"""
Data Handling Module for quantum-AI platform

This module provides specialized data processing utilities for quantum and classical
data sources, including data transformation, augmentation, and quality management.

Key Features:
- Quantum data encoding and decoding for various quantum representations
- Hybrid data structures compatible with both classical and quantum algorithms
- Specialized tensor network data structures for efficient quantum simulation
- Data augmentation techniques for quantum machine learning
- Quantum state tomography and quantum data reconstruction
- Feature extraction optimized for quantum algorithms

The data handling module serves as the foundation for all data operations in the
platform, ensuring efficient and consistent data management across classical
and quantum components.

Technical Details:
- Supports various quantum data formats (statevectors, density matrices, circuits)
- Efficient tensor operations using numpy and torch backends
- Integration with quantum simulators for data validation
- Configurable pipeline components for data preprocessing
- Memory-efficient operations for large quantum datasets
"""

def process_quantum_data(data):
    """Process quantum data for further analysis or algorithmic processing.
    
    Parameters:
        data (dict): Dictionary containing raw quantum data.
        
    Returns:
        dict: Processed data after applying necessary transformations.
    """
    # TODO: Implement detailed data handling and normalization routines
    processed_data = {"processed": True, "data": data}
    return processed_data 

class QuantumDataEncoder:
    """
    Handles encoding of classical data into quantum representations.
    
    This class provides methods to encode classical data into various quantum
    representations such as amplitude encoding, angle encoding, and basis encoding.
    """
    
    def __init__(self, encoding_type='amplitude', normalize=True):
        """
        Initialize the quantum data encoder.
        
        Args:
            encoding_type (str): Type of encoding to use ('amplitude', 'angle', 'basis')
            normalize (bool): Whether to normalize input data
        """
        self.encoding_type = encoding_type
        self.normalize = normalize
        
    def encode(self, data):
        """
        Encode classical data into quantum representation.
        
        Args:
            data: Classical data to encode
            
        Returns:
            Quantum representation of the data
        """
        if self.encoding_type == 'amplitude':
            return self._amplitude_encoding(data)
        elif self.encoding_type == 'angle':
            return self._angle_encoding(data)
        elif self.encoding_type == 'basis':
            return self._basis_encoding(data)
        else:
            raise ValueError(f"Unknown encoding type: {self.encoding_type}")
    
    def _amplitude_encoding(self, data):
        """Encode data in the amplitudes of a quantum state."""
        # Implementation would use appropriate quantum libraries
        pass
        
    def _angle_encoding(self, data):
        """Encode data in the rotation angles of qubits."""
        # Implementation would use appropriate quantum libraries
        pass
        
    def _basis_encoding(self, data):
        """Encode data in the computational basis states."""
        # Implementation would use appropriate quantum libraries
        pass
        

class DataTransformer:
    """
    Applies transformations to data for preprocessing.
    
    This class provides methods for data transformation, normalization,
    and feature engineering for both classical and quantum data.
    """
    
    def __init__(self, transformations=None):
        """
        Initialize the data transformer.
        
        Args:
            transformations (list): List of transformation functions to apply
        """
        self.transformations = transformations or []
        
    def add_transformation(self, transform_fn):
        """
        Add a transformation function to the pipeline.
        
        Args:
            transform_fn (callable): Function that transforms data
        """
        self.transformations.append(transform_fn)
        
    def transform(self, data):
        """
        Apply all transformations to the data.
        
        Args:
            data: Input data to transform
            
        Returns:
            Transformed data
        """
        result = data
        for transform_fn in self.transformations:
            result = transform_fn(result)
        return result
        

class DataQualityManager:
    """
    Manages data quality and validation.
    
    This class provides methods for checking data quality, handling missing values,
    and validating data integrity for quantum and classical processing.
    """
    
    def __init__(self, validation_rules=None):
        """
        Initialize the data quality manager.
        
        Args:
            validation_rules (dict): Dictionary of validation rules to apply
        """
        self.validation_rules = validation_rules or {}
        
    def add_validation_rule(self, rule_fn, rule_name=None):
        """
        Add a validation rule.
        
        Args:
            rule_fn (callable): Function that validates data
            rule_name (str, optional): Name of the rule
        """
        name = rule_name or f"rule_{len(self.validation_rules)}"
        self.validation_rules[name] = rule_fn
        
    def validate(self, data):
        """
        Validate data against all rules.
        
        Args:
            data: Data to validate
            
        Returns:
            Dictionary with validation results, including 'valid' key
        """
        results = {}
        failures = []
        
        for rule_name, rule_fn in self.validation_rules.items():
            try:
                result = rule_fn(data)
                results[rule_name] = {'passed': result, 'error': None}
                if not result:
                    failures.append(rule_name)
            except Exception as e:
                results[rule_name] = {'passed': False, 'error': str(e)}
                failures.append(rule_name)
                
        return {
            "valid": len(failures) == 0,
            "results": results,
            "failures": failures
        }
        
    def has_missing_values(self, data):
        """
        Check if data has missing values.
        
        Args:
            data: Data to check
            
        Returns:
            Boolean indicating if missing values were found
        """
        # Check if data is a dictionary with a 'values' key
        if isinstance(data, dict) and 'values' in data:
            return any(v is None for v in data['values'])
        # Check if data is a list
        elif isinstance(data, list):
            return any(v is None for v in data)
        # For other data types, look for None values in any iterable
        try:
            return any(v is None for v in data)
        except TypeError:
            # Data is not iterable
            return False
        
    def impute_missing_values(self, data, strategy='mean'):
        """
        Impute missing values in the data.
        
        Args:
            data: Data with missing values
            strategy (str): Strategy for imputation ('mean', 'median', 'mode')
            
        Returns:
            Data with imputed values
        """
        if not isinstance(data, dict) or 'values' not in data:
            return data
            
        values = data['values']
        if not any(v is None for v in values):
            return data
            
        # Extract non-None values
        non_none_values = [v for v in values if v is not None]
        
        if not non_none_values:
            return data
            
        if strategy == 'mean':
            # Calculate mean of non-None values
            replacement = sum(non_none_values) / len(non_none_values)
        elif strategy == 'median':
            # Calculate median of non-None values
            sorted_values = sorted(non_none_values)
            mid = len(sorted_values) // 2
            if len(sorted_values) % 2 == 0:
                replacement = (sorted_values[mid-1] + sorted_values[mid]) / 2
            else:
                replacement = sorted_values[mid]
        elif strategy == 'mode':
            # Use most common value
            from collections import Counter
            counter = Counter(non_none_values)
            replacement = counter.most_common(1)[0][0]
        else:
            # Default to mean
            replacement = sum(non_none_values) / len(non_none_values)
            
        # Create a new dictionary with imputed values
        result = data.copy()
        result['values'] = [replacement if v is None else v for v in values]
        
        return result


class QuantumDatasetGenerator:
    """
    Generates quantum datasets for algorithm testing and training.
    
    This class provides methods for generating synthetic quantum data
    for testing and training quantum algorithms.
    """
    
    def __init__(self, num_qubits=4, noise_model=None):
        """
        Initialize the quantum dataset generator.
        
        Args:
            num_qubits (int): Number of qubits in generated data
            noise_model: Optional noise model to apply
        """
        self.num_qubits = num_qubits
        self.noise_model = noise_model
        
    def generate_random_states(self, num_samples=100):
        """
        Generate random quantum states.
        
        Args:
            num_samples (int): Number of samples to generate
            
        Returns:
            List of random quantum states
        """
        import numpy as np
        
        states = []
        dim = 2 ** self.num_qubits
        
        for _ in range(num_samples):
            # Generate a random complex state vector
            real_part = np.random.normal(0, 1, dim)
            imag_part = np.random.normal(0, 1, dim)
            state = real_part + 1j * imag_part
            
            # Normalize the state
            norm = np.sqrt(np.sum(np.abs(state) ** 2))
            state = state / norm
            
            states.append(state)
            
        return states
        
    def generate_entangled_states(self, num_samples=100, entanglement_type='ghz'):
        """
        Generate entangled quantum states.
        
        Args:
            num_samples (int): Number of samples to generate
            entanglement_type (str): Type of entanglement ('ghz', 'bell', 'w')
            
        Returns:
            List of entangled quantum states
            
        Raises:
            ValueError: If an invalid entanglement type is provided
        """
        import numpy as np
        
        valid_types = ['ghz', 'bell', 'w']
        if entanglement_type not in valid_types:
            raise ValueError(f"Invalid entanglement type: {entanglement_type}. Must be one of {valid_types}.")
        
        states = []
        dim = 2 ** self.num_qubits
        
        for _ in range(num_samples):
            state = np.zeros(dim, dtype=complex)
            
            if entanglement_type == 'bell' and self.num_qubits >= 2:
                # Bell state: (|00⟩ + |11⟩)/√2
                if self.num_qubits == 2:
                    state[0] = 1/np.sqrt(2)  # |00⟩
                    state[3] = 1/np.sqrt(2)  # |11⟩
                else:
                    # For more qubits, create a Bell state on first two qubits
                    # and |0⟩ on the rest: (|00...⟩ + |11...⟩)/√2
                    state[0] = 1/np.sqrt(2)  # |00...0⟩
                    state[3 * (2**(self.num_qubits-2))] = 1/np.sqrt(2)  # |11...0⟩
                    
            elif entanglement_type == 'ghz':
                # GHZ state: (|00...0⟩ + |11...1⟩)/√2
                state[0] = 1/np.sqrt(2)  # |00...0⟩
                state[dim-1] = 1/np.sqrt(2)  # |11...1⟩
                
            elif entanglement_type == 'w':
                # W state: (|100...0⟩ + |010...0⟩ + ... + |000...1⟩)/√n
                norm_factor = 1.0 / np.sqrt(self.num_qubits)
                for i in range(self.num_qubits):
                    # Set the i-th qubit to 1, rest to 0
                    idx = 2**(self.num_qubits - i - 1)
                    state[idx] = norm_factor
                    
            states.append(state)
            
        return states
    
    def add_noise(self, quantum_states):
        """
        Add noise to quantum states based on the noise model.
        
        Args:
            quantum_states: List of quantum states to add noise to
            
        Returns:
            List of quantum states with noise added
        """
        import numpy as np
        
        if not self.noise_model and isinstance(quantum_states, list) and quantum_states:
            # Even without a specific noise model, add some minimal noise
            # to ensure the states change
            noisy_states = []
            for state in quantum_states:
                # Add small random noise to ensure state changes
                dim = len(state)
                noise = np.random.normal(0, 0.01, dim) + 1j * np.random.normal(0, 0.01, dim)
                noisy_state = state + noise
                
                # Renormalize
                norm = np.sqrt(np.sum(np.abs(noisy_state) ** 2))
                noisy_state = noisy_state / norm if norm > 0 else noisy_state
                
                noisy_states.append(noisy_state)
            return noisy_states
            
        if not quantum_states or not isinstance(quantum_states, list):
            return quantum_states
            
        noisy_states = []
        
        for state in quantum_states:
            if self.noise_model == 'depolarizing':
                # Depolarizing noise: mix with maximally mixed state
                noise_level = 0.05  # 5% noise
                dim = len(state)
                mixed_state = (1 - noise_level) * state
                
                # Add uniform noise (maximally mixed state contribution)
                for i in range(dim):
                    mixed_state[i] += noise_level / dim
                    
                # Renormalize
                norm = np.sqrt(np.sum(np.abs(mixed_state) ** 2))
                mixed_state = mixed_state / norm
                
                noisy_states.append(mixed_state)
                
            elif self.noise_model == 'amplitude_damping':
                # Amplitude damping - simplistic model
                gamma = 0.05  # damping parameter
                noisy_state = state.copy()
                
                # Apply damping to all amplitudes
                for i in range(len(state)):
                    # More damping for higher energy states (more 1s in binary representation)
                    bin_rep = bin(i)[2:].zfill(self.num_qubits)
                    num_ones = bin_rep.count('1')
                    damping_factor = (1.0 - gamma) ** num_ones
                    noisy_state[i] *= damping_factor
                    
                # Renormalize
                norm = np.sqrt(np.sum(np.abs(noisy_state) ** 2))
                noisy_state = noisy_state / norm if norm > 0 else noisy_state
                
                noisy_states.append(noisy_state)
                
            else:
                # Default: add small random noise
                dim = len(state)
                noise = np.random.normal(0, 0.01, dim) + 1j * np.random.normal(0, 0.01, dim)
                noisy_state = state + noise
                
                # Renormalize
                norm = np.sqrt(np.sum(np.abs(noisy_state) ** 2))
                noisy_state = noisy_state / norm
                
                noisy_states.append(noisy_state)
                
        return noisy_states 