"""
Module: qio_module.py
Description: This module handles quantum input/output operations and related utility functions for virtual quantum simulations.
Each function is documented with detailed inline comments to ensure clarity and maintainability.

TODO:
- Refactor complex functions into smaller, single-responsibility helper functions.
- Enhance type annotations throughout the module.
- Standardize error handling and add structured logging.
"""

import numpy as np
import opt_einsum as oe
from quantum_finance.backend.tensor_networks import mps_decomposition, low_rank_approximation
import logging

logger = logging.getLogger(__name__)

def solve_max_cut(graph):
    """
    Solves the Max-Cut problem using Quantum-Inspired Optimization (QIO).
    
    Args:
        graph (dict): A dictionary representing the graph where keys are node identifiers and values are lists of adjacent nodes.
    
    Returns:
        set: A set of nodes representing one partition of the Max-Cut.
    """
    # Initialize variables for QIO
    partition = set()
    
    # Example QIO logic (placeholder)
    for node in graph:
        if some_qio_condition(node):
            partition.add(node)
    
    return partition

def some_qio_condition(node: any) -> bool:
    """Placeholder function for quantum-inspired condition logic.
    
    Args:
        node (any): A node from the graph.
    
    Returns:
        bool: True if the node satisfies the quantum-inspired condition, otherwise False.
    """
    # Temporary placeholder logic: return True for demonstration purposes
    return True

class QIOOptimizer:
    def __init__(self, problem_size, num_layers):
        self.problem_size = problem_size
        self.num_layers = num_layers
        self.gamma = np.random.uniform(0, 2 * np.pi, self.num_layers)
        self.beta = np.random.uniform(0, np.pi, self.num_layers)

    def initialize_parameters(self):
        """
        Initializes the gamma and beta parameters for the optimizer.
        """
        self.gamma = np.random.uniform(0, 2 * np.pi, self.num_layers)
        self.beta = np.random.uniform(0, np.pi, self.num_layers)

    def apply_problem_hamiltonian(self, state, cost_function):
        """
        Applies the problem Hamiltonian to the current state based on the cost function.
        
        Args:
            state (array): Current state of the system.
            cost_function (callable): Function to compute the cost.
        
        Returns:
            array: Updated state after applying the problem Hamiltonian.
        """
        # Placeholder for applying the problem Hamiltonian
        return state

    def apply_mixing_hamiltonian(self, state):
        """
        Applies the mixing Hamiltonian to the current state.
        
        Args:
            state (array): Current state of the system.
        
        Returns:
            array: Updated state after applying the mixing Hamiltonian.
        """
        # Placeholder for applying the mixing Hamiltonian
        return state

    def optimize(self, cost_function, num_iterations):
        """
        Performs the optimization loop using QIO.
        
        Args:
            cost_function (callable): Function to compute the cost for a given state.
            num_iterations (int): Number of optimization iterations.
        
        Returns:
            int: The most probable solution after optimization.
        """
        # Initialize parameters and state
        self.initialize_parameters()
        state = np.ones(self.problem_size) / np.sqrt(self.problem_size)
        
        for _ in range(num_iterations):
            for layer in range(self.num_layers):
                state = self.apply_problem_hamiltonian(state, cost_function)  # Apply problem Hamiltonian
                state = self.apply_mixing_hamiltonian(state)                 # Apply mixing Hamiltonian
            
            # Update parameters with boundary checks to maintain valid ranges
            self.gamma += np.random.normal(0, 0.1, self.num_layers)
            self.beta += np.random.normal(0, 0.1, self.num_layers)
            
            # Ensure parameters stay within valid ranges
            self.gamma = np.mod(self.gamma, 2 * np.pi)
            self.beta = np.mod(self.beta, np.pi)
        
        # Return the index of the maximum probability in the state
        return np.argmax(np.abs(state)**2)  # Return the most probable solution

class QuantumInspiredOptimizer:
    def __init__(self, problem_size, population_size=100):
        self.problem_size = problem_size
        self.population_size = population_size
        self.population = np.random.rand(population_size, problem_size)

    def quantum_inspired_crossover(self, parent1, parent2):
        # Implement quantum-inspired crossover
        alpha = np.random.rand()
        child = alpha * parent1 + np.sqrt(1 - alpha**2) * parent2
        return child

    def quantum_inspired_mutation(self, individual, mutation_rate=0.01):
        # Implement quantum-inspired mutation
        mutation_mask = np.random.rand(self.problem_size) < mutation_rate
        individual[mutation_mask] = 1 - individual[mutation_mask]
        return individual

    def optimize(self, fitness_func, generations=100):
        for _ in range(generations):
            # Evaluate fitness
            fitness = np.array([fitness_func(ind) for ind in self.population])
            
            # Select parents
            parents = self.population[np.argsort(fitness)[-2:]]
            
            # Create new population
            new_population = []
            for _ in range(self.population_size):
                child = self.quantum_inspired_crossover(parents[0], parents[1])
                child = self.quantum_inspired_mutation(child)
                new_population.append(child)
            
            self.population = np.array(new_population)

        # Return best solution
        best_idx = np.argmax([fitness_func(ind) for ind in self.population])
        return self.population[best_idx]

# Example usage
def example_fitness_func(x):
    return -np.sum((x - 0.5)**2)  # Maximize this function

qio = QuantumInspiredOptimizer(problem_size=10)
best_solution = qio.optimize(example_fitness_func)
print("Best solution:", best_solution)
print("Fitness:", example_fitness_func(best_solution))

def entanglement_aware_attention(qs, ks):
    """
    Computes attention scores using optimized tensor contractions.
    """
    attn_scores = oe.contract('bhqd,bhkd->bhqk', qs, ks)
    return attn_scores

def parallel_processing(model, data_loader):
    """
    Utilizes multiple GPUs for model training.
    """
    model = nn.DataParallel(model)
    for data in data_loader:
        # Move data to GPU
        data = data.to('cuda')
        # Model computations
        # ...

def adaptive_learning_rate(optimizer, loss):
    """
    Adjusts the learning rate based on the loss value.
    """
    for param_group in optimizer.param_groups:
        param_group['lr'] = base_lr / (1 + decay * loss)

# Initialize memory-centric processor
class MemoryCentricProcessor:
    def __init__(self):
        # Initialize your processor here
        pass

    # Add other methods as needed

memory_processor = MemoryCentricProcessor()

def optimize(parameters):
    # Use memory-centric processor for optimization
    optimized_params = memory_processor.optimize(parameters)
    return optimized_params

def quantum_inspired_optimization(data):
    # Preprocess data
    processed_data = memory_processor.process(data)
    # Perform QIO using processed_data
    optimized_data = optimize(processed_data)
    return optimized_data

def optimize_quantum_data(data: list) -> list:
    """Optimize quantum data using quantum-inspired algorithms.

    Args:
        data (list): A list of quantum data measurements.

    Returns:
        list: The optimized quantum data.
    """
    # TODO: Implement optimization logic
    # For now, return the input data unchanged
    return data

# --- Enhanced Quantum Simulation Function Added ---

def simulate_quantum_circuit(params):
    """Simulate a quantum circuit with optimized error handling and logging.

    Parameters:
        params (dict): Contains the circuit configuration, noise models, and other simulation parameters.

    Returns:
        dict: A dictionary with keys 'output_state' and 'metrics' including performance data.

    Raises:
        ValueError: If the input parameters are not a dictionary.
    """
    # Validate input parameters
    if not isinstance(params, dict):
        raise ValueError("Parameters must be provided as a dictionary.")
    
    # Log simulation start using structured logging
    logger.info("Starting quantum circuit simulation with parameters: %s", params)
    
    try:
        # Placeholder for actual quantum simulation logic
        result = {
            "output_state": "simulated_state",
            "metrics": {"runtime": 0.123}
        }
        # Log successful completion
        logger.info("Simulation completed successfully.")
        return result
    except Exception as e:
        logger.error("Error during simulation: %s", str(e))
        raise

def simulate_quantum_operation(operation, qubits):
    """
    Simulate a quantum operation on a given set of qubits.

    Parameters:
        operation (str): The name of the quantum operation to perform (e.g., 'H', 'CNOT').
        qubits (list): A list representing the qubits involved in the operation.

    Returns:
        list: The resulting state of the qubits after applying the operation.
    """
    # TODO: Implement the simulation logic for the quantum operation
    pass

def load_quantum_data(source):
    """
    Load quantum data from a specified source.

    Parameters:
        source (str): The data source path or identifier.

    Returns:
        data: The loaded quantum data in the appropriate format.
    """
    # TODO: Add data loading logic here
    pass

# Additional functions should have similar inline documentation detailing parameters,
# processing steps, and returned values.