from ... import backend as T
import warnings

# Author: Jean Kossaifi

# License: BSD 3 clause


def khatri_rao(matrices, weights=None, skip_matrix=None, reverse=False, mask=None):
    """Khatri-Rao product of a list of matrices

        This can be seen as a column-wise kronecker product.
        (see [1]_ for more details).

        If one matrix only is given, that matrix is directly returned.

    Parameters
    ----------
    matrices : 2D-array list
        list of matrices with the same number of columns, i.e.::

            for i in len(matrices):
                matrices[i].shape = (n_i, m)
    
    weights : 1D-array
        array of weights for each rank, of length m, the number of column of the factors
        (i.e. m == factor[i].shape[1] for any factor)

    skip_matrix : None or int, optional, default is None
        if not None, index of a matrix to skip

    reverse : bool, optional
        if True, the order of the matrices is reversed

    Returns
    -------
    khatri_rao_product: matrix of shape ``(prod(n_i), m)``
        where ``prod(n_i) = prod([m.shape[0] for m in matrices])``
        i.e. the product of the number of rows of all the matrices in the product.

    Notes
    -----
    Mathematically:

    .. math::
         \\text{If every matrix } U_k \\text{ is of size } (I_k \\times R),\\\\
         \\text{Then } \\left(U_1 \\bigodot \\cdots \\bigodot U_n \\right) \\text{ is of size } (\\prod_{k=1}^n I_k \\times R)

    A more intuitive but slower implementation is::

        kr_product = np.zeros((n_rows, n_columns))
        for i in range(n_columns):
            cum_prod = matrices[0][:, i]  # Accumulates the khatri-rao product of the i-th columns
            for matrix in matrices[1:]:
                cum_prod = np.einsum('i,j->ij', cum_prod, matrix[:, i]).ravel()
            # the i-th column corresponds to the kronecker product of all the i-th columns of all matrices:
            kr_product[:, i] = cum_prod

        return kr_product


    References
    ----------
    .. [1] T.G.Kolda and B.W.Bader, "Tensor Decompositions and Applications",
       SIAM REVIEW, vol. 51, n. 3, pp. 455-500, 2009.
    """
    if skip_matrix is not None:
        matrices = [matrices[i] for i in range(len(matrices)) if i != skip_matrix]

    # Khatri-rao of only one matrix: just return that matrix
    if len(matrices) == 1:
        return matrices[0]

    if T.ndim(matrices[0]) == 2:
        n_columns = matrices[0].shape[1]
    else:
        n_columns = 1
        matrices = [T.reshape(m, (-1, 1)) for m in matrices]
        warnings.warn('Khatri-rao of a series of vectors instead of matrices. '
                      'Condidering each has a matrix with 1 column.')

    # Optional part, testing whether the matrices have the proper size
    for i, matrix in enumerate(matrices):
        if T.ndim(matrix) != 2:
            raise ValueError('All the matrices must have exactly 2 dimensions!'
                             'Matrix {} has dimension {} != 2.'.format(
                                 i, T.ndim(matrix)))
        if matrix.shape[1] != n_columns:
            raise ValueError('All matrices must have same number of columns!'
                             'Matrix {} has {} columns != {}.'.format(
                                 i, matrix.shape[1], n_columns))

    if reverse:
        matrices = matrices[::-1]
        # Note: we do NOT use .reverse() which would reverse matrices even outside this function
    
    shared_dim = 'a'
    start = ord('b')
    individual_dims = [chr(start + i) for i in range(len(matrices))]
    equation = ','.join(f'{i}{shared_dim}' for i in individual_dims)

    if weights is not None:
        equation += f',{shared_dim}'
        matrices = matrices + [weights]

    if mask is not None:
        equation += ',' + ''.join(individual_dims)
        matrices.append(mask)

    equation += '->' + ''.join(individual_dims) + shared_dim
        
    return T.reshape(T.einsum(equation, *matrices), (-1, n_columns))