//! Hybrid quantum-classical neural network layers

#![allow(unused_variables)] // Hybrid quantum layers

use crate::quantum::quantum_circuit::*;
use crate::quantum::quantum_ops::*;
use crate::tensor::Tensor;
use anyhow::Result;

/// Hybrid quantum-classical neural network layer
#[derive(Debug, Clone)]
pub struct QuantumNeuralLayer {
    pub input_qubits: usize,
    pub ansatz: QuantumAnsatz,
    pub parameters: Vec<f64>,
    pub measurement_basis: MeasurementBasis,
}

/// Quantum embedding layer for classical data
#[derive(Debug, Clone)]
pub struct QuantumEmbeddingLayer {
    pub classical_dim: usize,
    pub quantum_dim: usize,
    pub encoding: QuantumEncoding,
    pub weights: Vec<f64>,
}

impl QuantumNeuralLayer {
    /// Create a new quantum neural layer
    pub fn new(input_qubits: usize, ansatz: QuantumAnsatz, parameters: &[f64]) -> Result<Self> {
        if parameters.is_empty() {
            return Err(anyhow::anyhow!("Parameters cannot be empty"));
        }

        Ok(Self {
            input_qubits,
            ansatz,
            parameters: parameters.to_vec(),
            measurement_basis: MeasurementBasis::Computational,
        })
    }

    /// Forward pass through the quantum layer
    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        // Convert input tensor to quantum encoding
        let input_data = input.data()?;
        let qubits: Vec<usize> = (0..self.input_qubits).collect();

        // Build quantum circuit
        let circuit = self.ansatz.build_circuit(&qubits, &self.parameters)?;

        // Prepare initial state (simplified)
        let initial_state = QuantumState::zero_state(self.input_qubits);

        // Execute circuit
        let final_state = circuit.execute(&initial_state)?;

        // Measure and convert back to classical tensor
        let measurements = self.measure_state(&final_state)?;

        // Convert measurements to output tensor
        self.measurements_to_tensor(&measurements)
    }

    /// Backward pass (gradient computation)
    pub fn backward(&self, input: &Tensor, grad_output: &Tensor) -> Result<(Tensor, Vec<f64>)> {
        // Simplified gradient computation using parameter shift rule
        let mut param_gradients = vec![0.0; self.parameters.len()];

        for (i, _) in self.parameters.iter().enumerate() {
            let gradient = self.compute_parameter_gradient(i, grad_output)?;
            param_gradients[i] = gradient;
        }

        // Enhanced input gradient computation using parameter-shift rule
        let mut input_grad_data = vec![0.0f32; grad_output.shape()[0]];

        for i in 0..input_grad_data.len() {
            let mut gradient_sum = 0.0;

            // Use parameter-shift rule for input gradients
            let shift = std::f64::consts::PI / 2.0;

            // Create shifted input
            let mut input_plus = input.data().unwrap();
            let mut input_minus = input.data().unwrap();

            if i < input_plus.len() {
                input_plus[i] += shift as f32;
                input_minus[i] -= shift as f32;
            }

            // Forward pass with shifted inputs
            let input_plus_tensor = Tensor::from_vec(input_plus, &input.shape())?;
            let input_minus_tensor = Tensor::from_vec(input_minus, &input.shape())?;

            if let (Ok(output_plus), Ok(output_minus)) = (
                self.forward(&input_plus_tensor),
                self.forward(&input_minus_tensor),
            ) {
                // Compute gradient using finite differences
                let diff_data = output_plus.data().unwrap();
                let minus_data = output_minus.data().unwrap();

                let grad_output_data = grad_output.data().unwrap();
                for (j, (&plus_val, &minus_val)) in
                    diff_data.iter().zip(minus_data.iter()).enumerate()
                {
                    if j < grad_output_data.len() {
                        gradient_sum += ((plus_val - minus_val) / (2.0 * shift as f32)) as f64
                            * grad_output_data[j] as f64;
                    }
                }
            }

            input_grad_data[i] = gradient_sum as f32;
        }

        let input_grad = Tensor::from_vec(input_grad_data, &input.shape())?;

        Ok((input_grad, param_gradients))
    }

    /// Update parameters
    pub fn update_parameters(&mut self, gradients: &[f64], learning_rate: f64) {
        for (param, &grad) in self.parameters.iter_mut().zip(gradients) {
            *param -= learning_rate * grad;
        }
    }

    fn measure_state(&self, state: &QuantumState) -> Result<Vec<f64>> {
        // Simplified measurement - return probabilities
        let mut measurements = Vec::new();

        for i in 0..state.amplitudes.len() {
            measurements.push(state.probability(i));
        }

        Ok(measurements)
    }

    fn measurements_to_tensor(&self, measurements: &[f64]) -> Result<Tensor> {
        // Convert measurement probabilities to tensor
        let f32_data: Vec<f32> = measurements.iter().map(|&x| x as f32).collect();
        Ok(Tensor::from_vec(f32_data, &[measurements.len()])?)
    }

    fn compute_parameter_gradient(&self, param_index: usize, grad_output: &Tensor) -> Result<f64> {
        // Parameter shift rule: gradient = (f(θ + π/2) - f(θ - π/2)) / 2
        let shift = std::f64::consts::PI / 2.0;

        // Forward with positive shift
        let mut params_plus = self.parameters.clone();
        params_plus[param_index] += shift;
        let layer_plus = QuantumNeuralLayer {
            parameters: params_plus,
            ..self.clone()
        };

        // Forward with negative shift
        let mut params_minus = self.parameters.clone();
        params_minus[param_index] -= shift;
        let layer_minus = QuantumNeuralLayer {
            parameters: params_minus,
            ..self.clone()
        };

        // Dummy input for gradient computation
        let dummy_input = Tensor::zeros(&[1])?;

        let output_plus = layer_plus.forward(&dummy_input)?;
        let output_minus = layer_minus.forward(&dummy_input)?;

        // Simplified gradient calculation
        let grad_data_plus = output_plus.data()?;
        let grad_data_minus = output_minus.data()?;
        let grad_output_data = grad_output.data()?;

        let mut gradient = 0.0f64;
        for (i, (&plus, &minus)) in grad_data_plus.iter().zip(&grad_data_minus).enumerate() {
            if i < grad_output_data.len() {
                gradient += grad_output_data[i] as f64 * (plus as f64 - minus as f64) / 2.0;
            }
        }

        Ok(gradient)
    }
}

impl QuantumEmbeddingLayer {
    /// Create a new quantum embedding layer
    pub fn new(
        classical_dim: usize,
        quantum_dim: usize,
        encoding: QuantumEncoding,
    ) -> Result<Self> {
        if quantum_dim == 0 {
            return Err(anyhow::anyhow!("Quantum dimension must be positive"));
        }

        let num_qubits = (quantum_dim as f64).log2().ceil() as usize;
        let required_qubits = 2_usize.pow(num_qubits as u32);

        if required_qubits < quantum_dim {
            return Err(anyhow::anyhow!(
                "Quantum dimension {} requires {} qubits",
                quantum_dim,
                num_qubits + 1
            ));
        }

        let weights = vec![0.1; classical_dim]; // Initialize with small values

        Ok(Self {
            classical_dim,
            quantum_dim,
            encoding,
            weights,
        })
    }

    /// Embed classical data into quantum state
    pub fn embed(&self, input: &Tensor) -> Result<QuantumState> {
        let input_data = input.data()?;

        if input_data.len() != self.classical_dim {
            return Err(anyhow::anyhow!(
                "Input dimension {} does not match expected {}",
                input_data.len(),
                self.classical_dim
            ));
        }

        // Apply learned weights
        let weighted_input: Vec<f64> =
            input_data.iter().zip(&self.weights).map(|(&x, &w)| x as f64 * w).collect();

        // Determine number of qubits needed
        let num_qubits = (self.quantum_dim as f64).log2().ceil() as usize;

        // Create encoding circuit
        let circuit = self.encoding.encode(&weighted_input, num_qubits)?;

        // Execute on zero state
        let initial_state = QuantumState::zero_state(num_qubits);
        let embedded_state = circuit.execute(&initial_state)?;

        Ok(embedded_state)
    }

    /// Forward pass
    pub fn forward(&self, input: &Tensor) -> Result<Tensor> {
        let embedded_state = self.embed(input)?;

        // Convert quantum state back to classical tensor
        let probabilities: Vec<f32> = (0..embedded_state.amplitudes.len())
            .map(|i| embedded_state.probability(i) as f32)
            .take(self.quantum_dim)
            .collect();

        Ok(Tensor::from_vec(probabilities, &[self.quantum_dim])?)
    }

    /// Update weights
    pub fn update_weights(&mut self, gradients: &[f64], learning_rate: f64) {
        for (weight, &grad) in self.weights.iter_mut().zip(gradients) {
            *weight -= learning_rate * grad;
        }
    }
}

/// Quantum attention mechanism
#[derive(Debug, Clone)]
pub struct QuantumAttention {
    pub num_qubits: usize,
    pub query_embedding: QuantumEmbeddingLayer,
    pub key_embedding: QuantumEmbeddingLayer,
    pub value_embedding: QuantumEmbeddingLayer,
}

impl QuantumAttention {
    /// Create quantum attention layer
    pub fn new(input_dim: usize, num_qubits: usize) -> Result<Self> {
        let quantum_dim = 2_usize.pow(num_qubits as u32);

        let query_embedding =
            QuantumEmbeddingLayer::new(input_dim, quantum_dim, QuantumEncoding::Angle)?;

        let key_embedding =
            QuantumEmbeddingLayer::new(input_dim, quantum_dim, QuantumEncoding::Angle)?;

        let value_embedding =
            QuantumEmbeddingLayer::new(input_dim, quantum_dim, QuantumEncoding::Amplitude)?;

        Ok(Self {
            num_qubits,
            query_embedding,
            key_embedding,
            value_embedding,
        })
    }

    /// Compute quantum attention
    pub fn forward(&self, query: &Tensor, key: &Tensor, value: &Tensor) -> Result<Tensor> {
        // Embed inputs into quantum states
        let query_state = self.query_embedding.embed(query)?;
        let key_state = self.key_embedding.embed(key)?;
        let value_state = self.value_embedding.embed(value)?;

        // Compute attention using quantum fidelity
        let attention_weight = query_state.fidelity(&key_state);

        // Apply attention to value
        let value_output = self.value_embedding.forward(value)?;
        let value_data = value_output.data()?;

        let attended_data: Vec<f32> =
            value_data.iter().map(|&x| x * attention_weight as f32).collect();

        Ok(Tensor::from_vec(attended_data, &value_output.shape())?)
    }
}

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

    #[test]
    fn test_quantum_neural_layer() {
        let ansatz = QuantumAnsatz::Hardware;
        let parameters = vec![0.1, 0.2, 0.3];

        let layer = QuantumNeuralLayer::new(3, ansatz, &parameters);
        assert!(layer.is_ok());

        let layer = layer.unwrap();
        assert_eq!(layer.input_qubits, 3);
        assert_eq!(layer.parameters.len(), 3);
    }

    #[test]
    fn test_quantum_embedding_layer() {
        let encoding = QuantumEncoding::Angle;
        let layer = QuantumEmbeddingLayer::new(4, 4, encoding);
        assert!(layer.is_ok());

        let layer = layer.unwrap();
        assert_eq!(layer.classical_dim, 4);
        assert_eq!(layer.quantum_dim, 4);
        assert_eq!(layer.weights.len(), 4);
    }

    #[test]
    fn test_quantum_embedding_forward() {
        let encoding = QuantumEncoding::Angle;
        let layer = QuantumEmbeddingLayer::new(2, 4, encoding).unwrap();

        let input = Tensor::from_vec(vec![0.5, 1.0], &[2]).unwrap();
        let output = layer.forward(&input);
        assert!(output.is_ok());

        let output = output.unwrap();
        assert_eq!(output.shape(), &[4]);
    }

    #[test]
    fn test_quantum_attention() {
        let attention = QuantumAttention::new(3, 2);
        assert!(attention.is_ok());

        let attention = attention.unwrap();
        assert_eq!(attention.num_qubits, 2);
    }

    #[test]
    fn test_quantum_neural_layer_forward() {
        let ansatz = QuantumAnsatz::Hardware;
        let parameters = vec![0.1, 0.2];
        let layer = QuantumNeuralLayer::new(2, ansatz, &parameters).unwrap();

        let input = Tensor::from_vec(vec![0.5, 1.0], &[2]).unwrap();
        let output = layer.forward(&input);
        assert!(output.is_ok());
    }

    #[test]
    fn test_parameter_updates() {
        let ansatz = QuantumAnsatz::Hardware;
        let parameters = vec![0.1, 0.2, 0.3];
        let mut layer = QuantumNeuralLayer::new(3, ansatz, &parameters).unwrap();

        let gradients = vec![0.01, 0.02, 0.03];
        let learning_rate = 0.1;

        let original_params = layer.parameters.clone();
        layer.update_parameters(&gradients, learning_rate);

        for (i, (&new, &old)) in layer.parameters.iter().zip(&original_params).enumerate() {
            let expected = old - learning_rate * gradients[i];
            assert!((new - expected).abs() < 1e-10);
        }
    }

    #[test]
    fn test_embedding_weights_update() {
        let encoding = QuantumEncoding::Angle;
        let mut layer = QuantumEmbeddingLayer::new(3, 4, encoding).unwrap();

        let gradients = vec![0.01, 0.02, 0.03];
        let learning_rate = 0.1;

        let original_weights = layer.weights.clone();
        layer.update_weights(&gradients, learning_rate);

        for (i, (&new, &old)) in layer.weights.iter().zip(&original_weights).enumerate() {
            let expected = old - learning_rate * gradients[i];
            assert!((new - expected).abs() < 1e-10);
        }
    }
}
