import numpy as np

class Activations:
    @staticmethod
    def LeakyReLU(x, alpha=0.01):
        return np.where(x > 0, x, alpha * x)

    @staticmethod
    def LeakyReLU_deriv(x, alpha=0.01):
        return np.where(x > 0, 1, alpha)

    @staticmethod
    def InverseLeakyReLU(x, alpha=0.01):
        return np.where(x > 0, x, x / alpha)

    @staticmethod
    def ReLU(x):
        return np.maximum(0, x)

    @staticmethod
    def ReLU_deriv(x):
        return np.where(x > 0, 1, 0)

    @staticmethod
    def InverseReLU(x):
        return np.maximum(0, x)  # Note: This is lossy for negative values

    @staticmethod
    def Sigmoid(x):
        return 1 / (1 + np.exp(-x))

    @staticmethod
    def Sigmoid_deriv(x):
        s = Activations.Sigmoid(x)
        return s * (1 - s)

    @staticmethod
    def InverseSigmoid(x):
        return np.log(x / (1 - x))

    @staticmethod
    def Softmax(x):
        exp_x = np.exp(x - np.max(x, axis=0, keepdims=True))
        return exp_x / np.sum(exp_x, axis=0, keepdims=True)

    @staticmethod
    def InverseSoftmax(x):
        return np.log(x) - np.max(np.log(x))
    
    @classmethod
    def get_function_name(cls, func):
        return func.__name__

    @classmethod
    def get_all_activation_names(cls):
        return [name for name, func in cls.__dict__.items() 
                if callable(func) and not name.startswith("__") and 
                not name.endswith("_deriv") and not name.startswith("Inverse") and 
                not name in ['get_function_name', 'get_all_activation_names']]