import numpy

import cupy
import cupyx.scipy.sparse


def _sparse_frobenius_norm(x):
    if cupy.issubdtype(x.dtype, cupy.complexfloating):
        sqnorm = abs(x).power(2).sum()
    else:
        sqnorm = x.power(2).sum()
    return cupy.sqrt(sqnorm)


def norm(x, ord=None, axis=None):
    """Norm of a cupy.scipy.spmatrix

    This function is able to return one of seven different sparse matrix norms,
    depending on the value of the ``ord`` parameter.

    Args:
        x (sparse matrix) : Input sparse matrix.
        ord (non-zero int, inf, -inf, 'fro', optional) : Order of the norm (see
            table under ``Notes``). inf means numpy's `inf` object.
        axis : (int, 2-tuple of ints, None, optional): If `axis` is an
            integer, it specifies the axis of `x` along which to
            compute the vector norms.  If `axis` is a 2-tuple, it specifies the
            axes that hold 2-D matrices, and the matrix norms of these matrices
            are computed.  If `axis` is None then either a vector norm
            (when `x` is 1-D) or a matrix norm (when `x` is 2-D) is returned.
    Returns:
        ndarray : 0-D or 1-D array or norm(s).

    .. seealso:: :func:`scipy.sparse.linalg.norm`
    """

    if not cupyx.scipy.sparse.issparse(x):
        raise TypeError(("input is not sparse. use cupy.linalg.norm"))

    # Check the default case first and handle it immediately.
    if axis is None and ord in (None, 'fro', 'f'):
        return _sparse_frobenius_norm(x)

    # Some norms require functions that are not implemented for all types.
    x = x.tocsr()

    if axis is None:
        axis = (0, 1)
    elif not isinstance(axis, tuple):
        msg = "'axis' must be None, an integer or a tuple of integers"
        try:
            int_axis = int(axis)
        except TypeError:
            raise TypeError(msg)
        if axis != int_axis:
            raise TypeError(msg)
        axis = (int_axis,)

    nd = 2
    if len(axis) == 2:
        row_axis, col_axis = axis
        if not (-nd <= row_axis < nd and -nd <= col_axis < nd):
            raise ValueError('Invalid axis %r for an array with shape %r' %
                             (axis, x.shape))
        if row_axis % nd == col_axis % nd:
            raise ValueError('Duplicate axes given.')
        if ord == 2:
            raise NotImplementedError
            # return _multi_svd_norm(x, row_axis, col_axis, amax)
        elif ord == -2:
            raise NotImplementedError
            # return _multi_svd_norm(x, row_axis, col_axis, amin)
        elif ord == 1:
            return abs(x).sum(axis=row_axis).max()
        elif ord == numpy.inf:
            return abs(x).sum(axis=col_axis).max()
        elif ord == -1:
            return abs(x).sum(axis=row_axis).min()
        elif ord == -numpy.inf:
            return abs(x).sum(axis=col_axis).min()
        elif ord in (None, 'f', 'fro'):
            # The axis order does not matter for this norm.
            return _sparse_frobenius_norm(x)
        else:
            raise ValueError("Invalid norm order for matrices.")
    elif len(axis) == 1:
        a, = axis
        if not (-nd <= a < nd):
            raise ValueError('Invalid axis %r for an array with shape %r' %
                             (axis, x.shape))
        if ord == numpy.inf:
            return abs(x).max(axis=a).A.ravel()
        elif ord == -numpy.inf:
            return abs(x).min(axis=a).A.ravel()
        elif ord == 0:
            # Zero norm
            return (x != 0).astype(numpy.float32).sum(axis=a).ravel().astype(
                numpy.int_)
        elif ord == 1:
            # special case for speedup
            return abs(x).sum(axis=a).ravel()
        elif ord in (2, None):
            return cupy.sqrt(abs(x).power(2).sum(axis=a)).ravel()
        else:
            try:
                ord + 1
            except TypeError:
                raise ValueError('Invalid norm order for vectors.')
            return cupy.power(abs(x).power(ord).sum(axis=a), 1 / ord).ravel()
    else:
        raise ValueError("Improper number of dimensions to norm.")
