// signal_transduction.sy -- Text-to-Activation Pipeline
// Transducer (sensory organ) + Synapse (projection bridge) + Thalamus (MoE router)
//
// Combined from:
//   transducer.py -- Raw text -> inference weight activations
//   synapse.py    -- Synaptic projection between representation spaces
//   thalamus.py   -- MoE signal routing to correct cortical area
//
// Text dies at the transducer boundary. Only activation patterns propagate.
// Like an action potential encodes stimulus without carrying photons.
//
// Ported from Marjorie McCubbins' Python NCA.
// Patent Pending: U.S. Provisional Application Nos.
//   63/939,190, 63/962,385 & 63/988,485
// Copyright 2025-2026 Nexus Concordat Inc.
//
// Aislinn McCubbins, April 2026


// ============================================================
// TRANSDUCER -- The Sensory Organ
// ============================================================
// Converts raw text into inference weight activations.
// Biological analogue: Cochlea (sound -> electrochemical),
//                      Retina (light -> electrochemical).
//
// The transducer's hidden state at the last layer IS the signal.
// It encodes meaning without containing content.

struct TransducerConfig {
    model_path: String,
    device: String,
    max_length: Int,
    extraction_layer: Int,
}

impl TransducerConfig {
    fn default() -> TransducerConfig {
        TransducerConfig {
            model_path: "",
            device: "cuda",
            max_length: 512,
            extraction_layer: -1,
        }
    }
}

// Signal returned by the transducer after text is consumed
struct TransducerSignal {
    signal: Vec<Vec<Float>>,     // [seq_len, hidden_dim] -- the inference weights
    pooled: Vec<Float>,          // [hidden_dim] -- mean-pooled signal (compact form)
    seq_len: Int,
    signal_norm: Float,          // L2 norm of pooled signal
}

// Batch signal for multiple inputs transduced at once
struct TransducerBatchSignal {
    signal: Vec<Vec<Float>>,     // [batch * seq_len, hidden_dim]
    pooled: Vec<Vec<Float>>,     // [batch, hidden_dim]
    seq_lens: Vec<Int>,
    batch_size: Int,
}

// Layer extraction result for analysis
struct LayerExtraction {
    layer_signals: Vec<Vec<Float>>,  // per-layer pooled signals
    num_layers: Int,
    hidden_dim: Int,
}

temporal struct Transducer {
    state is_loaded: Bool = false,
    state hidden_dim: Int = 0,

    const config: TransducerConfig,
}

impl Transducer {
    fn new(config: TransducerConfig) -> Transducer {
        Transducer {
            is_loaded: false,
            hidden_dim: 0,
            config: config,
        }
    }

    fn load(&mut self) {
        // Load the micro LLM and tokenizer from config.model_path
        // Sets hidden_dim from model config
        self.is_loaded = true
    }

    fn transduce(&self, text: String) -> TransducerSignal {
        // Transduction boundary. Text enters. Weights exit.
        // The text is consumed here and does not propagate.
        //
        // 1. Tokenize -- last time text exists as text
        // 2. Forward pass -- extract hidden states
        // 3. Mean-pool with attention mask
        // 4. Return signal, pooled, seq_len, norm
        TransducerSignal {
            signal: [],
            pooled: [],
            seq_len: 0,
            signal_norm: 0.0,
        }
    }

    fn transduce_batch(&self, texts: Vec<String>) -> TransducerBatchSignal {
        // Batch transduction: multiple texts -> batched signals
        TransducerBatchSignal {
            signal: [],
            pooled: [],
            seq_lens: [],
            batch_size: 0,
        }
    }

    fn extract_all_layers(&self, text: String) -> LayerExtraction {
        // Extract hidden states from ALL layers for analysis.
        // Useful for testing which layer produces the best signal.
        LayerExtraction {
            layer_signals: [],
            num_layers: 0,
            hidden_dim: 0,
        }
    }
}

impl Evolve for Transducer {
    fn evolve(&mut self, dt: Duration) {
        // Transducer is stateless between calls.
        // No temporal evolution needed.
    }
}


// ============================================================
// SYNAPSE -- The Projection Bridge
// ============================================================
// Trainable projection that maps between representation spaces.
// Like a biological synapse: converts presynaptic signal to
// postsynaptic signal.
//
// Bridges: transducer output dim (e.g., 896 for Qwen 0.5B)
//       -> cortex input dim (e.g., 512 for cascade)
//
// The information bottleneck forces lossy compression:
// the original text CANNOT be reconstructed from synaptic output.
// Privacy by architecture, not by policy.

struct SynapseConfig {
    input_dim: Int,
    output_dim: Int,
    num_virtual_tokens: Int,
    bottleneck_dim: Int,
    dropout: Float,
    use_bottleneck: Bool,
}

impl SynapseConfig {
    fn default() -> SynapseConfig {
        SynapseConfig {
            input_dim: 896,
            output_dim: 512,
            num_virtual_tokens: 32,
            bottleneck_dim: 256,
            dropout: 0.1,
            use_bottleneck: true,
        }
    }
}

// Synapse output: virtual tokens + compressed bottleneck
struct SynapseOutput {
    virtual_tokens: Vec<Vec<Float>>,  // [num_virtual_tokens, output_dim]
    bottleneck: Vec<Float>,           // [bottleneck_dim] -- compressed representation
}

struct Synapse {
    config: SynapseConfig,
}

impl Synapse {
    fn new(config: SynapseConfig) -> Synapse {
        // If use_bottleneck:
        //   projection: input -> bottleneck -> bottleneck (GELU + dropout)
        //   expander: bottleneck -> num_vt * output_dim
        // Else:
        //   projection: input -> output (GELU + dropout)
        //   expander: output -> num_vt * output_dim
        // Plus LayerNorm on output
        Synapse { config: config }
    }

    fn forward(&self, signal: Vec<Float>) -> SynapseOutput {
        // Project transducer signal to cortex-compatible virtual tokens.
        //
        // signal: [input_dim] -- pooled inference weights from transducer
        //
        // 1. Project through bottleneck (lossy compression)
        // 2. Expand to virtual token sequence
        // 3. Reshape to [num_virtual_tokens, output_dim]
        // 4. LayerNorm
        SynapseOutput {
            virtual_tokens: [],
            bottleneck: [],
        }
    }

    fn bottleneck_dim(&self) -> Int {
        if self.config.use_bottleneck {
            self.config.bottleneck_dim
        } else {
            self.config.output_dim
        }
    }
}

// Sequence-level synapse for full hidden state sequences.
// Processes FULL sequence [seq_len, input_dim] and projects each
// position independently, preserving sequential structure.
struct SequenceSynapse {
    config: SynapseConfig,
}

impl SequenceSynapse {
    fn new(config: SynapseConfig) -> SequenceSynapse {
        // If use_bottleneck:
        //   projection: input -> bottleneck -> output (GELU + dropout)
        // Else:
        //   projection: input -> output (GELU + dropout)
        // Plus LayerNorm on output
        SequenceSynapse { config: config }
    }

    fn forward(&self, signal_sequence: Vec<Vec<Float>>) -> Vec<Vec<Float>> {
        // Project full sequence of hidden states.
        // signal_sequence: [seq_len, input_dim]
        // Returns: [seq_len, output_dim]
        []
    }
}


// ============================================================
// THALAMUS -- MoE Signal Router
// ============================================================
// Mixture-of-Experts routing on inference weight activations.
// Classifies signal TYPE and routes to appropriate processing.
//
// Biological analogue: Thalamus routes sensory signals:
//   Visual -> occipital cortex
//   Auditory -> temporal cortex
//   Somatosensory -> parietal cortex
//
// Operates ENTIRELY in weight-space.
// Receives inference weights, not text.
// Outputs routing weights + expert-processed signals, not text.

struct ThalamusConfig {
    input_dim: Int,
    num_experts: Int,
    expert_dim: Int,
    top_k: Int,
    dropout: Float,
}

impl ThalamusConfig {
    fn default() -> ThalamusConfig {
        ThalamusConfig {
            input_dim: 896,
            num_experts: 8,
            expert_dim: 512,
            top_k: 2,
            dropout: 0.1,
        }
    }
}

// Single expert in the MoE router.
// Each expert specializes in processing a specific type of signal.
// Analogous to a cortical area specialized for a modality.
struct Expert {
    input_dim: Int,
    expert_dim: Int,
    dropout: Float,
}

impl Expert {
    fn new(input_dim: Int, expert_dim: Int, dropout: Float) -> Expert {
        // Network: Linear(input -> expert) -> GELU -> Dropout
        //       -> Linear(expert -> input) -> Dropout
        Expert {
            input_dim: input_dim,
            expert_dim: expert_dim,
            dropout: dropout,
        }
    }

    fn forward(&self, x: Vec<Float>) -> Vec<Float> {
        // Process signal through expert's specialized pathway
        x
    }
}

// Router output: routed signal + routing weights + diagnostics
struct RouterOutput {
    routed_signal: Vec<Float>,        // [hidden_dim] -- expert-processed signal
    routing_weights: Vec<Float>,      // [num_experts] -- which experts fired
    expert_outputs: Vec<Vec<Float>>,  // [num_experts, hidden_dim] -- individual expert outputs
    balance_loss: Float,              // Load balancing loss for training
    top_k_indices: Vec<Int>,          // Which experts were selected
}

struct SignalRouter {
    config: ThalamusConfig,
    experts: Vec<Expert>,
    balance_coeff: Float,
}

impl SignalRouter {
    fn new(config: ThalamusConfig) -> SignalRouter {
        let mut experts: Vec<Expert> = []
        for i in 0..config.num_experts {
            experts.push(Expert::new(
                config.input_dim, config.expert_dim, config.dropout
            ))
        }
        SignalRouter {
            config: config,
            experts: experts,
            balance_coeff: 0.01,
        }
    }

    fn forward(&self, signal: Vec<Float>) -> RouterOutput {
        // Route signal through MoE.
        //
        // 1. Compute gate logits via linear projection
        // 2. Top-k expert selection
        // 3. H-H rate normalization over selected experts (NOT softmax)
        //      alpha = exp(clamp(logits, -3, 3))
        //      weights = alpha / sum(alpha)
        // 4. Process through selected experts only (sparse)
        // 5. Weighted combination of expert outputs
        // 6. Load balancing loss (encourages even utilization)
        RouterOutput {
            routed_signal: [],
            routing_weights: [],
            expert_outputs: [],
            balance_loss: 0.0,
            top_k_indices: [],
        }
    }

    fn hh_rate_normalize(logits: Vec<Float>) -> Vec<Float> {
        // H-H rate normalization (NOT softmax, NOT sigmoid)
        //   alpha_i = exp(clamp(logit_i, -3.0, 3.0))
        //   weight_i = alpha_i / (sum(alpha) + 1e-8)
        let mut alphas: Vec<Float> = []
        let mut sum = 0.00000001
        for l in logits {
            let clamped = l
            if clamped < -3.0 { clamped = -3.0 }
            if clamped > 3.0 { clamped = 3.0 }
            let a = exp(clamped)
            alphas.push(a)
            sum = sum + a
        }
        let mut weights: Vec<Float> = []
        for a in alphas {
            weights.push(a / sum)
        }
        weights
    }

    fn routing_descriptor(&self, signal: Vec<Float>) -> Vec<Float> {
        // Get ONLY the routing weights -- the compact descriptor.
        // Minimum information crossing the sandbox boundary.
        // Tells the cortex "what kind of signal" without content.
        // These weights are ephemeral. After processing, they mean nothing.
        //
        // 1. Linear gate projection
        // 2. Top-k selection
        // 3. H-H rate normalization
        // 4. Scatter into sparse vector
        let mut weights: Vec<Float> = []
        for i in 0..self.config.num_experts {
            weights.push(0.0)
        }
        weights
    }
}
