/* 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 <algorithm>

#include "tensorflow/compiler/tf2xla/shape_util.h"
#include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
#include "tensorflow/compiler/xla/client/xla_builder.h"
#include "tensorflow/core/framework/op_kernel.h"

#include "tensorflow/compiler/tf2xla/type_util.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "tensorflow/core/framework/kernel_def_builder.h"

namespace tensorflow {
namespace {

class DynamicUpdateSliceOp : public XlaOpKernel {
 public:
  explicit DynamicUpdateSliceOp(OpKernelConstruction* context)
      : XlaOpKernel(context) {}

  void Compile(XlaOpKernelContext* ctx) override {
    DataType index_type = ctx->InputType("indices");
    CHECK(index_type == DT_INT32 || index_type == DT_INT64);

    const TensorShape input_shape = ctx->InputShape("input");
    const TensorShape update_shape = ctx->InputShape("update");
    const TensorShape index_shape = ctx->InputShape("indices");

    OP_REQUIRES(
        ctx,
        TensorShapeUtils::IsVector(index_shape) &&
            index_shape.num_elements() == input_shape.dims(),
        errors::InvalidArgument("index must be a vector with length equal to "
                                "the number of input dimensions"));
    OP_REQUIRES(
        ctx, input_shape.dims() == update_shape.dims(),
        errors::InvalidArgument("input and update must have the same rank,"
                                " input shape is ",
                                input_shape.DebugString(), "; update shape is ",
                                update_shape.DebugString()));

    xla::XlaOp result = xla::DynamicUpdateSlice(
        ctx->Input("input"), ctx->Input("update"), ctx->Input("indices"));
    ctx->SetOutput(0, result);
  }
};

REGISTER_XLA_OP(Name("XlaDynamicUpdateSlice"), DynamicUpdateSliceOp);

class DynamicSliceOp : public XlaOpKernel {
 public:
  explicit DynamicSliceOp(OpKernelConstruction* context)
      : XlaOpKernel(context) {}

  void Compile(XlaOpKernelContext* ctx) override {
    DataType index_type = ctx->InputType("start_indices");
    CHECK(index_type == DT_INT32 || index_type == DT_INT64);
    CHECK(index_type == ctx->InputType("size_indices"));

    const TensorShape input_shape = ctx->InputShape("input");
    const TensorShape start_indices_shape = ctx->InputShape("start_indices");
    const TensorShape size_indices_shape = ctx->InputShape("size_indices");

    OP_REQUIRES(ctx,
                TensorShapeUtils::IsVector(start_indices_shape) &&
                    start_indices_shape.num_elements() == input_shape.dims(),
                errors::InvalidArgument(
                    "start_indices must be a vector with length equal to "
                    "input rank, but input rank is ",
                    input_shape.dims(), " and start_indices has shape ",
                    start_indices_shape.DebugString()));
    OP_REQUIRES(ctx,
                TensorShapeUtils::IsVector(size_indices_shape) &&
                    size_indices_shape.num_elements() == input_shape.dims(),
                errors::InvalidArgument(
                    "size_indices must be a vector with length equal to "
                    "input rank, but input rank is ",
                    input_shape.dims(), " and size_indices has shape ",
                    size_indices_shape.DebugString()));

    std::vector<int64> size_indices;
    OP_REQUIRES_OK(
        ctx, ctx->ConstantInputAsIntVector("size_indices", &size_indices));
    xla::XlaOp result = xla::DynamicSlice(
        ctx->Input("input"), ctx->Input("start_indices"), size_indices);
    ctx->SetOutput(0, result);
  }
};

REGISTER_XLA_OP(Name("XlaDynamicSlice").CompileTimeConstInput("size_indices"),
                DynamicSliceOp);

}  // namespace
}  // namespace tensorflow