import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
from typing import List, Tuple, Optional
import warnings


class KolmogorovQuantumCircuitClassifier(BaseEstimator, ClassifierMixin):
    """
    Quantum-inspired classifier using Kolmogorov complexity approximation via MDL.
    
    Dynamically prunes circuit depth by removing gates that don't reduce encoding cost.
    Uses minimum description length principle to optimize quantum circuit representation.
    
    Parameters
    ----------
    n_qubits : int, default=4
        Number of qubits in the quantum circuit
    max_depth : int, default=10
        Maximum circuit depth before pruning
    pruning_threshold : float, default=0.01
        Minimum MDL reduction required to keep a gate
    n_iterations : int, default=100
        Number of optimization iterations
    learning_rate : float, default=0.1
        Learning rate for parameter updates
    random_state : int, optional
        Random seed for reproducibility
    """
    
    def __init__(
        self,
        n_qubits: int = 4,
        max_depth: int = 10,
        pruning_threshold: float = 0.01,
        n_iterations: int = 100,
        learning_rate: float = 0.1,
        random_state: Optional[int] = None
    ):
        self.n_qubits = n_qubits
        self.max_depth = max_depth
        self.pruning_threshold = pruning_threshold
        self.n_iterations = n_iterations
        self.learning_rate = learning_rate
        self.random_state = random_state
        
    def _initialize_circuit(self) -> List[dict]:
        """Initialize quantum circuit with random gates."""
        rng = np.random.RandomState(self.random_state)
        circuit = []
        
        gate_types = ['RX', 'RY', 'RZ', 'CNOT']
        
        for depth in range(self.max_depth):
            for qubit in range(self.n_qubits):
                gate_type = rng.choice(gate_types)
                
                if gate_type == 'CNOT' and self.n_qubits > 1:
                    target = (qubit + 1) % self.n_qubits
                    gate = {
                        'type': gate_type,
                        'qubits': [qubit, target],
                        'params': None,
                        'depth': depth,
                        'active': True
                    }
                else:
                    gate = {
                        'type': gate_type if gate_type != 'CNOT' else 'RX',
                        'qubits': [qubit],
                        'params': rng.uniform(-np.pi, np.pi),
                        'depth': depth,
                        'active': True
                    }
                circuit.append(gate)
                
        return circuit
    
    def _apply_gate(self, state: np.ndarray, gate: dict) -> np.ndarray:
        """Apply a quantum gate to the state vector."""
        if not gate['active']:
            return state
            
        n = len(state)
        qubit = gate['qubits'][0]
        
        if gate['type'] == 'RX':
            theta = gate['params']
            cos_half = np.cos(theta / 2)
            sin_half = np.sin(theta / 2)
            
            new_state = state.copy()
            step = 2 ** qubit
            for i in range(0, n, 2 * step):
                for j in range(step):
                    idx0 = i + j
                    idx1 = i + j + step
                    new_state[idx0] = cos_half * state[idx0] - 1j * sin_half * state[idx1]
                    new_state[idx1] = -1j * sin_half * state[idx0] + cos_half * state[idx1]
            return new_state
            
        elif gate['type'] == 'RY':
            theta = gate['params']
            cos_half = np.cos(theta / 2)
            sin_half = np.sin(theta / 2)
            
            new_state = state.copy()
            step = 2 ** qubit
            for i in range(0, n, 2 * step):
                for j in range(step):
                    idx0 = i + j
                    idx1 = i + j + step
                    new_state[idx0] = cos_half * state[idx0] - sin_half * state[idx1]
                    new_state[idx1] = sin_half * state[idx0] + cos_half * state[idx1]
            return new_state
            
        elif gate['type'] == 'RZ':
            theta = gate['params']
            new_state = state.copy()
            step = 2 ** qubit
            for i in range(0, n, 2 * step):
                for j in range(step):
                    idx0 = i + j
                    idx1 = i + j + step
                    new_state[idx0] = state[idx0] * np.exp(-1j * theta / 2)
                    new_state[idx1] = state[idx1] * np.exp(1j * theta / 2)
            return new_state
            
        elif gate['type'] == 'CNOT' and len(gate['qubits']) == 2:
            control, target = gate['qubits']
            new_state = state.copy()
            
            for i in range(n):
                if (i >> control) & 1:
                    j = i ^ (1 << target)
                    if i > j:
                        new_state[i], new_state[j] = state[j], state[i]
            return new_state
            
        return state
    
    def _encode_input(self, x: np.ndarray) -> np.ndarray:
        """Encode classical input into quantum state."""
        state = np.zeros(2 ** self.n_qubits, dtype=complex)
        state[0] = 1.0
        
        # Amplitude encoding
        n_features = min(len(x), self.n_qubits)
        for i in range(n_features):
            theta = x[i] * np.pi
            gate = {'type': 'RY', 'qubits': [i], 'params': theta, 'active': True}
            state = self._apply_gate(state, gate)
            
        return state
    
    def _execute_circuit(self, x: np.ndarray) -> np.ndarray:
        """Execute quantum circuit on input."""
        state = self._encode_input(x)
        
        for gate in self.circuit_:
            state = self._apply_gate(state, gate)
            
        return state
    
    def _compute_mdl(self, circuit: List[dict], X: np.ndarray, y: np.ndarray) -> float:
        """
        Compute Minimum Description Length for the circuit.
        MDL = Model_Cost + Data_Cost
        """
        # Model cost: number of active gates and their parameters
        active_gates = [g for g in circuit if g['active']]
        n_params = sum(1 for g in active_gates if g['params'] is not None)
        model_cost = len(active_gates) + n_params * 0.5
        
        # Data cost: encoding error
        predictions = []
        for xi in X:
            state = self._encode_input(xi)
            for gate in circuit:
                state = self._apply_gate(state, gate)
            
            # Measure in computational basis
            probs = np.abs(state) ** 2
            pred = np.argmax(probs[:len(self.classes_)])
            predictions.append(pred)
        
        predictions = np.array(predictions)
        error_rate = np.mean(predictions != y)
        data_cost = -np.log(max(1 - error_rate, 1e-10)) * len(X)
        
        return model_cost + data_cost
    
    def _prune_circuit(self, X: np.ndarray, y: np.ndarray):
        """Prune gates that don't reduce MDL."""
        current_mdl = self._compute_mdl(self.circuit_, X, y)
        
        for gate in self.circuit_:
            if not gate['active']:
                continue
                
            # Temporarily deactivate gate
            gate['active'] = False
            new_mdl = self._compute_mdl(self.circuit_, X, y)
            
            # Keep gate deactivated if MDL doesn't increase significantly
            if new_mdl - current_mdl > self.pruning_threshold:
                gate['active'] = True
            else:
                current_mdl = new_mdl
    
    def _optimize_parameters(self, X: np.ndarray, y: np.ndarray):
        """Optimize gate parameters using gradient descent."""
        for iteration in range(self.n_iterations):
            for gate in self.circuit_:
                if not gate['active'] or gate['params'] is None:
                    continue
                
                # Finite difference gradient
                original_param = gate['params']
                epsilon = 0.01
                
                gate['params'] = original_param + epsilon
                loss_plus = self._compute_mdl(self.circuit_, X, y)
                
                gate['params'] = original_param - epsilon
                loss_minus = self._compute_mdl(self.circuit_, X, y)
                
                gradient = (loss_plus - loss_minus) / (2 * epsilon)
                
                # Update parameter
                gate['params'] = original_param - self.learning_rate * gradient
                gate['params'] = np.clip(gate['params'], -np.pi, np.pi)
    
    def fit(self, X, y):
        """
        Fit the quantum circuit classifier.
        
        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training data
        y : array-like of shape (n_samples,)
            Target values
            
        Returns
        -------
        self : object
            Fitted estimator
        """
        X, y = check_X_y(X, y)
        
        self.classes_ = unique_labels(y)
        self.n_features_in_ = X.shape[1]
        
        # Normalize features
        self.mean_ = np.mean(X, axis=0)
        self.std_ = np.std(X, axis=0) + 1e-8
        X_normalized = (X - self.mean_) / self.std_
        
        # Initialize circuit
        self.circuit_ = self._initialize_circuit()
        
        # Optimize parameters
        self._optimize_parameters(X_normalized, y)
        
        # Prune circuit based on MDL
        self._prune_circuit(X_normalized, y)
        
        # Final optimization after pruning
        self._optimize_parameters(X_normalized, y)
        
        return self
    
    def predict(self, X):
        """
        Predict class labels.
        
        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Test data
            
        Returns
        -------
        y_pred : array of shape (n_samples,)
            Predicted class labels
        """
        check_is_fitted(self)
        X = check_array(X)
        
        if X.shape[1] != self.n_features_in_:
            raise ValueError(f"Expected {self.n_features_in_} features, got {X.shape[1]}")
        
        X_normalized = (X - self.mean_) / self.std_
        
        predictions = []
        for xi in X_normalized:
            state = self._execute_circuit(xi)
            probs = np.abs(state) ** 2
            
            # Map to class labels
            pred_idx = np.argmax(probs[:len(self.classes_)])
            predictions.append(self.classes_[pred_idx])
        
        return np.array(predictions)
    
    def predict_proba(self, X):
        """
        Predict class probabilities.
        
        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Test data
            
        Returns
        -------
        proba : array of shape (n_samples, n_classes)
            Class probabilities
        """
        check_is_fitted(self)
        X = check_array(X)
        
        X_normalized = (X - self.mean_) / self.std_
        
        probabilities = []
        for xi in X_normalized:
            state = self._execute_circuit(xi)
            probs = np.abs(state) ** 2
            
            # Normalize to number of classes
            class_probs = probs[:len(self.classes_)]
            class_probs = class_probs / (np.sum(class_probs) + 1e-10)
            probabilities.append(class_probs)
        
        return np.array(probabilities)
    
    def get_circuit_depth(self):
        """Get the effective depth of the pruned circuit."""
        check_is_fitted(self)
        active_gates = [g for g in self.circuit_ if g['active']]
        if not active_gates:
            return 0
        return max(g['depth'] for g in active_gates) + 1
    
    def get_n_gates(self):
        """Get the number of active gates after pruning."""
        check_is_fitted(self)
        return sum(1 for g in self.circuit_ if g['active'])