"""
Stochastic Methods Adapter

This module provides an adapter for integrating stochastic quantum methods
into the unified API. It wraps the existing QuantumEnhancedCryptoRisk implementation
and adds the new QSDE solver capabilities.
"""

import logging
import time
import os
from typing import Dict, List, Any, Optional, Union, Tuple
import numpy as np

# Import the base interfaces
from ..unified_api import StochasticQuantumMethods, StochasticResult, StochasticConfig

# Import the implementations we're adapting
try:
    # Assuming implementations are now within quantum_toolkit.financial (adjust if needed)
    from quantum_toolkit.financial.quantum_enhanced_crypto_risk import QuantumEnhancedCryptoRisk
    from quantum_finance.quantum_bayesian_risk import QuantumBayesianRiskNetwork
    from quantum_finance.quantum_risk.analyzer import QuantumEnhancedCryptoRiskAnalyzer
    HAS_QUANTUM_RISK = True
except ImportError:
    HAS_QUANTUM_RISK = False
    logging.warning("QuantumEnhancedCryptoRisk not found, using simulation")

# Configure logging
logger = logging.getLogger(__name__)

class QSDESolver:
    """
    Quantum Stochastic Differential Equation Solver.
    
    This class implements quantum algorithms for solving stochastic
    differential equations.
    """
    
    def __init__(self, precision: float = 0.01, max_iterations: int = 1000):
        """
        Initialize the QSDE solver.
        
        Args:
            precision: Target precision for the solution
            max_iterations: Maximum number of iterations
        """
        self.precision = precision
        self.max_iterations = max_iterations
        logger.info(f"Initialized QSDESolver with precision={precision}, max_iterations={max_iterations}")
    
    def solve(self, 
             initial_state: List[float],
             drift_coefficient: float,
             diffusion_coefficient: float,
             time_steps: int,
             shots: int = 1024,
             adaptive_shots: bool = True) -> Dict[str, Any]:
        """
        Solve a stochastic differential equation using quantum algorithms.
        
        Args:
            initial_state: Initial state vector
            drift_coefficient: Drift coefficient for the SDE
            diffusion_coefficient: Diffusion coefficient for the SDE
            time_steps: Number of time steps for the simulation
            shots: Number of measurement shots (for fixed shot count)
            adaptive_shots: Whether to use adaptive shot count
            
        Returns:
            Dictionary with solution results
        """
        logger.info(f"Solving QSDE with {len(initial_state)} dimensions, {time_steps} time steps")
        start_time = time.time()
        
        # This is a placeholder implementation that would be replaced
        # with actual quantum circuit execution in the full implementation
        
        # Create a simulated trajectory
        trajectory = [initial_state.copy()]
        current_state = initial_state.copy()
        dt = 1.0 / time_steps
        
        for t in range(time_steps):
            # Simulate the SDE using Euler-Maruyama method
            for i in range(len(current_state)):
                # Generate random normal for Wiener process increment
                dW = np.random.normal(0, np.sqrt(dt))
                
                # Update using the SDE
                current_state[i] += drift_coefficient * current_state[i] * dt + \
                                  diffusion_coefficient * current_state[i] * dW
            
            trajectory.append(current_state.copy())
        
        # Calculate expectation values
        expectation_values = {
            'mean': np.mean(current_state),
            'variance': np.var(current_state),
            'max': np.max(current_state),
            'min': np.min(current_state)
        }
        
        execution_time = time.time() - start_time
        logger.info(f"QSDE solved in {execution_time:.2f} seconds")
        
        return {
            'final_state': current_state,
            'trajectory': trajectory,
            'expectation_values': expectation_values,
            'execution_time': execution_time,
            'shots_used': shots if not adaptive_shots else self._calculate_adaptive_shots(time_steps),
            'backend': 'qsde_simulator',  # This would be the actual backend in a real implementation
            'success': True
        }
    
    def _calculate_adaptive_shots(self, time_steps: int) -> int:
        """
        Calculate appropriate shot count based on time steps.
        
        Args:
            time_steps: Number of time steps
            
        Returns:
            Adaptive shot count
        """
        # Simple formula for increasing shots with complexity
        base_shots = 1024
        return min(16384, base_shots * (1 + time_steps // 10))

class StochasticMethodsAdapter(StochasticQuantumMethods):
    """
    Adapter for stochastic quantum methods, connecting the unified API
    to the existing implementations.
    """
    
    def __init__(self, config: StochasticConfig):
        """
        Initialize the stochastic methods adapter.
        
        Args:
            config: Configuration for stochastic methods
        """
        logger.info("Initializing StochasticMethodsAdapter")
        self.config = config
        
        # Initialize the QSDE solver
        self.qsde_solver = QSDESolver(
            precision=config.precision,
            max_iterations=config.max_iterations
        )
        
        # Initialize the risk assessor if available
        if HAS_QUANTUM_RISK:
            self.risk_assessor = QuantumEnhancedCryptoRisk(
                api_key=config.api_key,
                api_host=config.api_host
            )
            logger.info("Initialized QuantumEnhancedCryptoRisk")
        else:
            self.risk_assessor = None
            logger.warning("QuantumEnhancedCryptoRisk not available, risk assessment will be simulated")
    
    def simulate_qsde(self, parameters: Dict[str, Any]) -> StochasticResult:
        """
        Run a quantum stochastic differential equation simulation.
        
        Args:
            parameters: Simulation parameters
            
        Returns:
            StochasticResult with simulation results
        """
        logger.info("Running QSDE simulation")
        
        # Extract parameters
        initial_state = parameters.get('initial_state', [1.0])
        drift_coefficient = parameters.get('drift_coefficient', 0.1)
        diffusion_coefficient = parameters.get('diffusion_coefficient', 0.2)
        time_steps = parameters.get('time_steps', 100)
        shots = parameters.get('shots', self.config.default_shots)
        adaptive_shots = parameters.get('adaptive_shots', True)
        
        # Run the QSDE solver
        result = self.qsde_solver.solve(
            initial_state=initial_state,
            drift_coefficient=drift_coefficient,
            diffusion_coefficient=diffusion_coefficient,
            time_steps=time_steps,
            shots=shots,
            adaptive_shots=adaptive_shots
        )
        
        # Convert to standardized result format
        return StochasticResult(
            final_state=result['final_state'],
            trajectory=result['trajectory'],
            expectation_values=result['expectation_values'],
            execution_time=result['execution_time'],
            shots_used=result['shots_used'],
            backend_used=result['backend'],
            success=result['success'],
            error_message=None
        )
    
    def propagate_bayesian_risk(self, 
                             initial_probabilities: List[float],
                             market_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Propagate risk through a quantum Bayesian network.
        
        Args:
            initial_probabilities: Initial probabilities for risk factors
            market_data: Market data for risk assessment
            
        Returns:
            Dictionary with risk propagation results
        """
        logger.info(f"Propagating Bayesian risk with {len(initial_probabilities)} risk factors")
        start_time = time.time()
        
        # Use the real implementation if available
        if HAS_QUANTUM_RISK and self.risk_assessor is not None:
            try:
                # Use the existing implementation
                # Ensure self.risk_assessor has the attribute quantum_bayesian_network
                if hasattr(self.risk_assessor, 'quantum_bayesian_network'):
                    result = self.risk_assessor.quantum_bayesian_network.propagate_risk(
                        initial_probabilities=initial_probabilities,
                        market_data=market_data
                    )
                    
                    logger.info(f"Risk propagation completed in {time.time() - start_time:.2f} seconds")
                    return result
                else:
                    # Fallback if the attribute doesn't exist (e.g., configuration issue)
                    logger.warning("risk_assessor does not have quantum_bayesian_network attribute. Falling back to simulation.")
                    return self._simulate_risk_propagation(initial_probabilities, market_data)
                
            except Exception as e:
                logger.error(f"Error in risk propagation: {str(e)}")
                return self._simulate_risk_propagation(initial_probabilities, market_data)
        else:
            # Use simulated implementation
            logger.info("Using simulated risk propagation")
            return self._simulate_risk_propagation(initial_probabilities, market_data)
    
    def _simulate_risk_propagation(self, 
                                initial_probabilities: List[float],
                                market_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Simulate risk propagation when the real implementation is not available.
        
        Args:
            initial_probabilities: Initial probabilities for risk factors
            market_data: Market data for risk assessment
            
        Returns:
            Dictionary with simulated risk propagation results
        """
        start_time = time.time()
        
        # Simple simulation of a Bayesian network
        # This would be replaced with the actual implementation
        
        # Apply some transformations to the initial probabilities
        updated_probabilities = initial_probabilities.copy()
        
        # Simulate market impact on probabilities
        if market_data:
            # Adjust based on volatility if available
            volatility = market_data.get('volatility', 0.1)
            for i in range(len(updated_probabilities)):
                # Increase risk with volatility
                updated_probabilities[i] = min(0.95, updated_probabilities[i] * (1 + volatility))
            
            # Adjust based on market depth if available
            market_depth = market_data.get('market_depth', 1.0)
            if market_depth > 0:
                for i in range(len(updated_probabilities)):
                    # Decrease risk with higher market depth
                    updated_probabilities[i] *= (1 - 0.1 / market_depth)
        
        # Generate a trajectory
        trajectory = [initial_probabilities]
        steps = 5
        for i in range(steps):
            intermediate = initial_probabilities.copy()
            for j in range(len(intermediate)):
                # Linear interpolation
                intermediate[j] = initial_probabilities[j] + \
                                (updated_probabilities[j] - initial_probabilities[j]) * (i + 1) / steps
            trajectory.append(intermediate)
        
        # Calculate expectation values
        expectation_values = {
            'mean_risk': np.mean(updated_probabilities),
            'max_risk': np.max(updated_probabilities),
            'risk_variance': np.var(updated_probabilities)
        }
        
        execution_time = time.time() - start_time
        
        return {
            'updated_probabilities': updated_probabilities,
            'probability_trajectory': trajectory,
            'expectation_values': expectation_values,
            'execution_time': execution_time,
            'shots': 1024,  # Simulated shot count
            'backend': 'bayesian_simulator'
        } 