from .scatter import Scatter, scatter
from .ffi import index_backward
from .utils import gen_filled_tensor, gen_output


class ScatterMax(Scatter):
    def __init__(self, dim):
        super(ScatterMax, self).__init__('max', dim)

    def save_for_backward_step(self, *data):
        output, index, input, arg = data
        self.save_for_backward(index, arg)

    def backward_step(self, *data):
        grad, index, arg = data
        return index_backward(self.dim, index.data, grad, arg.data)


def scatter_max_(output, index, input, dim=0):
    r"""
    |

    .. image:: https://raw.githubusercontent.com/rusty1s/pytorch_scatter/
            master/docs/source/_figures/max.svg?sanitize=true
        :align: center
        :width: 400px

    |

    Maximizes all values from the :attr:`input` tensor into :attr:`output` at
    the indices specified in the :attr:`index` tensor along an given axis
    :attr:`dim`. If multiple indices reference the same location, their
    **contributions maximize** (`cf.` :meth:`~torch_scatter.scatter_add_`).
    The second return value is the index location in :attr:`input` of each
    maximum value found (argmax).

    For one-dimensional tensors, the operation computes

    .. math::
        \mathrm{output}_i = \max(\mathrm{output}_i, \max_j(\mathrm{input}_j))

    where max is over :math:`j` such that :math:`\mathrm{index}_j = i`.

    Args:
        output (Tensor): The destination tensor
        index (LongTensor): The indices of elements to scatter
        input (Tensor): The source tensor
        dim (int, optional): The axis along which to index

    :rtype: (:class:`Tensor`, :class:`LongTensor`)

    .. testsetup::

        import torch

    .. testcode::

        from torch_scatter import scatter_max_
        input =     torch.Tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
        index = torch.LongTensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])
        output = torch.zeros(2, 6)
        output = scatter_max_(output, index, input, dim=1)
        print(output)

    .. testoutput::

       (
        0  0  4  3  2  0
        2  4  3  0  0  0
       [torch.FloatTensor of size 2x6]
       ,
       -1 -1  3  4  0  1
        1  4  3 -1 -1 -1
       [torch.LongTensor of size 2x6]
       )
    """
    arg = gen_filled_tensor(index, output.size(), fill_value=-1)
    return scatter(ScatterMax, 'max', dim, output, index, input, arg)


def scatter_max(index, input, dim=0, size=None, fill_value=0):
    r"""Maximizes all values from the :attr:`input` tensor at the indices
    specified in the :attr:`index` tensor along an given axis :attr:`dim`
    (`cf.` :meth:`~torch_scatter.scatter_max_` and
    :meth:`~torch_scatter.scatter_add`).

    For one-dimensional tensors, the operation computes

    .. math::
        \mathrm{output}_i = \max(\mathrm{fill\_value},
        \max_j(\mathrm{input}_j))

    where max is over :math:`j` such that :math:`\mathrm{index}_j = i`.

    Args:
        index (LongTensor): The indices of elements to scatter
        input (Tensor): The source tensor
        dim (int, optional): The axis along which to index
        size (int, optional): Output size at dimension :attr:`dim`
        fill_value (int, optional): Initial filling of output tensor

    :rtype: (:class:`Tensor`, :class:`LongTensor`)

    .. testsetup::

        import torch

    .. testcode::

        from torch_scatter import scatter_max
        input =     torch.Tensor([[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]])
        index = torch.LongTensor([[4, 5, 4, 2, 3], [0, 0, 2, 2, 1]])
        output = scatter_max(index, input, dim=1)
        print(output)

    .. testoutput::

       (
        0  0  4  3  2  0
        2  4  3  0  0  0
       [torch.FloatTensor of size 2x6]
       ,
       -1 -1  3  4  0  1
        1  4  3 -1 -1 -1
       [torch.LongTensor of size 2x6]
       )
    """
    output = gen_output(index, input, dim, size, fill_value)
    return scatter_max_(output, index, input, dim)