import numpy as np
from scipy.integrate import solve_ivp


# Constants for integration
DEFAULT_SOLVER_METHOD = 'DOP853'
DEFAULT_TOLERANCE = 1e-9


class BaseSolver:
    """
    Base class for ODE solvers.
    """
    def solve(self, rhs, x0, t_eval):
        raise NotImplementedError("Subclasses must implement solve method")


class SciPySolver(BaseSolver):
    """
    Solver using scipy.integrate.solve_ivp
    """
    def __init__(self, method=DEFAULT_SOLVER_METHOD, rtol=DEFAULT_TOLERANCE, atol=DEFAULT_TOLERANCE):
        self.method = method
        self.rtol = rtol
        self.atol = atol
    
    def solve(self, rhs, x0, t_eval):
        """
        Solve ODE using scipy.integrate.solve_ivp
        
        Args:
            rhs: Right-hand side function of the ODE system
            x0: Initial conditions
            t_eval: Time points to evaluate the solution
            
        Returns:
            Tuple of (solution_successful, x_solution, y_solution)
        """
        try:
            sol = solve_ivp(rhs, (t_eval[0], t_eval[-1]), x0, method=self.method,
                           rtol=self.rtol, atol=self.atol, t_eval=t_eval)
            if sol.success:
                return True, sol.y[0], sol.y[1]
            else:
                return False, None, None
        except Exception:
            return False, None, None


class NeuralFlowSolver(BaseSolver):
    """
    Neural network solver that learns the vector field (x, y) -> (dx/dt, dy/dt)
    """
    def __init__(self, model=None, epochs=2000, lr=1e-3):
        self.model = model
        self.epochs = epochs
        self.lr = lr
        self.trained = False
    
    def train(self, rhs, x0, t_train, y_train):
        """
        Train the neural network to learn the vector field
        
        Args:
            rhs: Right-hand side function of the ODE system (used for generating training data)
            x0: Initial conditions
            t_train: Time points for training
            y_train: Target values for training (derivatives)
        """
        # This is a placeholder implementation - a real implementation would involve
        # training a neural network to approximate the vector field
        # For now, we'll just store the target data
        self.t_train = t_train
        self.y_train = y_train
        self.trained = True
    
    def solve(self, rhs, x0, t_eval):
        """
        Solve ODE using the trained neural network
        
        Args:
            rhs: Right-hand side function of the ODE system
            x0: Initial conditions
            t_eval: Time points to evaluate the solution
            
        Returns:
            Tuple of (solution_successful, x_solution, y_solution)
        """
        if not self.trained:
            raise ValueError("Model must be trained before solving")
        
        # This is a placeholder implementation
        # A real implementation would use the trained neural network to solve the ODE
        # For now, we'll fall back to scipy solver if model is not implemented
        try:
            sol = solve_ivp(rhs, (t_eval[0], t_eval[-1]), x0, method=DEFAULT_SOLVER_METHOD, t_eval=t_eval)
            if sol.success:
                return True, sol.y[0], sol.y[1]
            else:
                return False, None, None
        except Exception:
            return False, None, None