try:
    import numpypy as N
except:
    import numpy as N

def empty_like(a):
    '''
    returns empty array of same shape and type to a.
    Elements of the array may be arbitrary, different from zero.

    Parameters
    ----------
    a : array-like
        Input ndarray (or its subclass), list, typle or number.

    Returns
    -------
    out : ndarray
        ndarray of same shape and type to `a`.

    Examples
    --------
    empty_like(array([24, 20]))
    empty_like(array([[2.0, 4.0],[3,6]]))
    empty_like([[2.0, 4.0],[3,6]])
    '''
    if hasattr(a, 'shape') and hasattr(a, 'dtype'):
        # may be ndarray, matrix, sparse matrix or their subclass
        return N.empty(a.shape, a.dtype)

    if isinstance(a, (list, tuple)):
        tmp = N.asarray(a)
        # we can't use len(), because a may be list of lists or like that
        return N.empty(tmp.shape, tmp.dtype)

    # TODO: if not isscalar - raise bug
    # however, isscalar is unimplemented yet
    #assert type(a) in (float, int), 'this type is unimplemented for empty_like yet'
    return N.empty(1, type(a))


def ones_like(a):
    '''
    returns array of ones with same shape and type to a.
    Elements of the array may be arbitrary, different from zero.

    Parameters
    ----------
    a : array-like
        Input ndarray (or its subclass), list, typle or number.

    Returns
    -------
    out : ndarray
        ndarray of same shape and type to `a`.

    Examples
    --------
    ones_like(array([24, 20]))
    ones_like(array([[2.0, 4.0],[3,6]]))
    ones_like([[2.0, 4.0],[3,6]])
    '''
    if hasattr(a, 'shape') and hasattr(a, 'dtype'):
        # may be ndarray, matrix, sparse matrix or their subclass
        return N.ones(a.shape, a.dtype)

    if isinstance(a, (list, tuple)):
        tmp = N.asarray(a)
        # we can't use len(), because a may be list of lists or like that
        return N.ones(tmp.shape, tmp.dtype)

    # TODO: if not isscalar - raise bug
    #assert type(a) in (float, int), 'this type is unimplemented for ones_like yet'
    return N.ones(1, type(a))

def zeros_like(a):
    '''
    returns array of zeros with same shape and type to a.
    Elements of the array may be arbitrary, different from zero.

    Parameters
    ----------
    a : array-like
        Input ndarray (or its subclass), list, typle or number.

    Returns
    -------
    out : ndarray
        ndarray of same shape and type to `a`.

    Examples
    --------
    zeros_like(array([24, 20]))
    zeros_like(array([[2.0, 4.0],[3,6]]))
    zeros_like([[2.0, 4.0],[3,6]])
    '''
    if hasattr(a, 'shape') and hasattr(a, 'dtype'):
        # may be ndarray, matrix, sparse matrix or their subclass
        return N.zeros(a.shape, a.dtype)

    if isinstance(a, (list, tuple)):
        tmp = N.asarray(a)
        # we can't use len(), because a may be list of lists or like that
        return N.zeros(tmp.shape, tmp.dtype)

    # TODO: if not isscalar - raise bug
    #assert type(a) in (float, int), 'this type is unimplemented for zeros_like yet'
    return N.zeros(1, type(a))


if __name__ == '__main__':
    for a in [N.array([1, 2, 3]), N.array([[2.0, 4.0],[3,6]]),  N.ones((3, 4, 5)), N.zeros((3, 4, 5, 6))]:
        b = empty_like(a)
        assert b.shape == a.shape and a.dtype == b.dtype
        b = ones_like(a)
        assert b.shape == a.shape and a.dtype == b.dtype
        b = zeros_like(a)
        assert b.shape == a.shape and a.dtype == b.dtype
    for a in [1, 1.0]:
        b = empty_like(a)
        # b.dtype is not equal to type(a) for a=1, because b.dtype is int64 while type(a) is int
        assert b.shape == (1, ) #and b.dtype == type(a)
        b = ones_like(a)
        assert b.shape == (1, ) #and b.dtype == type(a)
        b = zeros_like(a)
        assert b.shape == (1, ) #and b.dtype == type(a)
    print('passed')