/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// See core/ops/sparse_ops.cc for documentation.
//
// NOTE: the operations in this file only are suitable for execution
// on CPUs.

#define EIGEN_USE_THREADS

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"

#include <numeric>
#include <sstream>
#include <string>
#include <unordered_map>
#include <utility>

#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/util/sparse/sparse_tensor.h"

namespace tensorflow {

// Operator to convert sparse representations to dense.
template <typename T, typename Index>
class SparseToDense : public OpKernel {
 public:
  explicit SparseToDense(OpKernelConstruction* context) : OpKernel(context) {
    OP_REQUIRES_OK(context,
                   context->GetAttr("validate_indices", &validate_indices_));
  }

  void Compute(OpKernelContext* c) override {
    // sparse_indices
    const Tensor& indices = c->input(0);
    OP_REQUIRES(c, indices.dims() <= 2,
                errors::InvalidArgument(
                    "sparse_indices should be a scalar, vector, or matrix, "
                    "got shape ",
                    indices.shape().DebugString()));
    const int64 num_elems = indices.dims() > 0 ? indices.dim_size(0) : 1;
    const int64 num_dims = indices.dims() > 1 ? indices.dim_size(1) : 1;

    // output_shape
    const Tensor& output_shape = c->input(1);
    OP_REQUIRES(
        c, IsLegacyVector(output_shape.shape()),
        errors::InvalidArgument("output_shape should be a vector, got shape ",
                                output_shape.shape().DebugString()));
    OP_REQUIRES(c, output_shape.NumElements() == num_dims,
                errors::InvalidArgument(
                    "output_shape has incorrect number of elements: ",
                    output_shape.NumElements(), " should be: ", num_dims));

    // sparse_values
    const Tensor& sparse_values = c->input(2);
    const int64 num_values = sparse_values.NumElements();
    OP_REQUIRES(c,
                sparse_values.dims() == 0 ||
                    (sparse_values.dims() == 1 && num_values == num_elems),
                errors::InvalidArgument("sparse_values has incorrect shape ",
                                        sparse_values.shape().DebugString(),
                                        ", should be [] or [", num_elems, "]"));

    // default_value
    const Tensor& default_value = c->input(3);
    OP_REQUIRES(c, TensorShapeUtils::IsScalar(default_value.shape()),
                errors::InvalidArgument("default_value should be a scalar."));

    auto output_shape_vec = output_shape.flat<Index>();
    TensorShape output_tensor_shape;
    OP_REQUIRES_OK(c, TensorShapeUtils::MakeShape(output_shape_vec.data(),
                                                  output_shape_vec.size(),
                                                  &output_tensor_shape));
    Tensor* output = nullptr;
    OP_REQUIRES_OK(c, c->allocate_output(0, output_tensor_shape, &output));

    TensorShape ix_shape({num_elems, num_dims});
    Tensor indices_shaped(DT_INT64, ix_shape);
    if (indices.dtype() == DT_INT64) {
      CHECK(indices_shaped.CopyFrom(indices, ix_shape));
    } else {
      indices_shaped.matrix<int64>() =
          indices.shaped<Index, 2>(ix_shape.dim_sizes()).template cast<int64>();
    }

    // If we received a scalar, we'll need to create a new
    // tensor with copies of the values as a vec.
    // TODO(ebrevdo): find a way to avoid this temp allocation.
    Tensor sparse_values_b;

    if (TensorShapeUtils::IsScalar(sparse_values.shape())) {
      OP_REQUIRES_OK(
          c, c->allocate_temp(DataTypeToEnum<T>::value,
                              TensorShape({num_elems}), &sparse_values_b));
      sparse_values_b.vec<T>().setConstant(sparse_values.scalar<T>()());
    } else {
      sparse_values_b = sparse_values;
    }

    // Assume SparseTensor is lexicographically sorted.
    gtl::InlinedVector<int64, 8> order(output->shape().dims());
    std::iota(order.begin(), order.end(), 0);
    sparse::SparseTensor st(indices_shaped, sparse_values_b, output->shape(),
                            order);

    if (validate_indices_) {
      OP_REQUIRES_OK(c, st.IndicesValid());
    }

    output->flat<T>().setConstant(default_value.scalar<T>()());
    OP_REQUIRES(c, st.template ToDense<T>(output, false /* initialize */),
                errors::InvalidArgument(
                    "Indices are not valid (out of bounds).  Shape: ",
                    output->shape().DebugString()));
  }

 private:
  bool validate_indices_;
};

#define REGISTER_KERNELS(type, index_type)                             \
  REGISTER_KERNEL_BUILDER(Name("SparseToDense")                        \
                              .Device(DEVICE_CPU)                      \
                              .TypeConstraint<type>("T")               \
                              .TypeConstraint<index_type>("Tindices"), \
                          SparseToDense<type, index_type>);

#define REGISTER_KERNELS_ALL(type) \
  REGISTER_KERNELS(type, int32);   \
  REGISTER_KERNELS(type, int64);

TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS_ALL);
REGISTER_KERNELS_ALL(bool);
REGISTER_KERNELS_ALL(string);

#undef REGISTER_KERNELS_ALL
#undef REGISTER_KERNELS

}  // namespace tensorflow
