"""
Quantum-inspired optimization module for HQDE framework.

This module implements quantum annealing and quantum-inspired optimization
algorithms for ensemble selection and hyperparameter optimization.
"""

import torch
import numpy as np
from typing import List, Dict, Optional, Tuple, Callable, Any
import math
import random


class QuantumEnsembleOptimizer:
    """Quantum-inspired optimizer for ensemble composition and hyperparameters."""

    def __init__(self,
                 temperature_schedule: str = "exponential",
                 initial_temperature: float = 10.0,
                 final_temperature: float = 0.01,
                 annealing_steps: int = 1000):
        """
        Initialize quantum ensemble optimizer.

        Args:
            temperature_schedule: Type of temperature schedule for annealing
            initial_temperature: Initial temperature for quantum annealing
            final_temperature: Final temperature for quantum annealing
            annealing_steps: Number of annealing steps
        """
        self.temperature_schedule = temperature_schedule
        self.initial_temperature = initial_temperature
        self.final_temperature = final_temperature
        self.annealing_steps = annealing_steps

    def get_temperature(self, step: int) -> float:
        """Get temperature at a given annealing step."""
        if step >= self.annealing_steps:
            return self.final_temperature

        progress = step / self.annealing_steps

        if self.temperature_schedule == "exponential":
            return self.initial_temperature * (self.final_temperature / self.initial_temperature) ** progress
        elif self.temperature_schedule == "linear":
            return self.initial_temperature * (1 - progress) + self.final_temperature * progress
        elif self.temperature_schedule == "cosine":
            return self.final_temperature + 0.5 * (self.initial_temperature - self.final_temperature) * \
                   (1 + math.cos(math.pi * progress))
        else:
            return self.initial_temperature * math.exp(-progress * 3)

    def formulate_qubo(self,
                      candidate_models: List[Dict[str, Any]],
                      constraints: Dict[str, Any]) -> torch.Tensor:
        """
        Formulate Quadratic Unconstrained Binary Optimization (QUBO) matrix.

        Args:
            candidate_models: List of candidate model configurations
            constraints: Optimization constraints (memory, accuracy, etc.)

        Returns:
            QUBO matrix for quantum annealing
        """
        num_models = len(candidate_models)
        qubo_matrix = torch.zeros(num_models, num_models)

        # Extract model properties
        accuracies = [model.get('accuracy', 0.5) for model in candidate_models]
        memory_costs = [model.get('memory_cost', 1.0) for model in candidate_models]
        compute_costs = [model.get('compute_cost', 1.0) for model in candidate_models]

        # Objective: maximize accuracy, minimize costs
        max_memory = constraints.get('max_memory', float('inf'))
        max_compute = constraints.get('max_compute', float('inf'))
        ensemble_size_target = constraints.get('ensemble_size', num_models // 2)

        # Diagonal terms (individual model contributions)
        for i in range(num_models):
            # Reward high accuracy
            accuracy_reward = accuracies[i] * 10.0

            # Penalize high costs
            memory_penalty = memory_costs[i] / max_memory * 5.0 if max_memory != float('inf') else 0
            compute_penalty = compute_costs[i] / max_compute * 5.0 if max_compute != float('inf') else 0

            qubo_matrix[i, i] = accuracy_reward - memory_penalty - compute_penalty

        # Off-diagonal terms (model interactions)
        for i in range(num_models):
            for j in range(i + 1, num_models):
                # Encourage diversity in ensemble
                accuracy_diff = abs(accuracies[i] - accuracies[j])
                diversity_bonus = accuracy_diff * 2.0

                # Penalize resource conflicts
                resource_conflict = (memory_costs[i] + memory_costs[j]) / max_memory * 2.0
                resource_conflict += (compute_costs[i] + compute_costs[j]) / max_compute * 2.0

                interaction_term = diversity_bonus - resource_conflict
                qubo_matrix[i, j] = qubo_matrix[j, i] = interaction_term

        # Add ensemble size constraint
        ensemble_size_penalty = 2.0
        for i in range(num_models):
            for j in range(num_models):
                if i != j:
                    qubo_matrix[i, j] -= ensemble_size_penalty / ensemble_size_target

        return qubo_matrix

    def quantum_annealing_solve(self,
                              qubo_matrix: torch.Tensor,
                              num_runs: int = 10) -> torch.Tensor:
        """
        Solve QUBO using simulated quantum annealing.

        Args:
            qubo_matrix: QUBO matrix to optimize
            num_runs: Number of annealing runs

        Returns:
            Best solution found (binary vector)
        """
        num_variables = qubo_matrix.shape[0]
        best_solution = None
        best_energy = float('inf')

        for run in range(num_runs):
            # Initialize random solution
            solution = torch.randint(0, 2, (num_variables,), dtype=torch.float32)

            # Annealing process
            for step in range(self.annealing_steps):
                temperature = self.get_temperature(step)

                # Select random variable to flip
                var_idx = random.randint(0, num_variables - 1)

                # Calculate energy change
                old_energy = self._calculate_qubo_energy(solution, qubo_matrix)

                # Flip bit
                solution[var_idx] = 1 - solution[var_idx]
                new_energy = self._calculate_qubo_energy(solution, qubo_matrix)

                energy_diff = new_energy - old_energy

                # Accept or reject move
                if energy_diff < 0 or random.random() < math.exp(-energy_diff / temperature):
                    # Accept move (bit stays flipped)
                    pass
                else:
                    # Reject move (flip bit back)
                    solution[var_idx] = 1 - solution[var_idx]

            # Check if this is the best solution
            final_energy = self._calculate_qubo_energy(solution, qubo_matrix)
            if final_energy < best_energy:
                best_energy = final_energy
                best_solution = solution.clone()

        return best_solution

    def _calculate_qubo_energy(self, solution: torch.Tensor, qubo_matrix: torch.Tensor) -> float:
        """Calculate energy of a QUBO solution."""
        return (solution @ qubo_matrix @ solution).item()

    def optimize_ensemble_composition(self,
                                    candidate_models: List[Dict[str, Any]],
                                    constraints: Dict[str, Any],
                                    use_quantum_annealing: bool = True) -> Tuple[List[int], Dict[str, Any]]:
        """
        Optimize ensemble composition using quantum-inspired methods.

        Args:
            candidate_models: List of candidate model configurations
            constraints: Optimization constraints
            use_quantum_annealing: Whether to use quantum annealing

        Returns:
            Tuple of (selected_model_indices, optimization_metrics)
        """
        # Formulate as QUBO problem
        qubo_matrix = self.formulate_qubo(candidate_models, constraints)

        if use_quantum_annealing:
            # Use quantum annealing
            solution = self.quantum_annealing_solve(qubo_matrix)
        else:
            # Use classical optimization (greedy)
            solution = self._greedy_solve(qubo_matrix)

        # Extract selected models
        selected_indices = [i for i, selected in enumerate(solution) if selected > 0.5]

        # Calculate optimization metrics
        metrics = self._calculate_optimization_metrics(
            selected_indices, candidate_models, constraints
        )

        return selected_indices, metrics

    def _greedy_solve(self, qubo_matrix: torch.Tensor) -> torch.Tensor:
        """Greedy solution for QUBO (fallback method)."""
        num_variables = qubo_matrix.shape[0]
        solution = torch.zeros(num_variables)

        # Greedily select variables that improve objective
        for _ in range(num_variables):
            best_var = -1
            best_improvement = 0

            for var in range(num_variables):
                if solution[var] == 0:  # Variable not selected
                    # Calculate improvement if we select this variable
                    test_solution = solution.clone()
                    test_solution[var] = 1

                    old_energy = self._calculate_qubo_energy(solution, qubo_matrix)
                    new_energy = self._calculate_qubo_energy(test_solution, qubo_matrix)
                    improvement = old_energy - new_energy

                    if improvement > best_improvement:
                        best_improvement = improvement
                        best_var = var

            if best_var >= 0 and best_improvement > 0:
                solution[best_var] = 1
            else:
                break

        return solution

    def _calculate_optimization_metrics(self,
                                      selected_indices: List[int],
                                      candidate_models: List[Dict[str, Any]],
                                      constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Calculate metrics for the optimization result."""
        if not selected_indices:
            return {
                'ensemble_size': 0,
                'total_accuracy': 0.0,
                'total_memory_cost': 0.0,
                'total_compute_cost': 0.0,
                'diversity_score': 0.0,
                'constraint_satisfaction': 0.0
            }

        selected_models = [candidate_models[i] for i in selected_indices]

        # Calculate metrics
        ensemble_size = len(selected_indices)
        total_accuracy = sum(model.get('accuracy', 0) for model in selected_models)
        total_memory_cost = sum(model.get('memory_cost', 0) for model in selected_models)
        total_compute_cost = sum(model.get('compute_cost', 0) for model in selected_models)

        # Diversity score (variance in accuracies)
        accuracies = [model.get('accuracy', 0) for model in selected_models]
        diversity_score = np.var(accuracies) if len(accuracies) > 1 else 0.0

        # Constraint satisfaction
        max_memory = constraints.get('max_memory', float('inf'))
        max_compute = constraints.get('max_compute', float('inf'))

        memory_satisfaction = 1.0 if total_memory_cost <= max_memory else max_memory / total_memory_cost
        compute_satisfaction = 1.0 if total_compute_cost <= max_compute else max_compute / total_compute_cost
        constraint_satisfaction = min(memory_satisfaction, compute_satisfaction)

        return {
            'ensemble_size': ensemble_size,
            'total_accuracy': total_accuracy,
            'total_memory_cost': total_memory_cost,
            'total_compute_cost': total_compute_cost,
            'diversity_score': diversity_score,
            'constraint_satisfaction': constraint_satisfaction,
            'average_accuracy': total_accuracy / ensemble_size if ensemble_size > 0 else 0.0
        }

    def optimize_hyperparameters(self,
                               objective_function: Callable,
                               parameter_space: Dict[str, Tuple[float, float]],
                               num_iterations: int = 100) -> Tuple[Dict[str, float], float]:
        """
        Optimize hyperparameters using quantum-inspired search.

        Args:
            objective_function: Function to optimize (should return higher values for better solutions)
            parameter_space: Dictionary of parameter ranges {name: (min, max)}
            num_iterations: Number of optimization iterations

        Returns:
            Tuple of (best_parameters, best_score)
        """
        best_params = None
        best_score = float('-inf')

        # Initialize with random parameters
        current_params = {}
        for param_name, (min_val, max_val) in parameter_space.items():
            current_params[param_name] = random.uniform(min_val, max_val)

        current_score = objective_function(current_params)

        # Quantum-inspired optimization loop
        for iteration in range(num_iterations):
            temperature = self.get_temperature(iteration * self.annealing_steps // num_iterations)

            # Generate quantum fluctuation in parameters
            new_params = {}
            for param_name, (min_val, max_val) in parameter_space.items():
                # Quantum tunneling effect: allow exploration beyond local minima
                quantum_noise = np.random.normal(0, temperature * (max_val - min_val) * 0.1)
                new_value = current_params[param_name] + quantum_noise

                # Ensure within bounds
                new_value = max(min_val, min(max_val, new_value))
                new_params[param_name] = new_value

            # Evaluate new parameters
            new_score = objective_function(new_params)

            # Quantum acceptance criteria
            score_diff = new_score - current_score
            if score_diff > 0 or random.random() < math.exp(score_diff / temperature):
                current_params = new_params
                current_score = new_score

                # Update best solution
                if current_score > best_score:
                    best_score = current_score
                    best_params = current_params.copy()

        return best_params, best_score