import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.preprocessing import StandardScaler
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
from scipy.special import softmax
from typing import Optional


class NeuralPlasticityQuantumAnnealingClassifier(BaseEstimator, ClassifierMixin):
    """
    Bio-inspired quantum annealing classifier with neural plasticity dynamics.
    
    This classifier mimics neural plasticity by adapting measurement collapse
    probabilities based on prediction error compression rates during quantum
    annealing schedules.
    
    Parameters
    ----------
    n_qubits : int, default=10
        Number of quantum states (qubits) per feature dimension
    n_annealing_steps : int, default=100
        Number of annealing schedule iterations
    initial_temperature : float, default=10.0
        Initial temperature for quantum annealing
    final_temperature : float, default=0.01
        Final temperature for quantum annealing
    plasticity_rate : float, default=0.1
        Learning rate for neural plasticity adaptation
    compression_threshold : float, default=0.05
        Threshold for prediction error compression detection
    hebbian_strength : float, default=0.5
        Strength of Hebbian-like weight updates
    random_state : int, optional
        Random seed for reproducibility
    """
    
    def __init__(
        self,
        n_qubits: int = 10,
        n_annealing_steps: int = 100,
        initial_temperature: float = 10.0,
        final_temperature: float = 0.01,
        plasticity_rate: float = 0.1,
        compression_threshold: float = 0.05,
        hebbian_strength: float = 0.5,
        random_state: Optional[int] = None
    ):
        self.n_qubits = n_qubits
        self.n_annealing_steps = n_annealing_steps
        self.initial_temperature = initial_temperature
        self.final_temperature = final_temperature
        self.plasticity_rate = plasticity_rate
        self.compression_threshold = compression_threshold
        self.hebbian_strength = hebbian_strength
        self.random_state = random_state
        
    def _initialize_quantum_state(self, n_features: int, n_classes: int):
        """Initialize quantum state parameters with superposition."""
        rng = np.random.RandomState(self.random_state)
        
        # Quantum weight matrix (features x qubits x classes)
        self.quantum_weights_ = rng.randn(n_features, self.n_qubits, n_classes) * 0.1
        
        # Collapse probability matrix (adaptive measurement probabilities)
        self.collapse_probs_ = np.ones((n_features, self.n_qubits)) / self.n_qubits
        
        # Plasticity traces (synaptic strength memory)
        self.plasticity_traces_ = np.zeros((n_features, self.n_qubits, n_classes))
        
        # Error compression history
        self.error_history_ = []
        
    def _annealing_schedule(self, step: int) -> float:
        """
        Compute temperature at given annealing step.
        Uses exponential cooling schedule inspired by simulated annealing.
        """
        progress = step / self.n_annealing_steps
        temp = self.initial_temperature * np.exp(
            -progress * np.log(self.initial_temperature / self.final_temperature)
        )
        return temp
    
    def _quantum_measurement_collapse(self, X: np.ndarray, temperature: float) -> np.ndarray:
        """
        Simulate quantum measurement collapse with temperature-dependent probabilities.
        
        Returns collapsed quantum states based on adaptive collapse probabilities.
        """
        n_samples, n_features = X.shape
        n_classes = self.quantum_weights_.shape[2]
        
        # Initialize collapsed state energies
        collapsed_energies = np.zeros((n_samples, n_classes))
        
        for i in range(n_features):
            # Normalize collapse probabilities with temperature
            temp_probs = softmax(self.collapse_probs_[i] / temperature)
            
            # Sample quantum state collapse for each sample
            for j in range(n_samples):
                # Weighted sampling based on adaptive collapse probabilities
                qubit_contributions = np.zeros(n_classes)
                
                for q in range(self.n_qubits):
                    # Quantum interference term
                    phase = X[j, i] * np.pi * q / self.n_qubits
                    amplitude = temp_probs[q] * np.cos(phase)
                    
                    # Accumulate weighted contributions
                    qubit_contributions += amplitude * self.quantum_weights_[i, q, :]
                
                collapsed_energies[j] += qubit_contributions
        
        return collapsed_energies
    
    def _compute_prediction_error(self, y_true: np.ndarray, y_pred_probs: np.ndarray) -> float:
        """Compute prediction error for plasticity adaptation."""
        y_true_onehot = np.zeros_like(y_pred_probs)
        y_true_onehot[np.arange(len(y_true)), y_true] = 1
        
        # Cross-entropy error
        error = -np.mean(np.sum(y_true_onehot * np.log(y_pred_probs + 1e-10), axis=1))
        return error
    
    def _compute_compression_rate(self) -> float:
        """
        Compute error compression rate (rate of error reduction).
        Mimics information compression in neural systems.
        """
        if len(self.error_history_) < 2:
            return 0.0
        
        recent_errors = self.error_history_[-10:]
        if len(recent_errors) < 2:
            return 0.0
        
        # Compute compression as relative error reduction
        compression = (recent_errors[0] - recent_errors[-1]) / (recent_errors[0] + 1e-10)
        return max(0.0, compression)
    
    def _adapt_collapse_probabilities(self, X: np.ndarray, y: np.ndarray, 
                                     y_pred_probs: np.ndarray):
        """
        Adapt measurement collapse probabilities based on prediction errors.
        Implements neural plasticity-inspired learning.
        """
        n_samples, n_features = X.shape
        y_true_onehot = np.zeros_like(y_pred_probs)
        y_true_onehot[np.arange(len(y)), y] = 1
        
        # Prediction error signal
        error_signal = y_true_onehot - y_pred_probs
        
        # Compute compression rate
        compression_rate = self._compute_compression_rate()
        
        # Adaptive plasticity modulation (higher when compression is low)
        plasticity_modulation = 1.0 + (1.0 - compression_rate)
        
        for i in range(n_features):
            for q in range(self.n_qubits):
                # Hebbian-like update: correlate input activity with error
                feature_activity = np.mean(np.abs(X[:, i]))
                qubit_phase = np.mean(np.cos(X[:, i] * np.pi * q / self.n_qubits))
                
                # Update quantum weights with plasticity
                for c in range(len(self.classes_)):
                    error_contribution = np.mean(error_signal[:, c])
                    
                    # Hebbian plasticity term
                    hebbian_update = (
                        self.hebbian_strength * 
                        feature_activity * 
                        qubit_phase * 
                        error_contribution
                    )
                    
                    # Update with plasticity modulation
                    self.quantum_weights_[i, q, c] += (
                        self.plasticity_rate * 
                        plasticity_modulation * 
                        hebbian_update
                    )
                    
                    # Update plasticity traces (exponential moving average)
                    self.plasticity_traces_[i, q, c] = (
                        0.9 * self.plasticity_traces_[i, q, c] + 
                        0.1 * np.abs(hebbian_update)
                    )
                
                # Adapt collapse probabilities based on plasticity traces
                trace_strength = np.mean(self.plasticity_traces_[i, q, :])
                self.collapse_probs_[i, q] += (
                    self.plasticity_rate * 
                    plasticity_modulation * 
                    trace_strength
                )
        
        # Normalize collapse probabilities
        self.collapse_probs_ = np.abs(self.collapse_probs_)
        self.collapse_probs_ /= (np.sum(self.collapse_probs_, axis=1, keepdims=True) + 1e-10)
    
    def fit(self, X_train, y_train):
        """
        Fit the quantum annealing classifier with neural plasticity.
        
        Parameters
        ----------
        X_train : array-like of shape (n_samples, n_features)
            Training data
        y_train : array-like of shape (n_samples,)
            Target values
            
        Returns
        -------
        self : object
            Fitted estimator
        """
        # Validate input
        X_train, y_train = check_X_y(X_train, y_train)
        
        # Store classes
        self.classes_ = unique_labels(y_train)
        self.n_classes_ = len(self.classes_)
        
        # Map labels to indices
        self.label_map_ = {label: idx for idx, label in enumerate(self.classes_)}
        y_mapped = np.array([self.label_map_[label] for label in y_train])
        
        # Standardize features
        self.scaler_ = StandardScaler()
        X_scaled = self.scaler_.fit_transform(X_train)
        
        # Initialize quantum state
        n_features = X_scaled.shape[1]
        self._initialize_quantum_state(n_features, self.n_classes_)
        
        # Quantum annealing with neural plasticity
        for step in range(self.n_annealing_steps):
            # Get current temperature
            temperature = self._annealing_schedule(step)
            
            # Quantum measurement collapse
            energies = self._quantum_measurement_collapse(X_scaled, temperature)
            
            # Convert energies to probabilities
            y_pred_probs = softmax(energies, axis=1)
            
            # Compute and store prediction error
            error = self._compute_prediction_error(y_mapped, y_pred_probs)
            self.error_history_.append(error)
            
            # Adapt collapse probabilities (neural plasticity)
            if step > 0:  # Skip first iteration
                self._adapt_collapse_probabilities(X_scaled, y_mapped, y_pred_probs)
        
        self.is_fitted_ = True
        return self
    
    def predict(self, X_test):
        """
        Predict class labels for samples in X_test.
        
        Parameters
        ----------
        X_test : array-like of shape (n_samples, n_features)
            Test data
            
        Returns
        -------
        y_pred : array-like of shape (n_samples,)
            Predicted class labels
        """
        # Check if fitted
        check_is_fitted(self, ['is_fitted_', 'quantum_weights_', 'scaler_'])
        
        # Validate input
        X_test = check_array(X_test)
        
        # Standardize features
        X_scaled = self.scaler_.transform(X_test)
        
        # Use final temperature for prediction
        temperature = self.final_temperature
        
        # Quantum measurement collapse
        energies = self._quantum_measurement_collapse(X_scaled, temperature)
        
        # Convert to probabilities and predict
        y_pred_probs = softmax(energies, axis=1)
        y_pred_indices = np.argmax(y_pred_probs, axis=1)
        
        # Map back to original labels
        y_pred = np.array([self.classes_[idx] for idx in y_pred_indices])
        
        return y_pred
    
    def predict_proba(self, X_test):
        """
        Predict class probabilities for samples in X_test.
        
        Parameters
        ----------
        X_test : array-like of shape (n_samples, n_features)
            Test data
            
        Returns
        -------
        y_pred_proba : array-like of shape (n_samples, n_classes)
            Predicted class probabilities
        """
        # Check if fitted
        check_is_fitted(self, ['is_fitted_', 'quantum_weights_', 'scaler_'])
        
        # Validate input
        X_test = check_array(X_test)
        
        # Standardize features
        X_scaled = self.scaler_.transform(X_test)
        
        # Use final temperature for prediction
        temperature = self.final_temperature
        
        # Quantum measurement collapse
        energies = self._quantum_measurement_collapse(X_scaled, temperature)
        
        # Convert to probabilities
        y_pred_probs = softmax(energies, axis=1)
        
        return y_pred_probs