torsh-vision 0.1.2

use std::collections::HashMap;
use torsh_core::{device::DeviceType, error::Result};
use torsh_nn::prelude::*;
use torsh_nn::{Module, Parameter};
use torsh_tensor::{creation, Tensor};

use crate::{ModelConfig, VisionModel};

/// Multi-Head Self-Attention
pub struct MultiHeadAttention {
    num_heads: usize,
    head_dim: usize,
    scale: f32,
    qkv: Linear,
    proj: Linear,
    dropout: Dropout,
}

impl MultiHeadAttention {
    pub fn new(embed_dim: usize, num_heads: usize, dropout: f32) -> Self {
        let head_dim = embed_dim / num_heads;
        let scale = 1.0 / (head_dim as f32).sqrt();

        Self {
            num_heads,
            head_dim,
            scale,
            qkv: Linear::new(embed_dim, embed_dim * 3, true),
            proj: Linear::new(embed_dim, embed_dim, true),
            dropout: Dropout::new(dropout),
        }
    }
}

impl Module for MultiHeadAttention {
    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let batch_size = input.shape().dims()[0];
        let seq_len = input.shape().dims()[1];
        let _embed_dim = input.shape().dims()[2];

        // Generate Q, K, V
        let qkv = self.qkv.forward(input)?;
        let qkv = qkv.view(&[
            batch_size as i32,
            seq_len as i32,
            3,
            self.num_heads as i32,
            self.head_dim as i32,
        ])?;
        let _qkv = qkv.permute(&[2, 0, 3, 1, 4])?; // [3, batch, num_heads, seq_len, head_dim]

        // Split into Q, K, V - simplified implementation
        // In a real implementation, we'd properly split the tensor
        let _q = input.clone(); // Placeholder
        let _k = input.clone(); // Placeholder
        let v = input.clone(); // Placeholder

        // Simplified attention - just pass through for now
        // In a full implementation, we'd compute: softmax(QK^T/sqrt(d_k))V
        let out = self.proj.forward(&v)?;
        self.dropout.forward(&out)
    }

    fn parameters(&self) -> HashMap<String, Parameter> {
        let mut params = HashMap::new();
        params.extend(self.qkv.parameters());
        params.extend(self.proj.parameters());
        params
    }

    fn named_parameters(&self) -> HashMap<String, Parameter> {
        self.parameters()
    }

    fn training(&self) -> bool {
        self.qkv.training()
    }

    fn train(&mut self) {
        self.qkv.train();
        self.proj.train();
        self.dropout.train();
    }

    fn eval(&mut self) {
        self.qkv.eval();
        self.proj.eval();
        self.dropout.eval();
    }

    fn to_device(&mut self, device: DeviceType) -> Result<()> {
        self.qkv.to_device(device)?;
        self.proj.to_device(device)?;
        self.dropout.to_device(device)?;
        Ok(())
    }

    fn set_training(&mut self, training: bool) {
        if training {
            self.train();
        } else {
            self.eval();
        }
    }
}

/// Transformer Encoder Block
pub struct TransformerBlock {
    norm1: LayerNorm,
    attn: MultiHeadAttention,
    norm2: LayerNorm,
    mlp: Sequential,
    dropout: Dropout,
}

impl TransformerBlock {
    pub fn new(embed_dim: usize, num_heads: usize, mlp_ratio: usize, dropout: f32) -> Result<Self> {
        let mlp_hidden_dim = embed_dim * mlp_ratio;

        let mlp = Sequential::new()
            .add(Linear::new(embed_dim, mlp_hidden_dim, true))
            .add(GELU::new(false))
            .add(Linear::new(mlp_hidden_dim, embed_dim, true))
            .add(Dropout::new(dropout));

        Ok(Self {
            norm1: LayerNorm::new(vec![embed_dim], 1e-5, true, torsh_core::DeviceType::Cpu)?,
            attn: MultiHeadAttention::new(embed_dim, num_heads, dropout),
            norm2: LayerNorm::new(vec![embed_dim], 1e-5, true, torsh_core::DeviceType::Cpu)?,
            mlp,
            dropout: Dropout::new(dropout),
        })
    }
}

impl Module for TransformerBlock {
    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        // Self-attention with residual connection
        let normed = self.norm1.forward(input)?;
        let attn_out = self.attn.forward(&normed)?;
        let x = input.add(&attn_out)?;

        // MLP with residual connection
        let normed = self.norm2.forward(&x)?;
        let mlp_out = self.mlp.forward(&normed)?;
        x.add(&mlp_out)
    }

    fn parameters(&self) -> HashMap<String, Parameter> {
        let mut params = HashMap::new();
        params.extend(self.norm1.parameters());
        params.extend(self.attn.parameters());
        params.extend(self.norm2.parameters());
        params.extend(self.mlp.parameters());
        params
    }

    fn named_parameters(&self) -> HashMap<String, Parameter> {
        self.parameters()
    }

    fn training(&self) -> bool {
        self.attn.training()
    }

    fn train(&mut self) {
        self.norm1.train();
        self.attn.train();
        self.norm2.train();
        self.mlp.train();
        self.dropout.train();
    }

    fn eval(&mut self) {
        self.norm1.eval();
        self.attn.eval();
        self.norm2.eval();
        self.mlp.eval();
        self.dropout.eval();
    }

    fn to_device(&mut self, device: DeviceType) -> Result<()> {
        self.norm1.to_device(device)?;
        self.attn.to_device(device)?;
        self.norm2.to_device(device)?;
        self.mlp.to_device(device)?;
        self.dropout.to_device(device)?;
        Ok(())
    }

    fn set_training(&mut self, training: bool) {
        if training {
            self.train();
        } else {
            self.eval();
        }
    }
}

/// Vision Transformer (ViT) Configuration
#[derive(Debug, Clone)]
pub struct ViTConfig {
    pub image_size: usize,
    pub patch_size: usize,
    pub embed_dim: usize,
    pub num_layers: usize,
    pub num_heads: usize,
    pub mlp_ratio: usize,
    pub num_classes: usize,
    pub dropout: f32,
}

impl ViTConfig {
    pub fn vit_tiny_patch16_224() -> Self {
        Self {
            image_size: 224,
            patch_size: 16,
            embed_dim: 192,
            num_layers: 12,
            num_heads: 3,
            mlp_ratio: 4,
            num_classes: 1000,
            dropout: 0.0,
        }
    }

    pub fn vit_small_patch16_224() -> Self {
        Self {
            image_size: 224,
            patch_size: 16,
            embed_dim: 384,
            num_layers: 12,
            num_heads: 6,
            mlp_ratio: 4,
            num_classes: 1000,
            dropout: 0.0,
        }
    }

    pub fn vit_base_patch16_224() -> Self {
        Self {
            image_size: 224,
            patch_size: 16,
            embed_dim: 768,
            num_layers: 12,
            num_heads: 12,
            mlp_ratio: 4,
            num_classes: 1000,
            dropout: 0.1,
        }
    }

    pub fn vit_large_patch16_224() -> Self {
        Self {
            image_size: 224,
            patch_size: 16,
            embed_dim: 1024,
            num_layers: 24,
            num_heads: 16,
            mlp_ratio: 4,
            num_classes: 1000,
            dropout: 0.1,
        }
    }

    pub fn vit_huge_patch14_224() -> Self {
        Self {
            image_size: 224,
            patch_size: 14,
            embed_dim: 1280,
            num_layers: 32,
            num_heads: 16,
            mlp_ratio: 4,
            num_classes: 1000,
            dropout: 0.1,
        }
    }
}

/// Vision Transformer (ViT) model
pub struct VisionTransformer {
    patch_embed: Conv2d,
    cls_token: Parameter,
    pos_embed: Parameter,
    pos_dropout: Dropout,
    blocks: Vec<TransformerBlock>,
    norm: LayerNorm,
    head: Linear,
    config: ViTConfig,
    is_training: bool,
}

impl VisionTransformer {
    pub fn new(vit_config: ViTConfig) -> Result<Self> {
        let num_patches = (vit_config.image_size / vit_config.patch_size).pow(2);
        let seq_len = num_patches + 1; // +1 for class token

        // Patch embedding: convolution that extracts patches
        let patch_embed = Conv2d::new(
            3,
            vit_config.embed_dim,
            (vit_config.patch_size, vit_config.patch_size),
            (vit_config.patch_size, vit_config.patch_size),
            (0, 0),
            (1, 1),
            false,
            1,
        );

        // Learnable parameters (simplified - would need proper initialization)
        let cls_token = Parameter::new(
            torsh_tensor::creation::zeros(&[1, 1, vit_config.embed_dim])
                .expect("tensor creation should succeed"),
        );
        let pos_embed = Parameter::new(
            torsh_tensor::creation::zeros(&[1, seq_len, vit_config.embed_dim])
                .expect("tensor creation should succeed"),
        );

        // Transformer blocks
        let mut blocks = Vec::new();
        for _ in 0..vit_config.num_layers {
            blocks.push(TransformerBlock::new(
                vit_config.embed_dim,
                vit_config.num_heads,
                vit_config.mlp_ratio,
                vit_config.dropout,
            )?);
        }

        Ok(Self {
            patch_embed,
            cls_token,
            pos_embed,
            pos_dropout: Dropout::new(vit_config.dropout),
            blocks,
            norm: LayerNorm::new(
                vec![vit_config.embed_dim],
                1e-5,
                true,
                torsh_core::DeviceType::Cpu,
            )?,
            head: Linear::new(vit_config.embed_dim, vit_config.num_classes, true),
            config: vit_config,
            is_training: true,
        })
    }

    pub fn from_config(model_config: ModelConfig, vit_config: ViTConfig) -> Result<Self> {
        let mut config = vit_config;
        config.num_classes = model_config.num_classes;
        config.dropout = model_config.dropout;
        Self::new(config)
    }

    pub fn vit_tiny_patch16_224(config: ModelConfig) -> Result<Self> {
        Self::from_config(config, ViTConfig::vit_tiny_patch16_224())
    }

    pub fn vit_small_patch16_224(config: ModelConfig) -> Result<Self> {
        Self::from_config(config, ViTConfig::vit_small_patch16_224())
    }

    pub fn vit_base_patch16_224(config: ModelConfig) -> Result<Self> {
        Self::from_config(config, ViTConfig::vit_base_patch16_224())
    }

    pub fn vit_large_patch16_224(config: ModelConfig) -> Result<Self> {
        Self::from_config(config, ViTConfig::vit_large_patch16_224())
    }

    pub fn vit_huge_patch14_224(config: ModelConfig) -> Result<Self> {
        Self::from_config(config, ViTConfig::vit_huge_patch14_224())
    }
}

impl Module for VisionTransformer {
    fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let batch_size = input.shape().dims()[0];

        // Patch embedding: [B, C, H, W] -> [B, embed_dim, H/P, W/P]
        let mut x = self.patch_embed.forward(input)?;

        // Flatten patches: [B, embed_dim, H/P, W/P] -> [B, num_patches, embed_dim]
        let num_patches = x.shape().dims()[2] * x.shape().dims()[3];
        x = x.view(&[
            batch_size as i32,
            self.config.embed_dim as i32,
            num_patches as i32,
        ])?;
        x = x.transpose(1, 2)?; // [B, num_patches, embed_dim]

        // Add class token
        let cls_tokens =
            self.cls_token
                .tensor()
                .read()
                .expand(&[batch_size, 1, self.config.embed_dim])?;
        x = Tensor::cat(&[&cls_tokens, &x], 1)?; // [B, num_patches+1, embed_dim]

        // Add positional embeddings
        x = x.add(&*self.pos_embed.tensor().read())?;
        x = self.pos_dropout.forward(&x)?;

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

        // Layer norm and classification head
        x = self.norm.forward(&x)?;

        // Take class token (first token) for classification
        let cls_token = x.slice(1, 0, 1)?; // [B, 1, embed_dim]
        let cls_token = cls_token
            .to_tensor()?
            .view(&[batch_size as i32, self.config.embed_dim as i32])?; // [B, embed_dim]

        self.head.forward(&cls_token)
    }

    fn parameters(&self) -> HashMap<String, Parameter> {
        let mut params = HashMap::new();
        params.extend(self.patch_embed.parameters());
        params.insert("cls_token".to_string(), self.cls_token.clone());
        params.insert("pos_embed".to_string(), self.pos_embed.clone());

        for (i, block) in self.blocks.iter().enumerate() {
            let block_params = block.parameters();
            for (key, param) in block_params {
                params.insert(format!("block{}.{}", i, key), param);
            }
        }

        params.extend(self.norm.parameters());
        params.extend(self.head.parameters());
        params
    }

    fn named_parameters(&self) -> HashMap<String, Parameter> {
        self.parameters()
    }

    fn training(&self) -> bool {
        self.is_training
    }

    fn train(&mut self) {
        self.is_training = true;
        self.patch_embed.train();
        self.pos_dropout.train();
        for block in &mut self.blocks {
            block.train();
        }
        self.norm.train();
        self.head.train();
    }

    fn eval(&mut self) {
        self.is_training = false;
        self.patch_embed.eval();
        self.pos_dropout.eval();
        for block in &mut self.blocks {
            block.eval();
        }
        self.norm.eval();
        self.head.eval();
    }

    fn to_device(&mut self, device: DeviceType) -> Result<()> {
        self.patch_embed.to_device(device)?;
        self.pos_dropout.to_device(device)?;
        for block in &mut self.blocks {
            block.to_device(device)?;
        }
        self.norm.to_device(device)?;
        self.head.to_device(device)?;
        Ok(())
    }

    fn set_training(&mut self, training: bool) {
        if training {
            self.train();
        } else {
            self.eval();
        }
    }
}

impl VisionModel for VisionTransformer {
    fn num_classes(&self) -> usize {
        self.config.num_classes
    }

    fn input_size(&self) -> (usize, usize) {
        (self.config.image_size, self.config.image_size)
    }

    fn name(&self) -> &str {
        "VisionTransformer"
    }
}