/* Copyright 2020 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 <cstdint>
#include <vector>

#define EIGEN_USE_THREADS

#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "xla/hlo/builder/xla_builder.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/op_requires.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {
namespace {

// The Xla kernel to build up the computation for get_item(data, index).
class GetItemXlaOp : public XlaOpKernel {
 public:
  explicit GetItemXlaOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}

  void Compile(XlaOpKernelContext* ctx) override {
    const TensorShape& data_shape = ctx->InputShape(0);
    const TensorShape& index_shape = ctx->InputShape(1);
    OP_REQUIRES(
        ctx, TensorShapeUtils::IsVectorOrHigher(data_shape),
        errors::InvalidArgument("data must be at least 1 dimensional."));
    OP_REQUIRES(ctx, index_shape.dims() == 1 && index_shape.dim_size(0) == 1,
                errors::InvalidArgument("index must be a vector of size 1."));

    // NOTE(pbar) Use Concat to extend the indices to match cl/142279605.
    // This isn't the simplest way to emit the indices, but the code for
    // dynamic slice needs to be able to see that minor dims are const zero.
    auto const_zero = xla::ConstantR0(ctx->builder(), 0);
    std::vector<xla::XlaOp> operands;
    operands.push_back(xla::Reshape(ctx->Input(1), {}));
    for (int i = 1; i < data_shape.dims(); i++) {
      operands.push_back(const_zero);
    }

    std::vector<int64_t> slice_sizes = {1};
    std::vector<int64_t> out_sizes = {};
    for (int i = 1; i < data_shape.dims(); i++) {
      auto size = data_shape.dim_size(i);
      slice_sizes.push_back(size);
      out_sizes.push_back(size);
    }
    // NOTE: DynamicSlice here doesn't raise an error or wraps the index
    // if its out-of-range.
    auto slice = xla::DynamicSlice(ctx->Input(0), operands, slice_sizes);
    // In-order collapse to remove the 1st dim.
    auto reshape = xla::Reshape(slice, out_sizes);
    ctx->SetOutput(0, reshape);
  }
};

REGISTER_XLA_OP(Name("GetItem"), GetItemXlaOp);

}  // namespace
}  // namespace tensorflow
