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#include "open3d/ml/tensorflow/TensorFlowHelper.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/lib/core/errors.h"

using namespace tensorflow;

REGISTER_OP("Open3DRadiusSearch")
        .Attr("T: {float, double}")
        .Attr("index_dtype: {int32, int64} = DT_INT32")
        .Attr("metric: {'L1', 'L2'} = 'L2'")
        .Attr("ignore_query_point: bool = false")
        .Attr("return_distances: bool = false")
        .Attr("normalize_distances: bool = false")
        .Input("points: T")
        .Input("queries: T")
        .Input("radii: T")
        .Input("points_row_splits: int64")
        .Input("queries_row_splits: int64")
        .Output("neighbors_index: index_dtype")
        .Output("neighbors_row_splits: int64")
        .Output("neighbors_distance: T")
        .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {
            using namespace ::tensorflow::shape_inference;
            using namespace open3d::ml::op_util;
            ShapeHandle points_shape, queries_shape, radii_shape,
                    points_row_splits_shape, queries_row_splits_shape,
                    neighbors_index_shape, neighbors_row_splits_shape,
                    neighbors_distance_shape;

            TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &points_shape));
            TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &queries_shape));
            TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &radii_shape));
            TF_RETURN_IF_ERROR(
                    c->WithRank(c->input(3), 1, &points_row_splits_shape));
            TF_RETURN_IF_ERROR(
                    c->WithRank(c->input(4), 1, &queries_row_splits_shape));

            Dim num_points("num_points");
            Dim num_queries("num_queries");
            Dim batch_size("batch_size");
            CHECK_SHAPE_HANDLE(c, points_shape, num_points, 3);
            CHECK_SHAPE_HANDLE(c, queries_shape, num_queries, 3);
            CHECK_SHAPE_HANDLE(c, radii_shape, num_queries);
            CHECK_SHAPE_HANDLE(c, points_row_splits_shape, batch_size + 1);
            CHECK_SHAPE_HANDLE(c, queries_row_splits_shape, batch_size + 1);

            // we cannot infer the number of neighbors
            neighbors_index_shape = c->MakeShape({c->UnknownDim()});
            c->set_output(0, neighbors_index_shape);

            neighbors_row_splits_shape = MakeShapeHandle(c, num_queries + 1);
            c->set_output(1, neighbors_row_splits_shape);

            bool return_distances;
            TF_RETURN_IF_ERROR(
                    c->GetAttr("return_distances", &return_distances));
            if (return_distances)
                neighbors_distance_shape = c->MakeShape({c->UnknownDim()});
            else
                neighbors_distance_shape = c->MakeShape({0});
            c->set_output(2, neighbors_distance_shape);

            return Status::OK();
        })
        .Doc(R"doc(
Computes the indices and distances of all neighbours within a radius.

This op computes the neighborhood for each query point and returns the indices
of the neighbors and optionally also the distances. Each query point has an
individual search radius. Points and queries can be batched with each batch
item having an individual number of points and queries. The following example
shows a simple search with just a single batch item::

  import open3d.ml.tf as ml3d

  points = [
      [0.1,0.1,0.1],
      [0.5,0.5,0.5],
      [1.7,1.7,1.7],
      [1.8,1.8,1.8],
      [0.3,2.4,1.4]]

  queries = [
      [1.0,1.0,1.0],
      [0.5,2.0,2.0],
      [0.5,2.1,2.2],
  ]

  radii = [1.0,1.0,1.0]

  ml3d.ops.radius_search(points, queries, radii,
                         points_row_splits=[0,5],
                         queries_row_splits=[0,3])
  # returns neighbors_index      = [1, 4, 4]
  #         neighbors_row_splits = [0, 1, 2, 3]
  #         neighbors_distance   = []


  # or with pytorch
  import torch
  import open3d.ml.torch as ml3d

  points = torch.Tensor([
    [0.1,0.1,0.1],
    [0.5,0.5,0.5],
    [1.7,1.7,1.7],
    [1.8,1.8,1.8],
    [0.3,2.4,1.4]])

  queries = torch.Tensor([
      [1.0,1.0,1.0],
      [0.5,2.0,2.0],
      [0.5,2.1,2.1],
  ])

  radii = torch.Tensor([1.0,1.0,1.0])

  ml3d.ops.radius_search(points, queries, radii,
                         points_row_splits=torch.LongTensor([0,5]),
                         queries_row_splits=torch.LongTensor([0,3]))
  # returns neighbors_index      = [1, 4, 4]
  #         neighbors_row_splits = [0, 1, 2, 3]
  #         neighbors_distance   = []


metric: Either L1 or L2. Default is L2

ignore_query_point: If true the points that coincide with the center of the
  search window will be ignored. This excludes the query point if **queries** and
  **points** are the same point cloud.

return_distances: If True the distances for each neighbor will be returned in
  the output tensor **neighbors_distance**.  If False a zero length Tensor will
  be returned for **neighbors_distances**.

normalize_distances: If True the returned distances will be normalized with the
  radii.

points: The 3D positions of the input points.

queries: The 3D positions of the query points.

radii: A vector with the individual radii for each query point.

points_row_splits: 1D vector with the row splits information if points is
  batched. This vector is [0, num_points] if there is only 1 batch item.

queries_row_splits: 1D vector with the row splits information if queries is
  batched. This vector is [0, num_queries] if there is only 1 batch item.

neighbors_index: The compact list of indices of the neighbors. The
  corresponding query point can be inferred from the
  **neighbor_count_row_splits** vector.

neighbors_row_splits: The exclusive prefix sum of the neighbor count for the
  query points including the total neighbor count as the last element. The
  size of this array is the number of queries + 1.

neighbors_distance: Stores the distance to each neighbor if **return_distances**
  is True. The distances are squared only if metric is L2.
  This is a zero length Tensor if **return_distances** is False.

)doc");
