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

def array_equal(a1, a2):
    """
    True if two arrays have the same shape and elements, False otherwise.

    Parameters
    ----------
    a1, a2 : array_like
        Input arrays.

    Returns
    -------
    b : bool
        Returns True if the arrays are equal.

    See Also
    --------
    allclose: Returns True if two arrays are element-wise equal within a
              tolerance.
    array_equiv: Returns True if input arrays are shape consistent and all
                 elements equal.

    Examples
    --------
    >>> np.array_equal([1, 2], [1, 2])
    True
    >>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
    True
    >>> np.array_equal([1, 2], [1, 2, 3])
    False
    >>> np.array_equal([1, 2], [1, 4])
    False

    """
    try:
        #a1, a2 = asarray(a1), asarray(a2)
        #  N.asarray(v) doesn't work - asarray is unimplemented yet, using walkaround instead.
        # TODO: may be comparison of numpy array with sparse matrix, maybe take care of it?
        # array(sparse matrix) in CPython numpy currently returns array of size one and dtype "object"
        if not isinstance(a1, N.ndarray):
            a1 = N.array(a1)
        if not isinstance(a2, N.ndarray):
            a2 = N.array(a2)
    except:
        return False
    if a1.shape != a2.shape:
        return False

    # CPython numpy version:
    #return bool(logical_and.reduce(equal(a1,a2).ravel()))

    # PyPy version:
    if a1.ndim == 0: # else bug with zero-size array indexation
        return a1 == a2
    Size = a1.size
    a1, a2 = a1.flat, a2.flat
    for i in range(Size):
        if a1[i] != a2[i]: return False
    return True