"""
Stochastic Quantum Circuit Implementation

This module implements quantum circuit operations using the stochastic
quantum methods framework, enabling gate-based quantum computing
within Barandes' stochastic approach.
"""

import numpy as np
import logging
from typing import List, Dict, Tuple, Any, Optional, Union
from dataclasses import dataclass
import itertools
import time

# Define logger *before* the try...except block that uses it
logger = logging.getLogger(__name__)

# Canonical import path post-refactor (April 2025): always use absolute import for clarity and reliability
from src.quantum_finance.quantum_toolkit.stochastic.stochastic_quantum_simulator import StochasticQuantumSimulator, Trajectory, ConfigurationPoint

@dataclass
class StochasticQuantumState:
    """
    Represents a quantum state in the stochastic framework.
    
    This combines both the wave function and trajectory representations,
    allowing conversion between the two as needed.
    """
    trajectories: List[Trajectory]
    wave_function: Optional[np.ndarray] = None
    basis_states: Optional[np.ndarray] = None
    num_qubits: int = 1
    
    def __post_init__(self):
        """Validate and initialize derived properties."""
        if self.wave_function is not None and self.basis_states is None:
            # Create default basis states for computational basis
            if self.num_qubits > 0:
                self.basis_states = np.array([
                    list(map(int, bin(i)[2:].zfill(self.num_qubits)))
                    for i in range(2**self.num_qubits)
                ])
                
    def __str__(self) -> str:
        """String representation of the state."""
        if self.wave_function is not None:
            return f"StochasticQuantumState(wave_function={self.wave_function}, num_trajectories={len(self.trajectories)})"
        else:
            return f"StochasticQuantumState(num_trajectories={len(self.trajectories)})"

class StochasticQuantumCircuit:
    """
    Implements quantum circuit operations using stochastic quantum methods.
    
    This class bridges the gap between the standard quantum circuit model
    and the stochastic quantum approach, allowing gate-based operations
    to be interpreted and executed in the stochastic framework.
    """
    
    def __init__(self, 
                num_qubits: int, 
                num_trajectories: int = 1000,
                dt: float = 0.01,
                hbar: float = 1.0,
                seed: Optional[int] = None):
        """
        Initialize a stochastic quantum circuit.
        
        Args:
            num_qubits: Number of qubits in the circuit
            num_trajectories: Number of stochastic trajectories to simulate
            dt: Time step for stochastic evolution
            hbar: Reduced Planck's constant
            seed: Random seed for reproducibility
        """
        self.num_qubits = num_qubits
        self.num_trajectories = num_trajectories
        self.dt = dt
        self.hbar = hbar
        self.seed = seed
        
        # Create simulator for stochastic operations
        self.simulator = StochasticQuantumSimulator(
            config_space_dim=2**num_qubits,  # For now, we use a simplified mapping
            num_trajectories=num_trajectories,
            dt=dt,
            hbar=hbar,
            seed=seed
        )
        
        # Generate computational basis states
        self.basis_states = np.array([
            list(map(int, bin(i)[2:].zfill(num_qubits)))
            for i in range(2**num_qubits)
        ])
        
        logger.info(f"Initialized StochasticQuantumCircuit with {num_qubits} qubits, "
                   f"{num_trajectories} trajectories")
    
    def initialize_state(self, statevector: np.ndarray) -> StochasticQuantumState:
        """
        Initialize a quantum state from a statevector.
        
        Args:
            statevector: Complex amplitude vector
            
        Returns:
            StochasticQuantumState combining wave function and trajectories
        """
        if len(statevector) != 2**self.num_qubits:
            raise ValueError(f"Statevector length {len(statevector)} does not match "
                            f"expected length {2**self.num_qubits} for {self.num_qubits} qubits")
        
        # Create a simplified configuration space representation
        # We use a 1D representation where each basis state corresponds to a position
        simplified_basis = np.arange(2**self.num_qubits).reshape(-1, 1)
        
        # Convert wave function to trajectories
        trajectories = self.simulator.quantum_stochastic_correspondence(
            quantum_state=statevector,
            basis_states=simplified_basis
        )
        
        return StochasticQuantumState(
            trajectories=trajectories,
            wave_function=statevector,
            basis_states=self.basis_states,
            num_qubits=self.num_qubits
        )
    
    def apply_gate(self, 
                  gate_matrix: np.ndarray, 
                  state: StochasticQuantumState,
                  target_qubits: Optional[List[int]] = None) -> StochasticQuantumState:
        """
        Apply a quantum gate to the state.
        
        Args:
            gate_matrix: Unitary matrix representing the gate
            state: Current quantum state
            target_qubits: Qubits to apply the gate to (if None, applies to all qubits)
            
        Returns:
            Updated quantum state after gate application
        """
        # For now, we use the wave function representation for gate application
        # and then convert back to trajectories
        if state.wave_function is None:
            # Convert trajectories to wave function
            grid_points = np.arange(2**self.num_qubits).reshape(-1, 1)
            amplitudes, phases = self.simulator.convert_to_wave_function(grid_points)
            wave_function = amplitudes * np.exp(1j * phases)
        else:
            wave_function = state.wave_function.copy()
        
        # If target qubits not specified, assume it applies to all qubits
        if target_qubits is None:
            if gate_matrix.shape[0] != 2**self.num_qubits:
                raise ValueError(f"Gate matrix dimension {gate_matrix.shape} does not match "
                                f"expected dimension {(2**self.num_qubits, 2**self.num_qubits)}")
            
            # Apply gate directly to wave function
            new_wave_function = gate_matrix @ wave_function
        else:
            # Apply gate to specific qubits
            # This is a simplified implementation
            # In a complete implementation, we would use tensor products
            # to construct the full gate matrix
            raise NotImplementedError("Application to specific qubits not yet implemented")
        
        # Create simplified basis for stochastic representation
        simplified_basis = np.arange(2**self.num_qubits).reshape(-1, 1)
        
        # Convert back to trajectories
        trajectories = self.simulator.quantum_stochastic_correspondence(
            quantum_state=new_wave_function,
            basis_states=simplified_basis
        )
        
        return StochasticQuantumState(
            trajectories=trajectories,
            wave_function=new_wave_function,
            basis_states=state.basis_states,
            num_qubits=self.num_qubits
        )
    
    def measure(self, 
               state: StochasticQuantumState, 
               qubits: Optional[List[int]] = None,
               num_shots: int = 1000) -> Dict[str, int]:
        """
        Measure the quantum state.
        
        Args:
            state: Quantum state to measure
            qubits: List of qubits to measure (if None, measures all qubits)
            num_shots: Number of measurement shots
            
        Returns:
            Dictionary of measurement results and their counts
        """
        if qubits is None:
            qubits = list(range(self.num_qubits))
        
        results = {}
        
        # Use wave function if available
        if state.wave_function is not None:
            probabilities = np.abs(state.wave_function)**2
            
            # Generate samples according to probabilities
            indices = np.random.choice(len(probabilities), size=num_shots, p=probabilities)
            
            # Count occurrences
            for idx in indices:
                # Convert to binary string
                bitstring = ''.join(map(str, self.basis_states[idx][qubits]))
                
                if bitstring in results:
                    results[bitstring] += 1
                else:
                    results[bitstring] = 1
        else:
            # Use trajectories for measurement
            # This is a simplified approach - in a complete implementation,
            # we would need to properly handle measurement in the stochastic framework
            
            # Sample from trajectories based on weights
            weights = np.array([traj.weight for traj in state.trajectories])
            weights /= np.sum(weights)  # Normalize weights
            
            indices = np.random.choice(
                len(state.trajectories), 
                size=num_shots, 
                p=weights
            )
            
            # Count occurrences
            for idx in indices:
                # Get configuration from trajectory
                config = int(state.trajectories[idx].points[-1].configuration[0])
                
                # Convert to binary and extract measured qubits
                binary = bin(config)[2:].zfill(self.num_qubits)
                bitstring = ''.join(binary[q] for q in qubits)
                
                if bitstring in results:
                    results[bitstring] += 1
                else:
                    results[bitstring] = 1
        
        return results
    
    def evolve_stochastic(self, 
                        state: StochasticQuantumState,
                        steps: int,
                        hamiltonian: Optional[np.ndarray] = None) -> StochasticQuantumState:
        """
        Evolve the quantum state using stochastic methods.
        
        Args:
            state: Quantum state to evolve
            steps: Number of time steps to evolve
            hamiltonian: Optional Hamiltonian matrix
            
        Returns:
            Evolved quantum state
        """
        if not state.trajectories:
            raise ValueError("No trajectories to evolve")
        
        # Define drift function based on Hamiltonian if provided
        # This is a simplified approach
        if hamiltonian is not None:
            # Time-dependent Schrödinger equation in Bohmian form gives a specific velocity field
            # This is a complex implementation beyond the scope of this simplified model
            drift_func = None  # To be implemented in a full version
        else:
            drift_func = None
        
        # Define potential function if needed
        potential_func = None
        
        # Get initial configuration from first trajectory point
        initial_config = state.trajectories[0].points[-1].configuration
        
        # Evolve using the stochastic simulator
        evolved_trajectories = self.simulator.evolve_stochastic_process(
            initial_state=initial_config,
            steps=steps,
            potential_func=potential_func,
            drift_func=drift_func
        )
        
        # Convert back to wave function representation
        grid_points = np.arange(2**self.num_qubits).reshape(-1, 1)
        amplitudes, phases = self.simulator.convert_to_wave_function(grid_points)
        new_wave_function = amplitudes * np.exp(1j * phases)
        
        return StochasticQuantumState(
            trajectories=evolved_trajectories,
            wave_function=new_wave_function,
            basis_states=state.basis_states,
            num_qubits=self.num_qubits
        )
        
    def simulate_circuit(self, gates: List[Tuple[np.ndarray, List[int]]]) -> StochasticQuantumState:
        """
        Simulate a complete quantum circuit.
        
        Args:
            gates: List of (gate_matrix, target_qubits) tuples
            
        Returns:
            Final quantum state after all gates are applied
        """
        # Initialize state to |0...0>
        initial_statevector = np.zeros(2**self.num_qubits, dtype=complex)
        initial_statevector[0] = 1.0
        
        state = self.initialize_state(initial_statevector)
        
        # Apply gates in sequence
        for gate_matrix, target_qubits in gates:
            state = self.apply_gate(gate_matrix, state, target_qubits)
        
        return state 