ruvnet--RuView/v2/crates/wifi-densepose-nn/src/translator.rs

//! Modality translation network for CSI to visual feature space conversion.
//!
//! This module implements the encoder-decoder network that translates
//! WiFi Channel State Information (CSI) into visual feature representations
//! compatible with the DensePose head.

use crate::error::{NnError, NnResult};
use crate::tensor::{Tensor, TensorShape, TensorStats};
use ndarray::Array4;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Configuration for the modality translator
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TranslatorConfig {
    /// Number of input channels (CSI features)
    pub input_channels: usize,
    /// Hidden channel sizes for encoder/decoder
    pub hidden_channels: Vec<usize>,
    /// Number of output channels (visual feature dimensions)
    pub output_channels: usize,
    /// Convolution kernel size
    #[serde(default = "default_kernel_size")]
    pub kernel_size: usize,
    /// Convolution stride
    #[serde(default = "default_stride")]
    pub stride: usize,
    /// Convolution padding
    #[serde(default = "default_padding")]
    pub padding: usize,
    /// Dropout rate
    #[serde(default = "default_dropout_rate")]
    pub dropout_rate: f32,
    /// Activation function
    #[serde(default = "default_activation")]
    pub activation: ActivationType,
    /// Normalization type
    #[serde(default = "default_normalization")]
    pub normalization: NormalizationType,
    /// Whether to use attention mechanism
    #[serde(default)]
    pub use_attention: bool,
    /// Number of attention heads
    #[serde(default = "default_attention_heads")]
    pub attention_heads: usize,
}

fn default_kernel_size() -> usize {
    3
}

fn default_stride() -> usize {
    1
}

fn default_padding() -> usize {
    1
}

fn default_dropout_rate() -> f32 {
    0.1
}

fn default_activation() -> ActivationType {
    ActivationType::ReLU
}

fn default_normalization() -> NormalizationType {
    NormalizationType::BatchNorm
}

fn default_attention_heads() -> usize {
    8
}

/// Type of activation function
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ActivationType {
    /// Rectified Linear Unit
    ReLU,
    /// Leaky ReLU with negative slope
    LeakyReLU,
    /// Gaussian Error Linear Unit
    GELU,
    /// Sigmoid
    Sigmoid,
    /// Tanh
    Tanh,
}

/// Type of normalization
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NormalizationType {
    /// Batch normalization
    BatchNorm,
    /// Instance normalization
    InstanceNorm,
    /// Layer normalization
    LayerNorm,
    /// No normalization
    None,
}

impl Default for TranslatorConfig {
    fn default() -> Self {
        Self {
            input_channels: 128, // CSI feature dimension
            hidden_channels: vec![256, 512, 256],
            output_channels: 256, // Visual feature dimension
            kernel_size: default_kernel_size(),
            stride: default_stride(),
            padding: default_padding(),
            dropout_rate: default_dropout_rate(),
            activation: default_activation(),
            normalization: default_normalization(),
            use_attention: false,
            attention_heads: default_attention_heads(),
        }
    }
}

impl TranslatorConfig {
    /// Create a new translator configuration
    pub fn new(input_channels: usize, hidden_channels: Vec<usize>, output_channels: usize) -> Self {
        Self {
            input_channels,
            hidden_channels,
            output_channels,
            ..Default::default()
        }
    }

    /// Enable attention mechanism
    pub fn with_attention(mut self, num_heads: usize) -> Self {
        self.use_attention = true;
        self.attention_heads = num_heads;
        self
    }

    /// Set activation type
    pub fn with_activation(mut self, activation: ActivationType) -> Self {
        self.activation = activation;
        self
    }

    /// Validate configuration
    pub fn validate(&self) -> NnResult<()> {
        if self.input_channels == 0 {
            return Err(NnError::config("input_channels must be positive"));
        }
        if self.hidden_channels.is_empty() {
            return Err(NnError::config("hidden_channels must not be empty"));
        }
        if self.output_channels == 0 {
            return Err(NnError::config("output_channels must be positive"));
        }
        if self.use_attention && self.attention_heads == 0 {
            return Err(NnError::config(
                "attention_heads must be positive when using attention",
            ));
        }
        Ok(())
    }

    /// Get the bottleneck dimension (smallest hidden channel)
    pub fn bottleneck_dim(&self) -> usize {
        *self.hidden_channels.last().unwrap_or(&self.output_channels)
    }
}

/// Output from the modality translator
#[derive(Debug, Clone)]
pub struct TranslatorOutput {
    /// Translated visual features
    pub features: Tensor,
    /// Intermediate encoder features (for skip connections)
    pub encoder_features: Option<Vec<Tensor>>,
    /// Attention weights (if attention is used)
    pub attention_weights: Option<Tensor>,
}

/// Weights for the modality translator
#[derive(Debug, Clone)]
pub struct TranslatorWeights {
    /// Encoder layer weights
    pub encoder: Vec<ConvBlockWeights>,
    /// Decoder layer weights
    pub decoder: Vec<ConvBlockWeights>,
    /// Attention weights (if used)
    pub attention: Option<AttentionWeights>,
}

/// Weights for a convolutional block
#[derive(Debug, Clone)]
pub struct ConvBlockWeights {
    /// Convolution weights
    pub conv_weight: Array4<f32>,
    /// Convolution bias
    pub conv_bias: Option<ndarray::Array1<f32>>,
    /// Normalization gamma
    pub norm_gamma: Option<ndarray::Array1<f32>>,
    /// Normalization beta
    pub norm_beta: Option<ndarray::Array1<f32>>,
    /// Running mean for batch norm
    pub running_mean: Option<ndarray::Array1<f32>>,
    /// Running var for batch norm
    pub running_var: Option<ndarray::Array1<f32>>,
}

/// Weights for multi-head attention
#[derive(Debug, Clone)]
pub struct AttentionWeights {
    /// Query projection
    pub query_weight: ndarray::Array2<f32>,
    /// Key projection
    pub key_weight: ndarray::Array2<f32>,
    /// Value projection
    pub value_weight: ndarray::Array2<f32>,
    /// Output projection
    pub output_weight: ndarray::Array2<f32>,
    /// Output bias
    pub output_bias: ndarray::Array1<f32>,
}

/// Modality translator for CSI to visual feature conversion
#[derive(Debug)]
pub struct ModalityTranslator {
    config: TranslatorConfig,
    /// Pre-loaded weights for native inference
    weights: Option<TranslatorWeights>,
}

impl ModalityTranslator {
    /// Create a new modality translator
    pub fn new(config: TranslatorConfig) -> NnResult<Self> {
        config.validate()?;
        Ok(Self {
            config,
            weights: None,
        })
    }

    /// Create with pre-loaded weights
    pub fn with_weights(config: TranslatorConfig, weights: TranslatorWeights) -> NnResult<Self> {
        config.validate()?;
        Ok(Self {
            config,
            weights: Some(weights),
        })
    }

    /// Get the configuration
    pub fn config(&self) -> &TranslatorConfig {
        &self.config
    }

    /// Check if weights are loaded
    pub fn has_weights(&self) -> bool {
        self.weights.is_some()
    }

    /// Get expected input shape
    pub fn expected_input_shape(
        &self,
        batch_size: usize,
        height: usize,
        width: usize,
    ) -> TensorShape {
        TensorShape::new(vec![batch_size, self.config.input_channels, height, width])
    }

    /// Validate input tensor
    pub fn validate_input(&self, input: &Tensor) -> NnResult<()> {
        let shape = input.shape();
        if shape.ndim() != 4 {
            return Err(NnError::shape_mismatch(
                vec![0, self.config.input_channels, 0, 0],
                shape.dims().to_vec(),
            ));
        }
        if shape.dim(1) != Some(self.config.input_channels) {
            return Err(NnError::invalid_input(format!(
                "Expected {} input channels, got {:?}",
                self.config.input_channels,
                shape.dim(1)
            )));
        }
        Ok(())
    }

    /// Forward pass through the translator
    ///
    /// # Errors
    /// Returns an error if no model weights are loaded. Load weights with
    /// `with_weights()` before calling forward(). Use `forward_mock()` in tests.
    pub fn forward(&self, input: &Tensor) -> NnResult<TranslatorOutput> {
        self.validate_input(input)?;

        if let Some(ref _weights) = self.weights {
            self.forward_native(input)
        } else {
            Err(NnError::inference("No model weights loaded. Load weights with with_weights() before calling forward(). Use MockBackend for testing."))
        }
    }

    /// Encode input to latent space
    ///
    /// # Errors
    /// Returns an error if no model weights are loaded.
    pub fn encode(&self, input: &Tensor) -> NnResult<Vec<Tensor>> {
        self.validate_input(input)?;

        if self.weights.is_none() {
            return Err(NnError::inference(
                "No model weights loaded. Cannot encode without weights.",
            ));
        }

        // Real encoding through the encoder path of forward_native
        let output = self.forward_native(input)?;
        output
            .encoder_features
            .ok_or_else(|| NnError::inference("Encoder features not available from forward pass"))
    }

    /// Decode from latent space
    ///
    /// # Errors
    /// Returns an error if no model weights are loaded or if encoded features are empty.
    pub fn decode(&self, encoded_features: &[Tensor]) -> NnResult<Tensor> {
        if encoded_features.is_empty() {
            return Err(NnError::invalid_input("No encoded features provided"));
        }
        if self.weights.is_none() {
            return Err(NnError::inference(
                "No model weights loaded. Cannot decode without weights.",
            ));
        }

        let last_feat = encoded_features.last().unwrap();
        let shape = last_feat.shape();
        let batch = shape.dim(0).unwrap_or(1);

        // Determine output spatial dimensions based on encoder structure
        let out_height = shape.dim(2).unwrap_or(1) * 2_usize.pow(encoded_features.len() as u32 - 1);
        let out_width = shape.dim(3).unwrap_or(1) * 2_usize.pow(encoded_features.len() as u32 - 1);

        Ok(Tensor::zeros_4d([
            batch,
            self.config.output_channels,
            out_height,
            out_width,
        ]))
    }

    /// Native forward pass with weights
    fn forward_native(&self, input: &Tensor) -> NnResult<TranslatorOutput> {
        let weights = self
            .weights
            .as_ref()
            .ok_or_else(|| NnError::inference("No weights loaded for native inference"))?;

        let input_arr = input.as_array4()?;
        let (_batch, _channels, _height, _width) = input_arr.dim();

        // Encode
        let mut encoder_outputs = Vec::new();
        let mut current = input_arr.clone();

        for (i, block_weights) in weights.encoder.iter().enumerate() {
            let stride = if i == 0 { self.config.stride } else { 2 };
            current = self.apply_conv_block(&current, block_weights, stride)?;
            current = self.apply_activation(&current);
            encoder_outputs.push(Tensor::Float4D(current.clone()));
        }

        // Apply attention if configured
        let attention_weights = if self.config.use_attention {
            if let Some(ref attn_weights) = weights.attention {
                let (attended, attn_w) = self.apply_attention(&current, attn_weights)?;
                current = attended;
                Some(Tensor::Float4D(attn_w))
            } else {
                None
            }
        } else {
            None
        };

        // Decode
        for block_weights in &weights.decoder {
            current = self.apply_deconv_block(&current, block_weights)?;
            current = self.apply_activation(&current);
        }

        // Final tanh normalization
        current = current.mapv(|x| x.tanh());

        Ok(TranslatorOutput {
            features: Tensor::Float4D(current),
            encoder_features: Some(encoder_outputs),
            attention_weights,
        })
    }

    /// Mock forward pass for testing
    #[cfg(test)]
    fn forward_mock(&self, input: &Tensor) -> NnResult<TranslatorOutput> {
        let shape = input.shape();
        let batch = shape.dim(0).unwrap_or(1);
        let height = shape.dim(2).unwrap_or(64);
        let width = shape.dim(3).unwrap_or(64);

        // Output has same spatial dimensions but different channels
        let features = Tensor::zeros_4d([batch, self.config.output_channels, height, width]);

        Ok(TranslatorOutput {
            features,
            encoder_features: None,
            attention_weights: None,
        })
    }

    /// Apply a convolutional block
    fn apply_conv_block(
        &self,
        input: &Array4<f32>,
        weights: &ConvBlockWeights,
        stride: usize,
    ) -> NnResult<Array4<f32>> {
        let (batch, in_channels, in_height, in_width) = input.dim();
        let (out_channels, _, kernel_h, kernel_w) = weights.conv_weight.dim();

        let out_height = (in_height + 2 * self.config.padding - kernel_h) / stride + 1;
        let out_width = (in_width + 2 * self.config.padding - kernel_w) / stride + 1;

        let mut output = Array4::zeros((batch, out_channels, out_height, out_width));

        // Simple strided convolution
        for b in 0..batch {
            for oc in 0..out_channels {
                for oh in 0..out_height {
                    for ow in 0..out_width {
                        let mut sum = 0.0f32;
                        for ic in 0..in_channels {
                            for kh in 0..kernel_h {
                                for kw in 0..kernel_w {
                                    let ih = oh * stride + kh;
                                    let iw = ow * stride + kw;
                                    if ih >= self.config.padding
                                        && ih < in_height + self.config.padding
                                        && iw >= self.config.padding
                                        && iw < in_width + self.config.padding
                                    {
                                        let input_val = input[[
                                            b,
                                            ic,
                                            ih - self.config.padding,
                                            iw - self.config.padding,
                                        ]];
                                        sum += input_val * weights.conv_weight[[oc, ic, kh, kw]];
                                    }
                                }
                            }
                        }
                        if let Some(ref bias) = weights.conv_bias {
                            sum += bias[oc];
                        }
                        output[[b, oc, oh, ow]] = sum;
                    }
                }
            }
        }

        // Apply normalization
        self.apply_normalization(&mut output, weights);

        Ok(output)
    }

    /// Apply transposed convolution for upsampling
    fn apply_deconv_block(
        &self,
        input: &Array4<f32>,
        weights: &ConvBlockWeights,
    ) -> NnResult<Array4<f32>> {
        let (batch, in_channels, in_height, in_width) = input.dim();
        let (out_channels, _, _kernel_h, _kernel_w) = weights.conv_weight.dim();

        // Upsample 2x
        let out_height = in_height * 2;
        let out_width = in_width * 2;

        // Simple nearest-neighbor upsampling + conv (approximation of transpose conv)
        let mut output = Array4::zeros((batch, out_channels, out_height, out_width));

        for b in 0..batch {
            for oc in 0..out_channels {
                for oh in 0..out_height {
                    for ow in 0..out_width {
                        let ih = oh / 2;
                        let iw = ow / 2;
                        let mut sum = 0.0f32;
                        for ic in 0..in_channels.min(weights.conv_weight.dim().1) {
                            sum += input[[b, ic, ih.min(in_height - 1), iw.min(in_width - 1)]]
                                * weights.conv_weight[[oc, ic, 0, 0]];
                        }
                        if let Some(ref bias) = weights.conv_bias {
                            sum += bias[oc];
                        }
                        output[[b, oc, oh, ow]] = sum;
                    }
                }
            }
        }

        Ok(output)
    }

    /// Apply normalization to output
    fn apply_normalization(&self, output: &mut Array4<f32>, weights: &ConvBlockWeights) {
        if let (Some(gamma), Some(beta), Some(mean), Some(var)) = (
            &weights.norm_gamma,
            &weights.norm_beta,
            &weights.running_mean,
            &weights.running_var,
        ) {
            let (batch, channels, height, width) = output.dim();
            let eps = 1e-5;

            for b in 0..batch {
                for c in 0..channels {
                    let scale = gamma[c] / (var[c] + eps).sqrt();
                    let shift = beta[c] - mean[c] * scale;
                    for h in 0..height {
                        for w in 0..width {
                            output[[b, c, h, w]] = output[[b, c, h, w]] * scale + shift;
                        }
                    }
                }
            }
        }
    }

    /// Apply activation function
    fn apply_activation(&self, input: &Array4<f32>) -> Array4<f32> {
        match self.config.activation {
            ActivationType::ReLU => input.mapv(|x| x.max(0.0)),
            ActivationType::LeakyReLU => input.mapv(|x| if x > 0.0 { x } else { 0.2 * x }),
            ActivationType::GELU => {
                // Approximate GELU: sqrt(2/π) ≈ 0.797_884_6
                input.mapv(|x| 0.5 * x * (1.0 + (0.797_884_6 * (x + 0.044715 * x.powi(3))).tanh()))
            }
            ActivationType::Sigmoid => input.mapv(|x| 1.0 / (1.0 + (-x).exp())),
            ActivationType::Tanh => input.mapv(|x| x.tanh()),
        }
    }

    /// Apply single-head scaled-dot-product attention over the spatial
    /// sequence: `softmax(Q·Kᵀ / √d) · V`, with `Q/K/V` linear projections of
    /// each token's channel vector and a final output projection.
    ///
    /// The spatial grid `[B, C, H, W]` is treated as a length-`H·W` token
    /// sequence of `C`-dim feature vectors. Each `*_weight` projection is a
    /// `[C × C]` matrix applied per token. This is a genuine attention
    /// operation (not the previous uniform-weight identity stub), so the
    /// returned per-pair attention weights actually depend on the input.
    ///
    /// # Errors
    /// Returns an error if any projection weight is not `[C × C]`, so a
    /// mis-shaped checkpoint can never be silently treated as a no-op.
    fn apply_attention(
        &self,
        input: &Array4<f32>,
        weights: &AttentionWeights,
    ) -> NnResult<(Array4<f32>, Array4<f32>)> {
        let (batch, channels, height, width) = input.dim();
        let seq_len = height * width;

        // Every projection must be a square [C × C] matrix to act per token.
        for (name, w) in [
            ("query_weight", &weights.query_weight),
            ("key_weight", &weights.key_weight),
            ("value_weight", &weights.value_weight),
            ("output_weight", &weights.output_weight),
        ] {
            if w.dim() != (channels, channels) {
                return Err(NnError::invalid_input(format!(
                    "attention {name} must be [{channels} x {channels}], got [{} x {}]",
                    w.dim().0,
                    w.dim().1
                )));
            }
        }
        if weights.output_bias.len() != channels {
            return Err(NnError::shape_mismatch(
                vec![channels],
                vec![weights.output_bias.len()],
            ));
        }

        // Flatten spatial grid into a [seq_len, channels] token matrix per batch.
        // Project to Q, K, V; compute scaled-dot-product attention; project out.
        let scale = 1.0 / (channels as f32).sqrt();
        let mut out = Array4::zeros((batch, channels, height, width));
        let mut attention_weights = Array4::zeros((batch, 1, seq_len, seq_len));

        for b in 0..batch {
            // Tokens: [seq_len, channels].
            let mut tokens = ndarray::Array2::<f32>::zeros((seq_len, channels));
            for h in 0..height {
                for w in 0..width {
                    let s = h * width + w;
                    for c in 0..channels {
                        tokens[[s, c]] = input[[b, c, h, w]];
                    }
                }
            }

            // Q = tokens·Wqᵀ, etc. (row vector × [C×C] projection).
            let q = tokens.dot(&weights.query_weight.t());
            let k = tokens.dot(&weights.key_weight.t());
            let v = tokens.dot(&weights.value_weight.t());

            // Scores = softmax_row(Q·Kᵀ · scale), then context = Scores·V.
            let scores = q.dot(&k.t()).mapv(|x| x * scale);
            for i in 0..seq_len {
                // Numerically-stable row softmax.
                let mut max = f32::NEG_INFINITY;
                for j in 0..seq_len {
                    max = max.max(scores[[i, j]]);
                }
                let mut sum = 0.0f32;
                let mut row = vec![0.0f32; seq_len];
                for j in 0..seq_len {
                    let e = (scores[[i, j]] - max).exp();
                    row[j] = e;
                    sum += e;
                }
                if sum > 0.0 {
                    for j in 0..seq_len {
                        row[j] /= sum;
                    }
                }
                for j in 0..seq_len {
                    attention_weights[[b, 0, i, j]] = row[j];
                }
            }

            // Context = attention · V, then output projection + bias.
            for h in 0..height {
                for w in 0..width {
                    let i = h * width + w;
                    // ctx[c] = Σ_j attn[i,j] · v[j,c]
                    let mut ctx = vec![0.0f32; channels];
                    for j in 0..seq_len {
                        let a = attention_weights[[b, 0, i, j]];
                        for c in 0..channels {
                            ctx[c] += a * v[[j, c]];
                        }
                    }
                    // out[c] = Σ_c' ctx[c'] · Wo[c, c'] + bias[c]
                    for c in 0..channels {
                        let mut acc = weights.output_bias[c];
                        for cp in 0..channels {
                            acc += ctx[cp] * weights.output_weight[[c, cp]];
                        }
                        out[[b, c, h, w]] = acc;
                    }
                }
            }
        }

        Ok((out, attention_weights))
    }

    /// Compute translation loss between predicted and target features
    pub fn compute_loss(
        &self,
        predicted: &Tensor,
        target: &Tensor,
        loss_type: LossType,
    ) -> NnResult<f32> {
        let pred_arr = predicted.as_array4()?;
        let target_arr = target.as_array4()?;

        if pred_arr.dim() != target_arr.dim() {
            return Err(NnError::shape_mismatch(
                pred_arr.shape().to_vec(),
                target_arr.shape().to_vec(),
            ));
        }

        let n = pred_arr.len() as f32;
        let loss = match loss_type {
            LossType::MSE => {
                pred_arr
                    .iter()
                    .zip(target_arr.iter())
                    .map(|(p, t)| (p - t).powi(2))
                    .sum::<f32>()
                    / n
            }
            LossType::L1 => {
                pred_arr
                    .iter()
                    .zip(target_arr.iter())
                    .map(|(p, t)| (p - t).abs())
                    .sum::<f32>()
                    / n
            }
            LossType::SmoothL1 => {
                pred_arr
                    .iter()
                    .zip(target_arr.iter())
                    .map(|(p, t)| {
                        let diff = (p - t).abs();
                        if diff < 1.0 {
                            0.5 * diff.powi(2)
                        } else {
                            diff - 0.5
                        }
                    })
                    .sum::<f32>()
                    / n
            }
        };

        Ok(loss)
    }

    /// Get feature statistics
    pub fn get_feature_stats(&self, features: &Tensor) -> NnResult<TensorStats> {
        TensorStats::from_tensor(features)
    }

    /// Get intermediate features for visualization
    pub fn get_intermediate_features(&self, input: &Tensor) -> NnResult<HashMap<String, Tensor>> {
        let output = self.forward(input)?;

        let mut features = HashMap::new();
        features.insert("output".to_string(), output.features);

        if let Some(encoder_feats) = output.encoder_features {
            for (i, feat) in encoder_feats.into_iter().enumerate() {
                features.insert(format!("encoder_{}", i), feat);
            }
        }

        if let Some(attn) = output.attention_weights {
            features.insert("attention".to_string(), attn);
        }

        Ok(features)
    }
}

/// Type of loss function for training
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LossType {
    /// Mean Squared Error
    MSE,
    /// L1 / Mean Absolute Error
    L1,
    /// Smooth L1 (Huber) loss
    SmoothL1,
}

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

    #[test]
    fn test_config_validation() {
        let config = TranslatorConfig::default();
        assert!(config.validate().is_ok());

        let invalid = TranslatorConfig {
            input_channels: 0,
            ..Default::default()
        };
        assert!(invalid.validate().is_err());
    }

    #[test]
    fn test_translator_creation() {
        let config = TranslatorConfig::new(128, vec![256, 512, 256], 256);
        let translator = ModalityTranslator::new(config).unwrap();
        assert!(!translator.has_weights());
    }

    #[test]
    fn test_forward_without_weights_errors() {
        let config = TranslatorConfig::new(128, vec![256, 512, 256], 256);
        let translator = ModalityTranslator::new(config).unwrap();

        let input = Tensor::zeros_4d([1, 128, 64, 64]);
        let result = translator.forward(&input);
        assert!(result.is_err());
        assert!(result
            .unwrap_err()
            .to_string()
            .contains("No model weights loaded"));
    }

    #[test]
    fn test_mock_forward() {
        let config = TranslatorConfig::new(128, vec![256, 512, 256], 256);
        let translator = ModalityTranslator::new(config).unwrap();

        let input = Tensor::zeros_4d([1, 128, 64, 64]);
        let output = translator.forward_mock(&input).unwrap();

        assert_eq!(output.features.shape().dim(1), Some(256));
    }

    #[test]
    fn test_encode_without_weights_errors() {
        let config = TranslatorConfig::new(128, vec![256, 512], 256);
        let translator = ModalityTranslator::new(config).unwrap();

        let input = Tensor::zeros_4d([1, 128, 64, 64]);
        let result = translator.encode(&input);
        assert!(result.is_err());
        assert!(result
            .unwrap_err()
            .to_string()
            .contains("No model weights loaded"));
    }

    #[test]
    fn test_decode_without_weights_errors() {
        let config = TranslatorConfig::new(128, vec![256, 512], 256);
        let translator = ModalityTranslator::new(config).unwrap();

        let features = vec![Tensor::zeros_4d([1, 512, 32, 32])];
        let result = translator.decode(&features);
        assert!(result.is_err());
        assert!(result
            .unwrap_err()
            .to_string()
            .contains("No model weights loaded"));
    }

    #[test]
    fn test_activation_types() {
        let config = TranslatorConfig::default().with_activation(ActivationType::GELU);
        assert_eq!(config.activation, ActivationType::GELU);
    }

    // ADR-155 §Tier-2: apply_attention must perform real scaled-dot-product
    // attention, not return uniform 1/seq_len weights. With identity Q/K/V
    // projections and a non-uniform input, the attention weights must NOT all
    // equal 1/seq_len, and each row must still be a valid distribution.
    #[test]
    fn test_attention_is_not_uniform_stub() {
        let channels = 4usize;
        let height = 2usize;
        let width = 2usize;
        let seq_len = height * width;

        // Identity projections so Q=K=V=tokens; output = identity, zero bias.
        let identity = ndarray::Array2::<f32>::eye(channels);
        let weights = AttentionWeights {
            query_weight: identity.clone(),
            key_weight: identity.clone(),
            value_weight: identity.clone(),
            output_weight: identity,
            output_bias: ndarray::Array1::zeros(channels),
        };

        // Non-uniform input: each spatial location has a distinct feature vector.
        let mut input = Array4::<f32>::zeros((1, channels, height, width));
        for c in 0..channels {
            for h in 0..height {
                for w in 0..width {
                    input[[0, c, h, w]] = (c + 2 * h + 4 * w) as f32;
                }
            }
        }

        let config = TranslatorConfig::default().with_attention(1);
        let translator = ModalityTranslator::new(config).unwrap();
        let (out, attn) = translator.apply_attention(&input, &weights).unwrap();

        // Each attention row must sum to 1 (valid softmax distribution).
        for i in 0..seq_len {
            let row_sum: f32 = (0..seq_len).map(|j| attn[[0, 0, i, j]]).sum();
            assert!((row_sum - 1.0).abs() < 1e-5, "row {i} sum = {row_sum}");
        }
        // Weights must NOT all be the uniform 1/seq_len value of the old stub.
        let uniform = 1.0 / seq_len as f32;
        let any_non_uniform = (0..seq_len)
            .flat_map(|i| (0..seq_len).map(move |j| (i, j)))
            .any(|(i, j)| (attn[[0, 0, i, j]] - uniform).abs() > 1e-4);
        assert!(any_non_uniform, "attention collapsed to uniform stub");
        // Output is finite and shaped like the input.
        assert_eq!(out.dim(), input.dim());
        assert!(out.iter().all(|v| v.is_finite()));
    }

    // ADR-155 §Tier-2: a mis-shaped projection weight must be rejected, never
    // silently treated as a no-op.
    #[test]
    fn test_attention_rejects_wrong_weight_shape() {
        let channels = 4usize;
        let bad = ndarray::Array2::<f32>::zeros((channels + 1, channels));
        let weights = AttentionWeights {
            query_weight: bad.clone(),
            key_weight: bad.clone(),
            value_weight: bad.clone(),
            output_weight: bad,
            output_bias: ndarray::Array1::zeros(channels),
        };
        let input = Array4::<f32>::zeros((1, channels, 2, 2));
        let config = TranslatorConfig::default().with_attention(1);
        let translator = ModalityTranslator::new(config).unwrap();
        assert!(translator.apply_attention(&input, &weights).is_err());
    }

    #[test]
    fn test_loss_computation() {
        let config = TranslatorConfig::default();
        let translator = ModalityTranslator::new(config).unwrap();

        let pred = Tensor::ones_4d([1, 256, 8, 8]);
        let target = Tensor::zeros_4d([1, 256, 8, 8]);

        let mse = translator
            .compute_loss(&pred, &target, LossType::MSE)
            .unwrap();
        assert_eq!(mse, 1.0);

        let l1 = translator
            .compute_loss(&pred, &target, LossType::L1)
            .unwrap();
        assert_eq!(l1, 1.0);
    }
}