//! Quantum algorithm implementations for clustering
//!
//! This module provides implementations of quantum algorithms like QAOA and VQE
//! specifically adapted for clustering problems.

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use scirs2_core::numeric::{Float, FromPrimitive};
use serde::{Deserialize, Serialize};
use std::fmt::Debug;

use crate::error::{ClusteringError, Result};

/// Configuration for QAOA clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QAOAConfig {
    /// Number of QAOA layers (p)
    pub p_layers: usize,
    /// Number of optimization iterations
    pub optimization_iterations: usize,
    /// Optimizer for classical parameters
    pub optimizer: String,
    /// Learning rate for parameter optimization
    pub learning_rate: f64,
    /// Cost function for clustering
    pub cost_function: QAOACostFunction,
    /// Number of measurement shots
    pub n_shots: usize,
    /// Enable noise simulation
    pub enable_noise: bool,
}

impl Default for QAOAConfig {
    fn default() -> Self {
        Self {
            p_layers: 1,
            optimization_iterations: 100,
            optimizer: "COBYLA".to_string(),
            learning_rate: 0.01,
            cost_function: QAOACostFunction::MaxCut,
            n_shots: 1024,
            enable_noise: false,
        }
    }
}

/// Cost functions for QAOA clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QAOACostFunction {
    /// Maximum cut formulation
    MaxCut,
    /// Minimum cut formulation
    MinCut,
    /// Graph coloring formulation
    GraphColoring { n_colors: usize },
    /// Custom Hamiltonian
    Custom { hamiltonian_params: Vec<f64> },
}

/// QAOA-based clustering algorithm
pub struct QAOAClustering<F: Float + FromPrimitive> {
    config: QAOAConfig,
    optimal_parameters: Option<(Vec<f64>, Vec<f64>)>, // (gamma, beta)
    cluster_assignments: Option<Array1<usize>>,
    initialized: bool,
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float + FromPrimitive + Debug> QAOAClustering<F> {
    /// Create a new QAOA clustering instance
    pub fn new(config: QAOAConfig) -> Self {
        Self {
            config,
            optimal_parameters: None,
            cluster_assignments: None,
            initialized: false,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Fit the QAOA clustering model
    pub fn fit(&mut self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        // Placeholder implementation - would contain full QAOA algorithm
        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % 2);
        self.cluster_assignments = Some(labels.clone());
        self.initialized = true;
        Ok(labels)
    }

    /// Predict cluster assignments
    pub fn predict(&self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        if !self.initialized {
            return Err(ClusteringError::InvalidInput(
                "Model must be fitted before prediction".to_string(),
            ));
        }

        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % 2);
        Ok(labels)
    }

    /// Get optimal QAOA parameters
    pub fn optimal_parameters(&self) -> Option<&(Vec<f64>, Vec<f64>)> {
        self.optimal_parameters.as_ref()
    }
}

/// Configuration for VQE clustering
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VQEConfig {
    /// Maximum iterations for VQE optimization
    pub max_iterations: usize,
    /// Convergence tolerance
    pub tolerance: f64,
    /// Optimizer for classical parameters
    pub optimizer: String,
    /// Ansatz circuit type
    pub ansatz: VQEAnsatz,
    /// Number of measurement shots
    pub n_shots: usize,
}

impl Default for VQEConfig {
    fn default() -> Self {
        Self {
            max_iterations: 200,
            tolerance: 1e-6,
            optimizer: "SLSQP".to_string(),
            ansatz: VQEAnsatz::RealAmplitudes { num_layers: 2 },
            n_shots: 1024,
        }
    }
}

/// Ansatz types for VQE
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum VQEAnsatz {
    /// Real amplitudes ansatz
    RealAmplitudes { num_layers: usize },
    /// Efficient SU(2) ansatz
    EfficientSU2 { num_layers: usize },
    /// Two-local ansatz
    TwoLocal {
        rotation_blocks: Vec<String>,
        entanglement_blocks: Vec<String>,
    },
    /// Custom ansatz
    Custom { gates: Vec<String> },
}

/// VQE-based clustering algorithm
pub struct VQEClustering<F: Float + FromPrimitive> {
    config: VQEConfig,
    optimal_energy: Option<f64>,
    optimal_parameters: Option<Vec<f64>>,
    cluster_assignments: Option<Array1<usize>>,
    initialized: bool,
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float + FromPrimitive + Debug> VQEClustering<F> {
    /// Create a new VQE clustering instance
    pub fn new(config: VQEConfig) -> Self {
        Self {
            config,
            optimal_energy: None,
            optimal_parameters: None,
            cluster_assignments: None,
            initialized: false,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Fit the VQE clustering model
    pub fn fit(&mut self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        // Placeholder implementation - would contain full VQE algorithm
        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % 2);
        self.cluster_assignments = Some(labels.clone());
        self.initialized = true;
        Ok(labels)
    }

    /// Predict cluster assignments
    pub fn predict(&self, data: ArrayView2<F>) -> Result<Array1<usize>> {
        if !self.initialized {
            return Err(ClusteringError::InvalidInput(
                "Model must be fitted before prediction".to_string(),
            ));
        }

        let n_samples = data.nrows();
        let labels = Array1::from_shape_fn(n_samples, |i| i % 2);
        Ok(labels)
    }

    /// Get optimal VQE energy
    pub fn optimal_energy(&self) -> Option<f64> {
        self.optimal_energy
    }

    /// Get optimal VQE parameters
    pub fn optimal_parameters(&self) -> Option<&Vec<f64>> {
        self.optimal_parameters.as_ref()
    }
}

/// Convenience function for QAOA clustering
pub fn qaoa_clustering<F: Float + FromPrimitive + Debug + 'static>(
    data: ArrayView2<F>,
    config: Option<QAOAConfig>,
) -> Result<Array1<usize>> {
    let config = config.unwrap_or_default();
    let mut clusterer = QAOAClustering::new(config);
    clusterer.fit(data)
}

/// Convenience function for VQE clustering
pub fn vqe_clustering<F: Float + FromPrimitive + Debug + 'static>(
    data: ArrayView2<F>,
    config: Option<VQEConfig>,
) -> Result<Array1<usize>> {
    let config = config.unwrap_or_default();
    let mut clusterer = VQEClustering::new(config);
    clusterer.fit(data)
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_qaoa_config_default() {
        let config = QAOAConfig::default();
        assert_eq!(config.p_layers, 1);
        assert_eq!(config.optimization_iterations, 100);
    }

    #[test]
    fn test_vqe_config_default() {
        let config = VQEConfig::default();
        assert_eq!(config.max_iterations, 200);
        assert!((config.tolerance - 1e-6).abs() < 1e-10);
    }

    #[test]
    fn test_qaoa_clustering_placeholder() {
        let data = Array2::from_shape_vec((4, 2), (0..8).map(|x| x as f64).collect())
            .expect("Operation failed");
        let result = qaoa_clustering(data.view(), None);
        assert!(result.is_ok());
    }

    #[test]
    fn test_vqe_clustering_placeholder() {
        let data = Array2::from_shape_vec((4, 2), (0..8).map(|x| x as f64).collect())
            .expect("Operation failed");
        let result = vqe_clustering(data.view(), None);
        assert!(result.is_ok());
    }
}
