from typing import Optional
from abc import ABC, abstractmethod

import numpy as np


class ActionNoise(ABC):
    """
    The action noise base class
    """

    def __init__(self):
        super(ActionNoise, self).__init__()

    def reset(self) -> None:
        """
        call end of episode reset for the noise
        """
        pass

    @abstractmethod
    def __call__(self) -> np.ndarray:
        raise NotImplementedError()


class NormalActionNoise(ActionNoise):
    """
    A Gaussian action noise

    :param mean: (np.ndarray) the mean value of the noise
    :param sigma: (np.ndarray) the scale of the noise (std here)
    """

    def __init__(self, mean: np.ndarray, sigma: np.ndarray):
        self._mu = mean
        self._sigma = sigma
        super(NormalActionNoise, self).__init__()

    def __call__(self) -> np.ndarray:
        return np.random.normal(self._mu, self._sigma)

    def __repr__(self) -> str:
        return f'NormalActionNoise(mu={self._mu}, sigma={self._sigma})'


class OrnsteinUhlenbeckActionNoise(ActionNoise):
    """
    A Ornstein Uhlenbeck action noise, this is designed to aproximate brownian motion with friction.

    Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab

    :param mean: (np.ndarray) the mean of the noise
    :param sigma: (np.ndarray) the scale of the noise
    :param theta: (float) the rate of mean reversion
    :param dt: (float) the timestep for the noise
    :param initial_noise: (Optional[np.ndarray]) the initial value for the noise output, (if None: 0)
    """

    def __init__(self, mean: np.ndarray,
                 sigma: np.ndarray,
                 theta: float = .15,
                 dt: float = 1e-2,
                 initial_noise: Optional[np.ndarray] = None):
        self._theta = theta
        self._mu = mean
        self._sigma = sigma
        self._dt = dt
        self.initial_noise = initial_noise
        self.noise_prev = np.zeros_like(self._mu)
        self.reset()
        super(OrnsteinUhlenbeckActionNoise, self).__init__()

    def __call__(self) -> np.ndarray:
        noise = self.noise_prev + self._theta * (self._mu - self.noise_prev) * self._dt + \
                self._sigma * np.sqrt(self._dt) * np.random.normal(size=self._mu.shape)
        self.noise_prev = noise
        return noise

    def reset(self) -> None:
        """
        reset the Ornstein Uhlenbeck noise, to the initial position
        """
        self.noise_prev = self.initial_noise if self.initial_noise is not None else np.zeros_like(self._mu)

    def __repr__(self) -> str:
        return f'OrnsteinUhlenbeckActionNoise(mu={self._mu}, sigma={self._sigma})'