from tensorflow.keras import backend as K
DEFAULT_NITER_BJORCK = 15
DEFAULT_NITER_SPECTRAL = 3
DEFAULT_NITER_SPECTRAL_INIT = 10

def bjorck_normalization(w, niter=DEFAULT_NITER_BJORCK):
    """
    apply Bjorck normalization on w.
    Args:
        w: weight to normalize, in order to work properly, we must have
            max_eigenval(w) ~= 1
        niter: number of iterations
    Returns:
        the orthonormal weights
    """
    for i in range(niter):
        # W = tf.Print(W,[tf.shape(W)])
        w = 1.5 * w - 0.5 * K.dot(w, K.dot(K.transpose(w), w))
    return w

def _power_iteration(w, u, niter=DEFAULT_NITER_SPECTRAL):
    """
    Internal function that performs the power iteration algorithm.
    Args:
        w: weights matrix that we want to find eigen vector
        u: initialization of the eigen vector
        niter: number of iteration, must be greater than 0
    Returns:
         u and v corresponding to the maximum eigenvalue
    """
    _u = u
    for i in range(niter):
        _v = K.l2_normalize(K.dot(_u, K.transpose(w)))
        _u = K.l2_normalize(K.dot(_v, w))
    return _u, _v

def spectral_normalization(kernel, u=None, niter=DEFAULT_NITER_SPECTRAL):
    """
    Normalize the kernel to have it's max eigenvalue == 1.
    Args:
        kernel: the kernel to normalize
        u: initialization for the max eigen vector
        niter: number of iteration
    Returns:
        the normalized kernel w_bar, it's shape, the maximum eigen vector, and the
        maximum eigen value
    """
    W_shape = kernel.shape
    if u is None:
        niter *= 2  # if u was not known increase number of iterations
        u = K.random_normal(shape=tuple([1, W_shape[-1]]))
    # Flatten the Tensor
    W_reshaped = K.reshape(kernel, [-1, W_shape[-1]])
    _u, _v = _power_iteration(W_reshaped, u, niter)
    # Calculate Sigma
    sigma = K.dot(_v, W_reshaped)
    sigma = K.dot(sigma, K.transpose(_u))
    # sigma/=self.kCoefLip
    # normalize it
    W_bar = W_reshaped / sigma
    return W_bar, _u, sigma