/* Copyright 2018 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.
==============================================================================*/
#include <limits>
#include <memory>
#include <string>
#include <vector>

#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"

namespace tensorflow {

using errors::InvalidArgument;

template <typename SPLITS_TYPE>
class RaggedTensorToSparseOp : public OpKernel {
 public:
  using OpKernel::OpKernel;
  using ConstFlatSplits = typename TTypes<SPLITS_TYPE>::ConstFlat;

  void Compute(OpKernelContext* context) override {
    // Read the `rt_nested_splits` input & convert to Eigen tensors.
    OpInputList rt_nested_splits_in;
    OP_REQUIRES_OK(
        context, context->input_list("rt_nested_splits", &rt_nested_splits_in));
    const int rt_nested_splits_len = rt_nested_splits_in.size();
    DCHECK_GT(rt_nested_splits_len, 0);  // Enforced by REGISTER_OP.
    std::vector<ConstFlatSplits> rt_nested_splits;
    rt_nested_splits.reserve(rt_nested_splits_len);
    for (int i = 0; i < rt_nested_splits_len; ++i) {
      rt_nested_splits.push_back(rt_nested_splits_in[i].flat<SPLITS_TYPE>());
    }

    // Read the `rt_dense_values` input.
    const Tensor& rt_dense_values_in = context->input(rt_nested_splits_len);
    OP_REQUIRES_OK(context,
                   ValidateInputs(rt_nested_splits, rt_dense_values_in));

    // Assemble each value in `sparse_indices` using three parts:
    // - `index_prefix` is the index in dimensions up through the last ragged
    //   dimension.
    // - `index_middle` is the index in the last ragged dimension.
    // - `index_suffix` is the index in the dense value dimensions.
    std::vector<int64> index_prefix(rt_nested_splits_len);
    std::vector<std::vector<int64>> index_suffixes =
        MakeIndexSuffixes(rt_dense_values_in.shape());

    // Allocate the `sparse_indices` output tensor.
    const int64 nvals =
        (rt_nested_splits.back()(rt_nested_splits.back().size() - 1) *
         index_suffixes.size());
    const int64 indices_len = rt_nested_splits_len + rt_dense_values_in.dims();
    Tensor* sparse_indices_out = nullptr;
    OP_REQUIRES_OK(
        context, context->allocate_output(0, TensorShape({nvals, indices_len}),
                                          &sparse_indices_out));
    auto sparse_indices = sparse_indices_out->tensor<int64, 2>();

    // pos[i] is the current position in rt_nested_splits[i].  final_pos is a
    // reference to make it easier to refer to pos[-1].
    std::vector<int64> pos(rt_nested_splits_len);
    int64& final_pos = pos[rt_nested_splits_len - 1];

    // Each iteration through the loop, we increment pos[-1], and add indices
    // for all the values corresponding to
    // rt_nested_splits[-1][pos[-1]:pos[-1]+1].
    int next_index = 0;
    int max_final_pos = rt_nested_splits.back().size() - 1;
    for (; final_pos < max_final_pos; ++final_pos) {
      // Update `pos` to skip over completed elements (i.e., elements where
      // we have already generated indices for all contained values).
      for (int dim = rt_nested_splits_len - 2; dim >= 0; --dim) {
        while (IsCompleted(pos, dim, rt_nested_splits)) {
          pos[dim] += 1;
        }
      }

      // Update index_prefix.
      for (int dim = 0; dim < index_prefix.size(); ++dim) {
        int start = dim > 0 ? rt_nested_splits[dim - 1](pos[dim - 1]) : 0;
        index_prefix[dim] = pos[dim] - start;
      }

      // Get length of the final-ragged-dimension slice.
      const auto& final_splits = rt_nested_splits[rt_nested_splits_len - 1];
      int64 slice_len = final_splits(final_pos + 1) - final_splits(final_pos);

      // Add sparse_indices for this slice.
      for (int64 i = 0; i < slice_len; ++i) {
        for (const auto& index_suffix : index_suffixes) {
          int dim = 0;
          for (int64 index : index_prefix) {  // index_prefix
            sparse_indices(next_index, dim++) = index;
          }
          sparse_indices(next_index, dim++) = i;  // index_middle
          for (int64 index : index_suffix) {      // index_suffix
            sparse_indices(next_index, dim++) = index;
          }
          DCHECK_EQ(dim, indices_len);
          ++next_index;
        }
      }
    }
    DCHECK_EQ(next_index, nvals);

    // Output the `sparse_values` Tensor.
    if (rt_dense_values_in.dims() == 1) {
      context->set_output(1, rt_dense_values_in);
    } else {
      Tensor sparse_values_out(rt_dense_values_in.dtype());
      bool shapes_match = sparse_values_out.CopyFrom(
          rt_dense_values_in, {rt_dense_values_in.NumElements()});
      DCHECK(shapes_match);
      context->set_output(1, sparse_values_out);
    }

    // Output the `sparse_dense_shape` Tensor.
    int64 ndims = rt_nested_splits_len + rt_dense_values_in.dims();
    Tensor* sparse_dense_shape_out = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(2, TensorShape({ndims}),
                                                     &sparse_dense_shape_out));
    auto sparse_dense_shape = sparse_dense_shape_out->vec<int64>();
    sparse_dense_shape(0) = rt_nested_splits_in[0].dim_size(0) - 1;
    for (int dim = 0; dim < rt_nested_splits_len; ++dim) {
      const auto& splits = rt_nested_splits[dim];
      SPLITS_TYPE max_width = 0;
      for (int i = 1; i < splits.size(); ++i) {
        max_width = std::max(max_width, splits(i) - splits(i - 1));
      }
      sparse_dense_shape(dim + 1) = max_width;
    }
    for (int dim = 1; dim < rt_dense_values_in.dims(); ++dim) {
      sparse_dense_shape(dim + rt_nested_splits_len) =
          rt_dense_values_in.dim_size(dim);
    }
  }

 private:
  // Validate `rt_nested_splits` to ensure we don't get any segfaults.
  static ::tensorflow::Status ValidateInputs(
      std::vector<ConstFlatSplits> rt_nested_splits,
      const Tensor& rt_dense_values_in) {
    for (int i = 0; i < rt_nested_splits.size(); ++i) {
      if (rt_nested_splits[i].size() == 0) {
        return InvalidArgument("ragged splits may not be empty.");
      }
      if (rt_nested_splits[i](0) != 0) {
        return InvalidArgument("First value of ragged splits must be 0.");
      }
      if (i > 0) {
        SPLITS_TYPE last_split =
            rt_nested_splits[i - 1](rt_nested_splits[i - 1].size() - 1);
        if (rt_nested_splits[i].size() != last_split + 1) {
          return InvalidArgument(
              "Final value of ragged splits must match the length "
              "the corresponding ragged values.");
        }
      }
    }
    if (rt_dense_values_in.dim_size(0) !=
        rt_nested_splits.back()(rt_nested_splits.back().size() - 1)) {
      return InvalidArgument(
          "Final value of ragged splits must match the length "
          "the corresponding ragged values.");
    }
    return ::tensorflow::Status::OK();
  }

  // Build a list of index suffixes that should be added for each ragged item,
  // to encode the indices of dense values in that ragged item.  This basically
  // just gives a row-major enumeration of all indices in the given tensor
  // shape, ignoring dim[0] (since that's the dimension that iterates over
  // values, and we want index suffixes for a single value).  Example:
  // MakeIndexSuffixes(TensorShape({100, 3, 2})
  //   --> {{0, 0}, {0, 1}, {1, 0}, {1, 1}, {2, 0}, {2, 1}}
  static std::vector<std::vector<int64>> MakeIndexSuffixes(
      const TensorShape& values_shape) {
    std::vector<std::vector<int64>> suffixes{{}};
    for (int dim = 1; dim < values_shape.dims(); ++dim) {
      std::vector<std::vector<int64>> new_suffixes;
      for (const auto& suffix : suffixes) {
        for (int i = 0; i < values_shape.dim_size(dim); ++i) {
          new_suffixes.push_back(suffix);
          new_suffixes.back().push_back(i);
        }
      }
      suffixes.swap(new_suffixes);
    }
    return suffixes;
  }

  // Returns true if the ragged element at pos[dim] is "completed".  A ragged
  // element is completed if we have already generated indices for all of its
  // values.
  static bool IsCompleted(
      const std::vector<int64>& pos, int dim,
      const std::vector<ConstFlatSplits>& rt_nested_splits) {
    int64 current_child = pos[dim + 1];
    int64 limit_child = rt_nested_splits[dim](pos[dim] + 1);
    return current_child >= limit_child;
  }
};

REGISTER_KERNEL_BUILDER(Name("RaggedTensorToSparse")
                            .Device(DEVICE_CPU)
                            .TypeConstraint<int32>("Tsplits"),
                        RaggedTensorToSparseOp<int32>);

REGISTER_KERNEL_BUILDER(Name("RaggedTensorToSparse")
                            .Device(DEVICE_CPU)
                            .TypeConstraint<int64>("Tsplits"),
                        RaggedTensorToSparseOp<int64>);

}  // namespace tensorflow
