trustformers-models 0.1.1

use trustformers_core::{
    errors::{tensor_op_error, Result},
    layers::{Embedding, LayerNorm, Linear},
    tensor::Tensor,
    traits::Layer,
};

use crate::biologically_inspired::{
    biological_memory::BiologicalMemory,
    capsule_networks::CapsuleNetwork,
    config::{BiologicalArchitecture, BiologicalConfig},
    dendritic_computation::DendriticComputation,
    hopfield_networks::HopfieldNetwork,
    liquid_time_constant::LiquidTimeConstantNetwork,
    neural_turing_machine::NeuralTuringMachine,
    reservoir_computing::ReservoirComputing,
    spiking_networks::SpikingNeuralNetwork,
};

/// Biologically-inspired model output
#[derive(Debug, Clone)]
pub struct BiologicalModelOutput {
    /// Hidden states from the model
    pub hidden_states: Tensor,
    /// Spike trains (for spiking networks)
    pub spike_trains: Option<Tensor>,
    /// Memory states (for memory-based models)
    pub memory_states: Option<Tensor>,
    /// Attention weights (for attention-based models)
    pub attention_weights: Option<Tensor>,
    /// Capsule outputs (for capsule networks)
    pub capsule_outputs: Option<Tensor>,
    /// Dendritic activations (for dendritic computation)
    pub dendritic_activations: Option<Tensor>,
    /// Plasticity traces (for learning)
    pub plasticity_traces: Option<Tensor>,
}

/// Core biologically-inspired model
#[derive(Debug)]
pub struct BiologicalModel {
    /// Configuration
    pub config: BiologicalConfig,
    /// Token embeddings
    pub embeddings: Embedding,
    /// Layer normalization
    pub layer_norm: LayerNorm,
    /// Architecture-specific model
    pub architecture: BiologicalArchitectureModel,
    /// Output projection
    pub output_projection: Linear,
}

/// Architecture-specific model implementations
#[derive(Debug)]
pub enum BiologicalArchitectureModel {
    /// Spiking neural network
    SpikingNeuralNetwork(SpikingNeuralNetwork),
    /// Hopfield network
    HopfieldNetwork(HopfieldNetwork),
    /// Liquid time-constant network
    LiquidTimeConstant(LiquidTimeConstantNetwork),
    /// Neural Turing machine
    NeuralTuringMachine(NeuralTuringMachine),
    /// Reservoir computing
    ReservoirComputing(ReservoirComputing),
    /// Capsule network
    CapsuleNetwork(CapsuleNetwork),
    /// Dendritic computation
    DendriticComputation(DendriticComputation),
    /// Biological memory
    BiologicalMemory(BiologicalMemory),
}

impl BiologicalModel {
    /// Create a new biologically-inspired model
    pub fn new(config: BiologicalConfig) -> Result<Self> {
        let embeddings = Embedding::new(config.vocab_size, config.d_model, None)?;
        let layer_norm = LayerNorm::new(vec![config.d_model], 1e-12)?;
        let output_projection = Linear::new(config.d_model, config.vocab_size, config.use_bias);

        let architecture = match config.architecture {
            BiologicalArchitecture::SpikingNeuralNetwork => {
                BiologicalArchitectureModel::SpikingNeuralNetwork(SpikingNeuralNetwork::new(
                    &config,
                )?)
            },
            BiologicalArchitecture::HopfieldNetwork => {
                BiologicalArchitectureModel::HopfieldNetwork(HopfieldNetwork::new(&config)?)
            },
            BiologicalArchitecture::LiquidTimeConstant => {
                BiologicalArchitectureModel::LiquidTimeConstant(LiquidTimeConstantNetwork::new(
                    &config,
                )?)
            },
            BiologicalArchitecture::NeuralTuringMachine => {
                BiologicalArchitectureModel::NeuralTuringMachine(NeuralTuringMachine::new(&config)?)
            },
            BiologicalArchitecture::ReservoirComputing => {
                BiologicalArchitectureModel::ReservoirComputing(ReservoirComputing::new(&config)?)
            },
            BiologicalArchitecture::CapsuleNetwork => {
                BiologicalArchitectureModel::CapsuleNetwork(CapsuleNetwork::new(&config)?)
            },
            BiologicalArchitecture::DendriticComputation => {
                BiologicalArchitectureModel::DendriticComputation(DendriticComputation::new(
                    &config,
                )?)
            },
            BiologicalArchitecture::BiologicalMemory => {
                BiologicalArchitectureModel::BiologicalMemory(BiologicalMemory::new(&config)?)
            },
        };

        Ok(Self {
            config,
            embeddings,
            layer_norm,
            architecture,
            output_projection,
        })
    }

    /// Forward pass through the model
    pub fn forward(&mut self, input_ids: &Tensor) -> Result<BiologicalModelOutput> {
        let _batch_size = input_ids.shape()[0];
        let _seq_len = input_ids.shape()[1];

        // Convert tensor to token IDs - assuming input_ids is I64 tensor with token IDs
        let token_ids = match input_ids {
            Tensor::I64(arr) => arr.iter().map(|&x| x as u32).collect::<Vec<u32>>(),
            _ => {
                return Err(tensor_op_error(
                    "tensor_operation",
                    "Expected I64 tensor for input_ids",
                ))
            },
        };

        // Get embeddings
        let embeddings = self.embeddings.forward(token_ids)?;
        let embeddings = self.layer_norm.forward(embeddings)?;

        // Process through architecture-specific model
        let output = match &mut self.architecture {
            BiologicalArchitectureModel::SpikingNeuralNetwork(model) => {
                model.forward(&embeddings)?
            },
            BiologicalArchitectureModel::HopfieldNetwork(model) => model.forward(&embeddings)?,
            BiologicalArchitectureModel::LiquidTimeConstant(model) => model.forward(&embeddings)?,
            BiologicalArchitectureModel::NeuralTuringMachine(model) => {
                model.forward(&embeddings)?
            },
            BiologicalArchitectureModel::ReservoirComputing(model) => model.forward(&embeddings)?,
            BiologicalArchitectureModel::CapsuleNetwork(model) => model.forward(&embeddings)?,
            BiologicalArchitectureModel::DendriticComputation(model) => {
                model.forward(&embeddings)?
            },
            BiologicalArchitectureModel::BiologicalMemory(model) => model.forward(&embeddings)?,
        };

        Ok(output)
    }

    /// Get model configuration
    pub fn config(&self) -> &BiologicalConfig {
        &self.config
    }

    /// Get architecture type
    pub fn architecture(&self) -> &BiologicalArchitecture {
        &self.config.architecture
    }

    /// Update plasticity (for learning models)
    pub fn update_plasticity(&mut self, targets: &Tensor) -> Result<()> {
        match &mut self.architecture {
            BiologicalArchitectureModel::SpikingNeuralNetwork(model) => {
                model.update_plasticity(targets)?;
            },
            BiologicalArchitectureModel::HopfieldNetwork(model) => {
                model.update_plasticity(targets)?;
            },
            BiologicalArchitectureModel::BiologicalMemory(model) => {
                model.update_plasticity(targets)?;
            },
            _ => {
                // Some architectures don't support plasticity updates
            },
        }
        Ok(())
    }

    /// Reset internal states (for stateful models)
    pub fn reset_states(&mut self) -> Result<()> {
        match &mut self.architecture {
            BiologicalArchitectureModel::SpikingNeuralNetwork(model) => {
                model.reset_states()?;
            },
            BiologicalArchitectureModel::LiquidTimeConstant(model) => {
                model.reset_states()?;
            },
            BiologicalArchitectureModel::NeuralTuringMachine(model) => {
                model.reset_states()?;
            },
            BiologicalArchitectureModel::ReservoirComputing(model) => {
                model.reset_states()?;
            },
            BiologicalArchitectureModel::BiologicalMemory(model) => {
                model.reset_states()?;
            },
            _ => {
                // Some architectures don't have internal states
            },
        }
        Ok(())
    }

    /// Get memory usage statistics
    pub fn get_memory_stats(&self) -> BiologicalMemoryStats {
        let base_params = self.embeddings.parameter_count()
            + self.layer_norm.parameter_count()
            + self.output_projection.parameter_count();

        let (architecture_params, memory_usage) = match &self.architecture {
            BiologicalArchitectureModel::SpikingNeuralNetwork(model) => {
                (model.parameter_count(), model.memory_usage())
            },
            BiologicalArchitectureModel::HopfieldNetwork(model) => {
                (model.parameter_count(), model.memory_usage())
            },
            BiologicalArchitectureModel::LiquidTimeConstant(model) => {
                (model.parameter_count(), model.memory_usage())
            },
            BiologicalArchitectureModel::NeuralTuringMachine(model) => {
                (model.parameter_count(), model.memory_usage())
            },
            BiologicalArchitectureModel::ReservoirComputing(model) => {
                (model.parameter_count(), model.memory_usage())
            },
            BiologicalArchitectureModel::CapsuleNetwork(model) => {
                (model.parameter_count(), model.memory_usage())
            },
            BiologicalArchitectureModel::DendriticComputation(model) => {
                (model.parameter_count(), model.memory_usage())
            },
            BiologicalArchitectureModel::BiologicalMemory(model) => {
                (model.parameter_count(), model.memory_usage())
            },
        };

        BiologicalMemoryStats {
            total_parameters: base_params + architecture_params,
            architecture_parameters: architecture_params,
            memory_usage_mb: memory_usage,
            architecture_type: self.config.architecture.clone(),
        }
    }
}

/// Memory statistics for biological models
#[derive(Debug, Clone)]
pub struct BiologicalMemoryStats {
    /// Total number of parameters
    pub total_parameters: usize,
    /// Architecture-specific parameters
    pub architecture_parameters: usize,
    /// Memory usage in MB
    pub memory_usage_mb: f32,
    /// Architecture type
    pub architecture_type: BiologicalArchitecture,
}

/// Biologically-inspired model for causal language modeling
#[derive(Debug)]
pub struct BiologicalModelForCausalLM {
    /// Base model
    pub model: BiologicalModel,
    /// Language modeling head
    pub lm_head: Linear,
}

impl BiologicalModelForCausalLM {
    /// Create a new causal language modeling model
    pub fn new(config: BiologicalConfig) -> Result<Self> {
        let model = BiologicalModel::new(config.clone())?;
        let lm_head = Linear::new(config.d_model, config.vocab_size, false);

        Ok(Self { model, lm_head })
    }

    /// Forward pass for causal language modeling
    pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
        let output = self.model.forward(input_ids)?;
        let logits = self.lm_head.forward(output.hidden_states)?;
        Ok(logits)
    }

    /// Get model configuration
    pub fn config(&self) -> &BiologicalConfig {
        self.model.config()
    }

    /// Update plasticity
    pub fn update_plasticity(&mut self, targets: &Tensor) -> Result<()> {
        self.model.update_plasticity(targets)
    }

    /// Reset internal states
    pub fn reset_states(&mut self) -> Result<()> {
        self.model.reset_states()
    }
}

/// Biologically-inspired model for sequence classification
#[derive(Debug)]
pub struct BiologicalModelForSequenceClassification {
    /// Base model
    pub model: BiologicalModel,
    /// Classification head
    pub classifier: Linear,
    /// Number of labels
    pub num_labels: usize,
}

impl BiologicalModelForSequenceClassification {
    /// Create a new sequence classification model
    pub fn new(config: BiologicalConfig, num_labels: usize) -> Result<Self> {
        let model = BiologicalModel::new(config.clone())?;
        let classifier = Linear::new(config.d_model, num_labels, true);

        Ok(Self {
            model,
            classifier,
            num_labels,
        })
    }

    /// Forward pass for sequence classification
    pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
        let output = self.model.forward(input_ids)?;

        // Take the mean of the sequence for classification (simple pooling for now)
        let pooled = output.hidden_states.mean()?;
        let logits = self.classifier.forward(pooled)?;

        Ok(logits)
    }

    /// Get model configuration
    pub fn config(&self) -> &BiologicalConfig {
        self.model.config()
    }

    /// Update plasticity
    pub fn update_plasticity(&mut self, targets: &Tensor) -> Result<()> {
        self.model.update_plasticity(targets)
    }

    /// Reset internal states
    pub fn reset_states(&mut self) -> Result<()> {
        self.model.reset_states()
    }
}