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

use super::{config::BiologicalConfig, model::BiologicalModelOutput};

/// Hopfield network memory state
#[derive(Debug, Clone)]
pub struct HopfieldMemoryState {
    /// Stored patterns
    pub patterns: Tensor,
    /// Pattern activations
    pub activations: Tensor,
    /// Memory weights
    pub weights: Tensor,
    /// Current state
    pub current_state: Tensor,
}

/// Modern Hopfield network layer
#[derive(Debug)]
pub struct HopfieldLayer {
    /// Configuration
    pub config: BiologicalConfig,
    /// Query projection
    pub query_projection: Linear,
    /// Key projection
    pub key_projection: Linear,
    /// Value projection
    pub value_projection: Linear,
    /// Output projection
    pub output_projection: Linear,
    /// Memory state
    pub memory_state: Option<HopfieldMemoryState>,
    /// Layer normalization
    pub layer_norm: LayerNorm,
    /// Beta parameter for sharpness
    pub beta: f32,
}

impl HopfieldLayer {
    /// Create a new Hopfield layer
    pub fn new(config: &BiologicalConfig) -> Result<Self> {
        let d_model = config.d_model;
        let _memory_capacity = config.memory_capacity;

        let query_projection = Linear::new(d_model, d_model, config.use_bias);
        let key_projection = Linear::new(d_model, d_model, config.use_bias);
        let value_projection = Linear::new(d_model, d_model, config.use_bias);
        let output_projection = Linear::new(d_model, d_model, config.use_bias);
        let layer_norm = LayerNorm::new(vec![d_model], 1e-12)?;

        Ok(Self {
            config: config.clone(),
            query_projection,
            key_projection,
            value_projection,
            output_projection,
            memory_state: None,
            layer_norm,
            beta: 1.0,
        })
    }

    /// Initialize memory state
    pub fn init_memory(&mut self, batch_size: usize) -> Result<()> {
        let d_model = self.config.d_model;
        let memory_capacity = self.config.memory_capacity;

        // Initialize stored patterns
        let patterns = Tensor::randn(&[memory_capacity, d_model])?
            .scalar_mul(self.config.initializer_range)?;
        let activations = Tensor::zeros(&[batch_size, memory_capacity])?;
        let weights = Tensor::zeros(&[memory_capacity, memory_capacity])?;
        let current_state = Tensor::zeros(&[batch_size, d_model])?;

        self.memory_state = Some(HopfieldMemoryState {
            patterns,
            activations,
            weights,
            current_state,
        });

        Ok(())
    }

    /// Forward pass through Hopfield layer
    pub fn forward(&mut self, input: &Tensor) -> Result<Tensor> {
        let batch_size = input.shape()[0];
        let seq_len = input.shape()[1];

        // Initialize memory if not present
        if self.memory_state.is_none() {
            self.init_memory(batch_size)?;
        }

        let mut outputs = Vec::new();

        // Process each time step
        for t in 0..seq_len {
            let input_t = input.slice(1, t, t + 1)?.squeeze(1)?;
            let output_t = self.forward_timestep(&input_t)?;
            outputs.push(output_t);
        }

        // Stack outputs
        let mut output = outputs[0].clone();
        for i in 1..outputs.len() {
            output = Tensor::concat(&[output, outputs[i].clone()], 1)?;
        }

        Ok(output)
    }

    /// Forward pass for a single timestep
    fn forward_timestep(&mut self, input: &Tensor) -> Result<Tensor> {
        // Project input to query, key, value
        let query = self.query_projection.forward(input.clone())?;
        let (key, value) = {
            let memory_state = self.memory_state.as_ref().unwrap();
            let key = self.key_projection.forward(memory_state.patterns.clone())?;
            let value = self.value_projection.forward(memory_state.patterns.clone())?;
            (key, value)
        };

        // Compute attention weights (modern Hopfield)
        let attention_scores = query.matmul(&key.transpose(0, 1)?)?;
        let attention_weights = attention_scores.softmax(1)?;

        // Compute output
        let output = attention_weights.matmul(&value)?;

        // Update memory state (simplified)
        {
            let memory_state = self.memory_state.as_mut().unwrap();

            // Simple memory update - add current pattern to memory
            let pattern_update = input.mul_scalar(0.1)?; // learning rate
            memory_state.patterns = memory_state.patterns.add(&pattern_update)?;

            // Update current state with output
            memory_state.current_state = output.clone();

            // Update activations (simple running average)
            memory_state.activations =
                memory_state.activations.mul_scalar(0.9)?.add(&output.mul_scalar(0.1)?)?;
        }

        // Apply layer normalization
        let normalized_output = self.layer_norm.forward(output)?;

        // Final projection
        let final_output = self.output_projection.forward(normalized_output)?;

        Ok(final_output)
    }

    /// Update memory state
    #[allow(dead_code)]
    fn update_memory_state(
        &self,
        input: &Tensor,
        output: &Tensor,
        memory_state: &mut HopfieldMemoryState,
    ) -> Result<()> {
        // Update current state
        memory_state.current_state = output.clone();

        // Update pattern activations based on similarity
        let similarities = input.matmul(&memory_state.patterns.transpose(0, 1)?)?;
        memory_state.activations = similarities.softmax(1)?;

        // Update weights using Hebbian learning
        let learning_rate = self.config.learning_rate;
        let outer_product =
            memory_state.activations.transpose(0, 1)?.matmul(&memory_state.activations)?;
        let weight_update = outer_product.mul_scalar(learning_rate)?;
        memory_state.weights = memory_state.weights.add(&weight_update)?;

        Ok(())
    }

    /// Store new pattern in memory
    pub fn store_pattern(&mut self, pattern: &Tensor) -> Result<()> {
        if let Some(memory_state) = &mut self.memory_state {
            let _memory_capacity = self.config.memory_capacity;
            let _pattern_size = pattern.shape()[1];

            // Find least used pattern slot (simple replacement strategy)
            let _activation_sum = memory_state.activations.sum(None, false)?;
            // Use simple strategy - assume index 0 for now (argmin not available)
            let _min_idx = 0usize;

            // Replace pattern (simplified - just update the whole patterns tensor)
            let _new_pattern = pattern.slice(0, 0, 1)?.squeeze(0)?;
            // For now, just update patterns - more complex indexing update needed
            // memory_state.patterns = new_pattern.unsqueeze(0)?;
        }

        Ok(())
    }

    /// Retrieve pattern from memory
    pub fn retrieve_pattern(&mut self, query: &Tensor) -> Result<Tensor> {
        if let Some(memory_state) = &mut self.memory_state {
            // Compute similarities
            let similarities = query.matmul(&memory_state.patterns.transpose(0, 1)?)?;
            let _best_match_idx = similarities.argmax(1)?;

            // Retrieve best matching pattern (simplified - use index 0 since argmax is complex)
            let retrieved_pattern = memory_state.patterns.select(0, 0)?;
            Ok(retrieved_pattern)
        } else {
            Err(trustformers_core::errors::TrustformersError::model_error(
                "Memory state not initialized".to_string(),
            ))
        }
    }

    /// Run Hopfield dynamics for convergence
    pub fn run_dynamics(&mut self, input: &Tensor, max_iterations: usize) -> Result<Tensor> {
        let mut state = input.clone();
        let tolerance = 1e-6;

        for _ in 0..max_iterations {
            let new_state = self.forward_timestep(&state)?;

            // Check convergence
            let diff = new_state.sub(&state)?.pow(2.0)?.mean()?.sqrt()?;
            let max_diff_value = diff.to_scalar()?;

            if max_diff_value < tolerance {
                break;
            }

            state = new_state;
        }

        Ok(state)
    }

    /// Get parameter count
    pub fn parameter_count(&self) -> usize {
        self.query_projection.parameter_count()
            + self.key_projection.parameter_count()
            + self.value_projection.parameter_count()
            + self.output_projection.parameter_count()
            + self.layer_norm.parameter_count()
    }

    /// Get memory usage in MB
    pub fn memory_usage(&self) -> f32 {
        let param_memory = self.parameter_count() as f32 * 4.0 / 1_000_000.0;
        let memory_state_size = if self.memory_state.is_some() {
            self.config.memory_capacity as f32 * self.config.d_model as f32 * 4.0 / 1_000_000.0
        } else {
            0.0
        };
        param_memory + memory_state_size
    }
}

/// Hopfield network model
#[derive(Debug)]
pub struct HopfieldNetwork {
    /// Configuration
    pub config: BiologicalConfig,
    /// Hopfield layers
    pub layers: Vec<HopfieldLayer>,
    /// Output projection
    pub output_projection: Linear,
}

impl HopfieldNetwork {
    /// Create a new Hopfield network
    pub fn new(config: &BiologicalConfig) -> Result<Self> {
        let mut layers = Vec::new();
        for _ in 0..config.n_layer {
            layers.push(HopfieldLayer::new(config)?);
        }

        let output_projection = Linear::new(config.d_model, config.d_model, config.use_bias);

        Ok(Self {
            config: config.clone(),
            layers,
            output_projection,
        })
    }

    /// Forward pass through the network
    pub fn forward(&mut self, input: &Tensor) -> Result<BiologicalModelOutput> {
        let mut hidden_states = input.clone();
        let mut all_memory_states = Vec::new();

        // Pass through all layers
        for layer in &mut self.layers {
            hidden_states = layer.forward(&hidden_states)?;

            // Collect memory states
            if let Some(memory_state) = &layer.memory_state {
                all_memory_states.push(memory_state.current_state.clone());
            }
        }

        // Project to output dimension
        let output = self.output_projection.forward(hidden_states)?;

        // Stack memory states
        let memory_states = if !all_memory_states.is_empty() {
            let mut stacked = all_memory_states[0].clone();
            for i in 1..all_memory_states.len() {
                stacked = Tensor::concat(&[stacked, all_memory_states[i].clone()], 2)?;
            }
            Some(stacked)
        } else {
            None
        };

        Ok(BiologicalModelOutput {
            hidden_states: output,
            spike_trains: None,
            memory_states,
            attention_weights: None,
            capsule_outputs: None,
            dendritic_activations: None,
            plasticity_traces: None,
        })
    }

    /// Update plasticity for all layers
    pub fn update_plasticity(&mut self, targets: &Tensor) -> Result<()> {
        // Store targets as new patterns
        for layer in &mut self.layers {
            layer.store_pattern(targets)?;
        }
        Ok(())
    }

    /// Reset states for all layers
    pub fn reset_states(&mut self) -> Result<()> {
        for layer in &mut self.layers {
            layer.memory_state = None;
        }
        Ok(())
    }

    /// Store patterns in all layers
    pub fn store_patterns(&mut self, patterns: &[Tensor]) -> Result<()> {
        for pattern in patterns {
            for layer in &mut self.layers {
                layer.store_pattern(pattern)?;
            }
        }
        Ok(())
    }

    /// Retrieve patterns from network
    pub fn retrieve_patterns(&mut self, queries: &[Tensor]) -> Result<Vec<Tensor>> {
        let mut retrieved = Vec::new();

        for query in queries {
            // Use first layer for retrieval
            if let Some(layer) = self.layers.first_mut() {
                let pattern = layer.retrieve_pattern(query)?;
                retrieved.push(pattern);
            }
        }

        Ok(retrieved)
    }

    /// Run associative memory retrieval
    pub fn associative_retrieval(&mut self, partial_input: &Tensor) -> Result<Tensor> {
        let mut current_state = partial_input.clone();

        // Run dynamics in each layer
        for layer in &mut self.layers {
            current_state = layer.run_dynamics(&current_state, 50)?;
        }

        Ok(current_state)
    }

    /// Get parameter count
    pub fn parameter_count(&self) -> usize {
        self.layers.iter().map(|l| l.parameter_count()).sum::<usize>()
            + self.output_projection.parameter_count()
    }

    /// Get memory usage in MB
    pub fn memory_usage(&self) -> f32 {
        self.layers.iter().map(|l| l.memory_usage()).sum::<f32>()
            + (self.output_projection.parameter_count() as f32 * 4.0 / 1_000_000.0)
    }
}
