (7, 7)).stride((2, 2)).padding((3, 3)),
                Conv2d::new(64, 128, (3, 3)).stride((2, 2)).padding((1, 1)),
                Conv2d::new(128, 256, (3, 3)).stride((2, 2)).padding((1, 1)),
            ],
            bn_layers: vec![
                BatchNorm2d::new(64),
                BatchNorm2d::new(128),
                BatchNorm2d::new(256),
            ],
            projection: Dense::new(256, embed_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for (conv, bn) in self.conv_layers.iter_mut().zip(self.bn_layers.iter_mut()) {
            out = conv.forward(&out, training);
            out = bn.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        // Global average pooling
        out = self.global_avg_pool(&out);
        
        // Project to embedding space
        self.projection.forward(&out, training)
    }

    fn global_avg_pool(&self, x: &Tensor) -> Tensor {
        let dims = x.dims();
        let batch = dims[0];
        let channels = dims[1];
        let height = dims[2];
        let width = dims[3];
        let data = x.data_f32();
        
        let mut result = vec![0.0f32; batch * channels];
        
        for b in 0..batch {
            for c in 0..channels {
                let mut sum = 0.0f32;
                for h in 0..height {
                    for w in 0..width {
                        let idx = ((b * channels + c) * height + h) * width + w;
                        sum += data[idx];
                    }
                }
                result[b * channels + c] = sum / (height * width) as f32;
            }
        }
        
        Tensor::from_slice(&result, &[batch, channels]).unwrap()
    }
}

struct CLIPTextEncoder {
    embedding: Dense,
    transformer_layers: Vec<TransformerLayer>,
    projection: Dense,
}

struct TransformerLayer {
    attention: Dense,
    ffn: Vec<Dense>,
}

impl TransformerLayer {
    fn new(d_model: usize) -> Self {
        TransformerLayer {
            attention: Dense::new(d_model, d_model),
            ffn: vec![
                Dense::new(d_model, d_model * 4),
                Dense::new(d_model * 4, d_model),
            ],
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let attn_out = self.attention.forward(x, training);
        
        let mut ffn_out = attn_out;
        for (i, layer) in self.ffn.iter_mut().enumerate() {
            ffn_out = layer.forward(&ffn_out, training);
            if i == 0 {
                ffn_out = ReLU::new().forward(&ffn_out);
            }
        }
        
        ffn_out
    }
}

impl CLIPTextEncoder {
    fn new(vocab_size: usize, d_model: usize, embed_dim: usize, num_layers: usize) -> Self {
        CLIPTextEncoder {
            embedding: Dense::new(vocab_size, d_model),
            transformer_layers: (0..num_layers).map(|_| TransformerLayer::new(d_model)).collect(),
            projection: Dense::new(d_model, embed_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.embedding.forward(x, training);
        
        for layer in &mut self.transformer_layers {
            out = layer.forward(&out, training);
        }
        
        self.projection.forward(&out, training)
    }
}

impl CLIP {
    pub fn new(vocab_size: usize, embed_dim: usize) -> Self {
        CLIP {
            image_encoder: CLIPImageEncoder::new(embed_dim),
            text_encoder: CLIPTextEncoder::new(vocab_size, 512, embed_dim, 12),
            temperature: 0.07,
        }
    }

    pub fn forward(&mut self, images: &Tensor, texts: &Tensor, training: bool) -> (Tensor, Tensor) {
        let image_features = self.image_encoder.forward(images, training);
        let text_features = self.text_encoder.forward(texts, training);
        
        // Normalize features
        let image_features_norm = self.l2_normalize(&image_features);
        let text_features_norm = self.l2_normalize(&text_features);
        
        (image_features_norm, text_features_norm)
    }

    fn l2_normalize(&self, x: &Tensor) -> Tensor {
        let data = x.data_f32();
        let batch_size = x.dims()[0];
        let dim = x.dims()[1];
        
        let mut result = Vec::new();
        
        for b in 0..batch_size {
            let offset = b * dim;
            
            // Compute L2 norm
            let mut norm = 0.0f32;
            for i in 0..dim {
                norm += data[offset + i].powi(2);
            }
            norm = norm.sqrt().max(1e-8);
            
            // Normalize
            for i in 0..dim {
                result.push(data[offset + i] / norm);
            }
        }
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }
}

/// ALIGN (A Large-scale ImaGe and Noisy-text embedding)
pub struct ALIGN {
    image_encoder: EfficientNetEncoder,
    text_encoder: BERTEncoder,
    temperature: f32,
}

struct EfficientNetEncoder {
    blocks: Vec<MBConvBlock>,
    projection: Dense,
}

struct MBConvBlock {
    expand: Conv2d,
    depthwise: Conv2d,
    project: Conv2d,
}

impl MBConvBlock {
    fn new(in_channels: usize, out_channels: usize) -> Self {
        MBConvBlock {
            expand: Conv2d::new(in_channels, in_channels * 6, (1, 1)),
            depthwise: Conv2d::new(in_channels * 6, in_channels * 6, (3, 3)).padding((1, 1)),
            project: Conv2d::new(in_channels * 6, out_channels, (1, 1)),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.expand.forward(x, training);
        out = ReLU::new().forward(&out);
        
        out = self.depthwise.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        self.project.forward(&out, training)
    }
}

impl EfficientNetEncoder {
    fn new(embed_dim: usize) -> Self {
        EfficientNetEncoder {
            blocks: vec![
                MBConvBlock::new(3, 32),
                MBConvBlock::new(32, 64),
                MBConvBlock::new(64, 128),
            ],
            projection: Dense::new(128, embed_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for block in &mut self.blocks {
            out = block.forward(&out, training);
        }
        
        self.projection.forward(&out, training)
    }
}

struct BERTEncoder {
    embedding: Dense,
    layers: Vec<TransformerLayer>,
    projection: Dense,
}

impl BERTEncoder {
    fn new(vocab_size: usize, d_model: usize, embed_dim: usize) -> Self {
        BERTEncoder {
            embedding: Dense::new(vocab_size, d_model),
            layers: (0..12).map(|_| TransformerLayer::new(d_model)).collect(),
            projection: Dense::new(d_model, embed_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.embedding.forward(x, training);
        
        for layer in &mut self.layers {
            out = layer.forward(&out, training);
        }
        
        self.projection.forward(&out, training)
    }
}

impl ALIGN {
    pub fn new(vocab_size: usize, embed_dim: usize) -> Self {
        ALIGN {
            image_encoder: EfficientNetEncoder::new(embed_dim),
            text_encoder: BERTEncoder::new(vocab_size, 768, embed_dim),
            temperature: 0.07,
        }
    }

    pub fn forward(&mut self, images: &Tensor, texts: &Tensor, training: bool) -> (Tensor, Tensor) {
        let image_features = self.image_encoder.forward(images, training);
        let text_features = self.text_encoder.forward(texts, training);
        
        (image_features, text_features)
    }
}

/// ViLT (Vision-and-Language Transformer)
pub struct ViLT {
    patch_embedding: PatchEmbedding,
    text_embedding: Dense,
    transformer: Vec<TransformerLayer>,
    mlm_head: Dense,
    itm_head: Dense,
}

struct PatchEmbedding {
    projection: Conv2d,
}

impl PatchEmbedding {
    fn new(patch_size: usize, embed_dim: usize) -> Self {
        PatchEmbedding {
            projection: Conv2d::new(3, embed_dim, (patch_size, patch_size)).stride((patch_size, patch_size)),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        self.projection.forward(x, training)
    }
}

impl ViLT {
    pub fn new(vocab_size: usize, patch_size: usize, embed_dim: usize) -> Self {
        ViLT {
            patch_embedding: PatchEmbedding::new(patch_size, embed_dim),
            text_embedding: Dense::new(vocab_size, embed_dim),
            transformer: (0..12).map(|_| TransformerLayer::new(embed_dim)).collect(),
            mlm_head: Dense::new(embed_dim, vocab_size),
            itm_head: Dense::new(embed_dim, 2),
        }
    }

    pub fn forward(&mut self, images: &Tensor, texts: &Tensor, training: bool) -> (Tensor, Tensor) {
        let image_embeds = self.patch_embedding.forward(images, training);
        let text_embeds = self.text_embedding.forward(texts, training);
        
        // Concatenate image and text embeddings
        let combined = self.concatenate(&image_embeds, &text_embeds);
        
        let mut out = combined;
        for layer in &mut self.transformer {
            out = layer.forward(&out, training);
        }
        
        let mlm_logits = self.mlm_head.forward(&out, training);
        let itm_logits = self.itm_head.forward(&out, training);
        
        (mlm_logits, itm_logits)
    }

    fn concatenate(&self, a: &Tensor, b: &Tensor) -> Tensor {
        a.clone() // Simplified
    }
}

/// BLIP (Bootstrapping Language-Image Pre-training)
pub struct BLIP {
    vision_encoder: VisionEncoder,
    text_encoder: TextEncoder,
    text_decoder: TextDecoder,
}

struct VisionEncoder {
    conv_layers: Vec<Conv2d>,
    projection: Dense,
}

impl VisionEncoder {
    fn new(embed_dim: usize) -> Self {
        VisionEncoder {
            conv_layers: vec![
                Conv2d::new(3, 64, (3, 3)).padding((1, 1)),
                Conv2d::new(64, 128, (3, 3)).padding((1, 1)),
            ],
            projection: Dense::new(128, embed_dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for conv in &mut self.conv_layers {
            out = conv.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        self.projection.forward(&out, training)
    }
}

struct TextEncoder {
    embedding: Dense,
    layers: Vec<TransformerLayer>,
}

impl TextEncoder {
    fn new(vocab_size: usize, d_model: usize) -> Self {
        TextEncoder {
            embedding: Dense::new(vocab_size, d_model),
            layers: (0..6).map(|_| TransformerLayer::new(d_model)).collect(),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.embedding.forward(x, training);
        
        for layer in &mut self.layers {
            out = layer.forward(&out, training);
        }
        
        out
    }
}

struct TextDecoder {
    embedding: Dense,
    layers: Vec<TransformerLayer>,
    lm_head: Dense,
}

impl TextDecoder {
    fn new(vocab_size: usize, d_model: usize) -> Self {
        TextDecoder {
            embedding: Dense::new(vocab_size, d_model),
            layers: (0..6).map(|_| TransformerLayer::new(d_model)).collect(),
            lm_head: Dense::new(d_model, vocab_size),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.embedding.forward(x, training);
        
        for layer in &mut self.layers {
            out = layer.forward(&out, training);
        }
        
        self.lm_head.forward(&out, training)
    }
}

impl BLIP {
    pub fn new(vocab_size: usize, embed_dim: usize) -> Self {
        BLIP {
            vision_encoder: VisionEncoder::new(embed_dim),
            text_encoder: TextEncoder::new(vocab_size, embed_dim),
            text_decoder: TextDecoder::new(vocab_size, embed_dim),
        }
    }

    pub fn forward(&mut self, images: &Tensor, texts: &Tensor, training: bool) -> (Tensor, Tensor) {
        let image_features = self.vision_encoder.forward(images, training);
        let text_features = self.text_encoder.forward(texts, training);
        let decoded_text = self.text_decoder.forward(texts, training);
        
        (text_features, decoded_text)
    }
}

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

    #[test]
    fn test_clip() {
        let mut clip = CLIP::new(50000, 512);
        let images = Tensor::from_slice(&vec![0.5f32; 2 * 3 * 224 * 224], &[2, 3, 224, 224]).unwrap();
        let texts = Tensor::from_slice(&vec![1.0f32; 2 * 77], &[2, 77]).unwrap();
        let (img_feat, txt_feat) = clip.forward(&images, &texts, false);
        assert_eq!(img_feat.dims()[1], 512);
        assert_eq!(txt_feat.dims()[1], 512);
    }

    #[test]
    fn test_vilt() {
        let mut vilt = ViLT::new(30000, 16, 768);
        let images = Tensor::from_slice(&vec![0.5f32; 1 * 3 * 224 * 224], &[1, 3, 224, 224]).unwrap();
        let texts = Tensor::from_slice(&vec![1.0f32; 1 * 40], &[1, 40]).unwrap();
        let (mlm, itm) = vilt.forward(&images, &texts, false);
        assert_eq!(mlm.dims()[0], 1);
    }
}