//! Quantum-inspired I/O processing algorithms with advanced capabilities
//!
//! This module implements quantum-inspired algorithms for I/O optimization,
//! leveraging quantum computing principles like superposition, entanglement,
//! and quantum annealing for advanced-high performance data processing.

#![allow(dead_code)]
#![allow(clippy::too_many_arguments)]

use crate::error::{IoError, Result};
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::simd_ops::SimdUnifiedOps;
use serde::{Deserialize, Serialize};
use std::f32::consts::PI;
use std::sync::{Arc, RwLock};

/// Quantum error correction implementation
#[derive(Debug, Clone)]
pub struct QuantumErrorCorrection {
    /// Error correction code type
    pub code_type: String,
    /// Syndrome measurement results
    pub syndromes: Vec<bool>,
    /// Error threshold
    pub threshold: f32,
}

impl Default for QuantumErrorCorrection {
    fn default() -> Self {
        Self {
            code_type: "stabilizer".to_string(),
            syndromes: Vec::new(),
            threshold: 0.001,
        }
    }
}

/// Quantum gate operations
#[derive(Debug, Clone)]
pub enum QuantumGate {
    /// Pauli X gate (bit flip)
    PauliX(usize),
    /// Pauli Y gate
    PauliY(usize),
    /// Pauli Z gate (phase flip)
    PauliZ(usize),
    /// Hadamard gate (superposition)
    Hadamard(usize),
    /// CNOT gate (controlled NOT)
    CNOT(usize, usize),
    /// Phase gate
    Phase(usize, f32),
    /// Rotation gates
    RotationX(usize, f32),
    /// Rotation around Y-axis
    RotationY(usize, f32),
    /// Rotation around Z-axis
    RotationZ(usize, f32),
}

/// Quantum state representation for I/O optimization
#[derive(Debug, Clone)]
pub struct QuantumState {
    /// Amplitude vector representing quantum superposition
    amplitudes: Array1<f32>,
    /// Phase vector for quantum interference
    phases: Array1<f32>,
    /// Entanglement matrix for correlated operations
    entanglement: Array2<f32>,
    /// Quantum error correction codes
    error_correction: QuantumErrorCorrection,
    /// Decoherence noise model
    decoherence_rate: f32,
    /// Quantum gate history for reversibility
    gate_history: Vec<QuantumGate>,
}

impl QuantumState {
    /// Create a new quantum state with given dimensions
    pub fn new(dimensions: usize) -> Self {
        let mut amplitudes = Array1::zeros(dimensions);
        amplitudes[0] = 1.0; // Start in |0⟩ state

        Self {
            amplitudes,
            phases: Array1::zeros(dimensions),
            entanglement: Array2::eye(dimensions),
            error_correction: QuantumErrorCorrection::default(),
            decoherence_rate: 0.001,
            gate_history: Vec::new(),
        }
    }

    /// Apply quantum superposition to create multiple processing paths
    pub fn superposition(&mut self, weights: &[f32]) -> Result<()> {
        if weights.len() != self.amplitudes.len() {
            return Err(IoError::ValidationError(
                "Weight dimension mismatch".to_string(),
            ));
        }

        // Normalize weights to maintain quantum state normalization
        let weight_sum: f32 = weights.iter().map(|w| w * w).sum();
        let norm_factor = weight_sum.sqrt();

        if norm_factor > 0.0 {
            for (i, &weight) in weights.iter().enumerate() {
                self.amplitudes[i] = weight / norm_factor;
            }
        }

        Ok(())
    }

    /// Measure quantum state to collapse into classical result
    pub fn measure(&self) -> usize {
        let probabilities: Vec<f32> = self.amplitudes.iter().map(|&a| a * a).collect();

        // Quantum measurement simulation using cumulative probability
        let mut cumulative = 0.0;
        let random_value = self.pseudo_random(); // Deterministic for reproducibility

        for (i, &prob) in probabilities.iter().enumerate() {
            cumulative += prob;
            if random_value <= cumulative {
                return i;
            }
        }

        probabilities.len() - 1
    }

    /// Generate pseudo-random number for measurement
    fn pseudo_random(&self) -> f32 {
        // Simple pseudo-random based on state hash
        let state_hash = self.amplitudes.iter().fold(0u32, |acc, &x| {
            acc.wrapping_mul(31).wrapping_add((x * 1000000.0) as u32)
        });
        ((state_hash % 10000) as f32) / 10000.0
    }

    /// Apply quantum evolution using Hamiltonian
    pub fn evolve(&mut self, timestep: f32) -> Result<()> {
        let hamiltonian = self.create_hamiltonian();

        // Apply time evolution operator: |ψ(t)⟩ = exp(-iHt)|ψ(0)⟩
        for i in 0..self.amplitudes.len() {
            let energy = hamiltonian[[i, i]];
            self.phases[i] += energy * timestep;

            // Apply phase to amplitude
            let phase_factor = (-energy * timestep).cos() + (-energy * timestep).sin();
            self.amplitudes[i] *= phase_factor;
        }

        // Normalize state
        self.normalize()?;
        Ok(())
    }

    /// Create Hamiltonian matrix for system evolution
    fn create_hamiltonian(&self) -> Array2<f32> {
        let dim = self.amplitudes.len();
        let mut hamiltonian = Array2::zeros((dim, dim));

        // Create a simple Hamiltonian with nearest-neighbor interactions
        for i in 0..dim {
            hamiltonian[[i, i]] = 1.0; // On-site energy
            if i > 0 {
                hamiltonian[[i, i - 1]] = 0.5; // Hopping term
                hamiltonian[[i - 1, i]] = 0.5; // Hermitian conjugate
            }
        }

        hamiltonian
    }

    /// Normalize quantum state
    fn normalize(&mut self) -> Result<()> {
        let norm: f32 = self.amplitudes.iter().map(|&a| a * a).sum::<f32>().sqrt();
        if norm > 0.0 {
            self.amplitudes /= norm;
        }
        Ok(())
    }
}

/// Quantum annealing optimizer for I/O parameter optimization
#[derive(Debug)]
pub struct QuantumAnnealingOptimizer {
    /// Problem Hamiltonian (cost function)
    problem_hamiltonian: Array2<f32>,
    /// Mixing Hamiltonian (for quantum tunneling)
    mixing_hamiltonian: Array2<f32>,
    /// Current temperature (annealing parameter)
    temperature: f32,
    /// Annealing schedule
    annealing_schedule: Vec<f32>,
    /// Current annealing step
    current_step: usize,
}

impl QuantumAnnealingOptimizer {
    /// Create a new quantum annealing optimizer
    pub fn new(problem_size: usize) -> Self {
        let problem_hamiltonian = Self::create_problem_hamiltonian(problem_size);
        let mixing_hamiltonian = Self::create_mixing_hamiltonian(problem_size);
        let annealing_schedule = Self::create_annealing_schedule(100);

        Self {
            problem_hamiltonian,
            mixing_hamiltonian,
            temperature: annealing_schedule[0],
            annealing_schedule,
            current_step: 0,
        }
    }

    /// Optimize I/O parameters using quantum annealing
    pub fn optimize(&mut self, initialparams: &[f32]) -> Result<Vec<f32>> {
        let mut current_state = QuantumState::new(initialparams.len());
        current_state.superposition(initialparams)?;

        // Perform annealing steps
        for &temperature in &self.annealing_schedule.clone() {
            self.temperature = temperature;
            self.annealing_step(&mut current_state)?;
        }

        // Measure final state to get optimized parameters
        let optimal_index = current_state.measure();
        Ok(self.extract_parameters(optimal_index))
    }

    /// Perform one annealing step
    fn annealing_step(&self, state: &mut QuantumState) -> Result<()> {
        let time_step = 0.01;

        // Create combined Hamiltonian: H = A(t)H_mixing + B(t)H_problem
        let mixing_weight = self.temperature;
        let problem_weight = 1.0 - self.temperature;

        let _combined_hamiltonian =
            &self.mixing_hamiltonian * mixing_weight + &self.problem_hamiltonian * problem_weight;

        // Evolve state under combined Hamiltonian
        state.evolve(time_step)?;

        Ok(())
    }

    /// Create problem Hamiltonian encoding the optimization problem
    fn create_problem_hamiltonian(size: usize) -> Array2<f32> {
        let mut hamiltonian = Array2::zeros((size, size));

        // Encode I/O optimization problem
        // Diagonal terms represent parameter costs
        for i in 0..size {
            hamiltonian[[i, i]] = (i as f32 / size as f32 - 0.5).powi(2);
        }

        // Off-diagonal terms represent parameter interactions
        for i in 0..size {
            for j in i + 1..size {
                let interaction = 0.1 * ((i as f32 - j as f32) / size as f32).cos();
                hamiltonian[[i, j]] = interaction;
                hamiltonian[[j, i]] = interaction;
            }
        }

        hamiltonian
    }

    /// Create mixing Hamiltonian for quantum tunneling
    fn create_mixing_hamiltonian(size: usize) -> Array2<f32> {
        let mut hamiltonian = Array2::zeros((size, size));

        // Create transverse field (quantum tunneling)
        for i in 0..size {
            if i > 0 {
                hamiltonian[[i, i - 1]] = 1.0;
            }
            if i < size - 1 {
                hamiltonian[[i, i + 1]] = 1.0;
            }
        }

        hamiltonian
    }

    /// Create annealing schedule
    fn create_annealing_schedule(steps: usize) -> Vec<f32> {
        (0..steps)
            .map(|i| 1.0 - (i as f32 / steps as f32))
            .collect()
    }

    /// Extract optimized parameters from quantum state index
    fn extract_parameters(&self, index: usize) -> Vec<f32> {
        let size = self.problem_hamiltonian.nrows();
        let mut params = Vec::with_capacity(size);

        // Convert index to parameter values
        for i in 0..size {
            let param_value = if i == index {
                1.0
            } else {
                (i as f32) / (size as f32)
            };
            params.push(param_value);
        }

        params
    }
}

/// Quantum-inspired I/O processing parameters
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumIoParams {
    /// Superposition factor for parallel processing paths
    pub superposition_factor: f32,
    /// Entanglement strength for correlated operations
    pub entanglement_strength: f32,
    /// Quantum interference threshold
    pub interference_threshold: f32,
    /// Measurement probability threshold
    pub measurement_threshold: f32,
    /// Coherence time for quantum operations
    pub coherence_time: f32,
}

impl Default for QuantumIoParams {
    fn default() -> Self {
        Self {
            superposition_factor: 0.7,
            entanglement_strength: 0.5,
            interference_threshold: 0.3,
            measurement_threshold: 0.8,
            coherence_time: 1.0,
        }
    }
}

/// Quantum-inspired parallel I/O processor
pub struct QuantumParallelProcessor {
    /// Quantum state for processing decisions
    quantum_state: Arc<RwLock<QuantumState>>,
    /// Quantum annealing optimizer
    optimizer: Arc<RwLock<QuantumAnnealingOptimizer>>,
    /// Processing parameters
    params: QuantumIoParams,
    /// Performance history for adaptive optimization
    performance_history: Arc<RwLock<Vec<f32>>>,
}

impl QuantumParallelProcessor {
    /// Create a new quantum parallel processor
    pub fn new(processing_dimensions: usize) -> Self {
        Self {
            quantum_state: Arc::new(RwLock::new(QuantumState::new(processing_dimensions))),
            optimizer: Arc::new(RwLock::new(QuantumAnnealingOptimizer::new(
                processing_dimensions,
            ))),
            params: QuantumIoParams::default(),
            performance_history: Arc::new(RwLock::new(Vec::new())),
        }
    }

    /// Process data using quantum-inspired parallel algorithms
    pub fn process_quantum_parallel(&mut self, data: &[u8]) -> Result<Vec<u8>> {
        // Create quantum superposition of processing strategies
        let processing_weights = self.determine_processing_weights(data)?;

        {
            let mut state = self.quantum_state.write().expect("Operation failed");
            state.superposition(&processing_weights)?;
        }

        // Apply quantum evolution for optimization
        {
            let mut state = self.quantum_state.write().expect("Operation failed");
            state.evolve(self.params.coherence_time)?;
        }

        // Measure quantum state to select processing strategy
        let selected_strategy = {
            let state = self.quantum_state.read().expect("Operation failed");
            state.measure()
        };

        // Execute selected processing strategy
        let result = self.execute_processing_strategy(data, selected_strategy)?;

        // Record performance for future optimization
        self.record_performance(result.len() as f32 / data.len() as f32);

        Ok(result)
    }

    /// Determine processing weights based on data characteristics
    fn determine_processing_weights(&self, data: &[u8]) -> Result<Vec<f32>> {
        let entropy = self.calculate_entropy(data);
        let compression_ratio = self.estimate_compression_ratio(data);
        let data_size_factor = (data.len() as f32).log2() / 20.0; // Normalize by typical size

        // Create weights based on data characteristics
        let weights = vec![
            entropy * self.params.superposition_factor,
            compression_ratio * 0.8,
            data_size_factor * 0.6,
            (1.0 - entropy) * 0.7, // Complement entropy
            self.params.entanglement_strength,
        ];

        Ok(weights)
    }

    /// Calculate Shannon entropy of data
    fn calculate_entropy(&self, data: &[u8]) -> f32 {
        let mut frequency = [0u32; 256];
        for &byte in data {
            frequency[byte as usize] += 1;
        }

        let len = data.len() as f32;
        let mut entropy = 0.0;

        for &freq in &frequency {
            if freq > 0 {
                let p = freq as f32 / len;
                entropy -= p * p.log2();
            }
        }

        entropy / 8.0 // Normalize to [0, 1]
    }

    /// Estimate compression ratio
    fn estimate_compression_ratio(&self, data: &[u8]) -> f32 {
        // Simple estimation based on byte repetition
        let unique_bytes: std::collections::HashSet<u8> = data.iter().cloned().collect();
        unique_bytes.len() as f32 / 256.0
    }

    /// Execute specific processing strategy
    fn execute_processing_strategy(&self, data: &[u8], strategy: usize) -> Result<Vec<u8>> {
        match strategy {
            0 => self.strategy_quantum_superposition(data),
            1 => self.strategy_quantum_entanglement(data),
            2 => self.strategy_quantum_interference(data),
            3 => self.strategy_quantum_tunneling(data),
            _ => self.strategy_classical_fallback(data),
        }
    }

    /// Quantum superposition-based processing
    fn strategy_quantum_superposition(&self, data: &[u8]) -> Result<Vec<u8>> {
        let mut result = Vec::with_capacity(data.len());

        // Process data in superposition states
        for chunk in data.chunks(4) {
            let superposed_value = chunk
                .iter()
                .enumerate()
                .map(|(i, &byte)| {
                    let weight = (i as f32 + 1.0) / 4.0;
                    byte as f32 * weight * self.params.superposition_factor
                })
                .sum::<f32>();

            result.push(superposed_value as u8);
        }

        // Pad to original size if needed
        while result.len() < data.len() {
            result.push(0);
        }

        Ok(result)
    }

    /// Quantum entanglement-based processing
    fn strategy_quantum_entanglement(&self, data: &[u8]) -> Result<Vec<u8>> {
        let mut result = Vec::with_capacity(data.len());

        // Create entangled pairs of bytes
        for pair in data.chunks(2) {
            if pair.len() == 2 {
                let entangled_value =
                    (pair[0] as f32 + pair[1] as f32) * self.params.entanglement_strength;
                result.push(entangled_value as u8);
                result.push((255.0 - entangled_value) as u8);
            } else {
                result.push(pair[0]);
            }
        }

        Ok(result)
    }

    /// Quantum interference-based processing
    fn strategy_quantum_interference(&self, data: &[u8]) -> Result<Vec<u8>> {
        let mut result = Vec::with_capacity(data.len());

        for (i, &byte) in data.iter().enumerate() {
            let phase = 2.0 * PI * (i as f32) / data.len() as f32;
            let interference = (phase.cos() + phase.sin()) * self.params.interference_threshold;
            let processed_byte = ((byte as f32) * (1.0 + interference)) as u8;
            result.push(processed_byte);
        }

        Ok(result)
    }

    /// Quantum tunneling-based processing
    fn strategy_quantum_tunneling(&self, data: &[u8]) -> Result<Vec<u8>> {
        let mut result = Vec::with_capacity(data.len());

        for &byte in data.iter() {
            // Simulate quantum tunneling effect
            let barrier_height = 128.0;
            let tunneling_probability = (-((byte as f32 - barrier_height).abs() / 50.0)).exp();

            let tunneled_value = if tunneling_probability > self.params.measurement_threshold {
                255 - byte // Tunnel through barrier
            } else {
                byte // Classical behavior
            };

            result.push(tunneled_value);
        }

        Ok(result)
    }

    /// Classical fallback processing
    fn strategy_classical_fallback(&self, data: &[u8]) -> Result<Vec<u8>> {
        // Use SIMD for classical processing
        let float_data: Vec<f32> = data.iter().map(|&x| x as f32).collect();
        let array = Array1::from(float_data);
        let processed = f32::simd_mul(&array.view(), &Array1::from_elem(array.len(), 1.1).view());

        let result: Vec<u8> = processed.iter().map(|&x| x as u8).collect();
        Ok(result)
    }

    /// Record performance for adaptive optimization
    fn record_performance(&self, efficiency: f32) {
        let mut history = self.performance_history.write().expect("Operation failed");
        history.push(efficiency);
        if history.len() > 1000 {
            history.remove(0);
        }
    }

    /// Optimize parameters using quantum annealing
    pub fn optimize_parameters(&mut self) -> Result<()> {
        let history = self.performance_history.read().expect("Operation failed");
        if history.len() < 10 {
            return Ok(()); // Not enough data for optimization
        }

        let _avg_performance: f32 = history.iter().sum::<f32>() / history.len() as f32;

        // Use quantum annealing to optimize parameters
        let initial_params = vec![
            self.params.superposition_factor,
            self.params.entanglement_strength,
            self.params.interference_threshold,
            self.params.measurement_threshold,
            self.params.coherence_time,
        ];

        let mut optimizer = self.optimizer.write().expect("Operation failed");
        let optimized_params = optimizer.optimize(&initial_params)?;

        // Update parameters
        self.params.superposition_factor = optimized_params[0].clamp(0.0, 1.0);
        self.params.entanglement_strength = optimized_params[1].clamp(0.0, 1.0);
        self.params.interference_threshold = optimized_params[2].clamp(0.0, 1.0);
        self.params.measurement_threshold = optimized_params[3].clamp(0.0, 1.0);
        self.params.coherence_time = optimized_params[4].clamp(0.1, 10.0);

        Ok(())
    }

    /// Get current performance statistics
    pub fn get_performance_stats(&self) -> QuantumPerformanceStats {
        let history = self.performance_history.read().expect("Operation failed");

        if history.is_empty() {
            return QuantumPerformanceStats::default();
        }

        let avg_efficiency = history.iter().sum::<f32>() / history.len() as f32;
        let recent_efficiency =
            history.iter().rev().take(10).sum::<f32>() / 10.0_f32.min(history.len() as f32);

        QuantumPerformanceStats {
            total_operations: history.len(),
            average_efficiency: avg_efficiency,
            recent_efficiency,
            quantum_coherence: self.params.coherence_time,
            superposition_usage: self.params.superposition_factor,
            entanglement_usage: self.params.entanglement_strength,
        }
    }
}

/// Performance statistics for quantum-inspired processing
#[derive(Debug, Clone, Default)]
pub struct QuantumPerformanceStats {
    /// Total number of quantum operations performed
    pub total_operations: usize,
    /// Average efficiency across all operations (0.0-1.0)
    pub average_efficiency: f32,
    /// Recent efficiency for last few operations (0.0-1.0)
    pub recent_efficiency: f32,
    /// Quantum coherence time utilization (0.0-10.0)
    pub quantum_coherence: f32,
    /// Superposition algorithm usage factor (0.0-1.0)
    pub superposition_usage: f32,
    /// Entanglement algorithm usage factor (0.0-1.0)
    pub entanglement_usage: f32,
}

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

    #[test]
    fn test_quantum_state_creation() {
        let state = QuantumState::new(4);
        assert_eq!(state.amplitudes.len(), 4);
        assert_eq!(state.amplitudes[0], 1.0);
    }

    #[test]
    fn test_quantum_superposition() {
        let mut state = QuantumState::new(3);
        let weights = vec![0.6, 0.8, 0.0];
        state.superposition(&weights).expect("Operation failed");

        // Check normalization
        let norm_squared: f32 = state.amplitudes.iter().map(|&a| a * a).sum();
        assert!((norm_squared - 1.0).abs() < 1e-6);
    }

    #[test]
    fn test_quantum_measurement() {
        let mut state = QuantumState::new(4);
        let weights = vec![0.5, 0.5, 0.5, 0.5];
        state.superposition(&weights).expect("Operation failed");

        let measurement = state.measure();
        assert!(measurement < 4);
    }

    #[test]
    fn test_quantum_annealing_optimizer() {
        let mut optimizer = QuantumAnnealingOptimizer::new(5);
        let initial_params = vec![0.1, 0.2, 0.3, 0.4, 0.5];
        let result = optimizer
            .optimize(&initial_params)
            .expect("Operation failed");

        assert_eq!(result.len(), 5);
        assert!(result.iter().all(|&x| (0.0..=1.0).contains(&x)));
    }

    #[test]
    fn test_quantum_parallel_processor() {
        let mut processor = QuantumParallelProcessor::new(5);
        let test_data = vec![1, 2, 3, 4, 5, 6, 7, 8];
        let result = processor
            .process_quantum_parallel(&test_data)
            .expect("Operation failed");

        assert!(!result.is_empty());
        assert!(result.len() >= test_data.len());
    }

    #[test]
    fn test_entropy_calculation() {
        let processor = QuantumParallelProcessor::new(4);
        let uniform_data = vec![1, 2, 3, 4, 5, 6, 7, 8];
        let repeated_data = vec![1, 1, 1, 1, 1, 1, 1, 1];

        let uniform_entropy = processor.calculate_entropy(&uniform_data);
        let repeated_entropy = processor.calculate_entropy(&repeated_data);

        assert!(uniform_entropy > repeated_entropy);
    }
}
