/* 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.
==============================================================================*/

#include <functional>
#include <memory>
#include <vector>

#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/fake_input.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/kernels/ops_testutil.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/lib/random/simple_philox.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"

namespace tensorflow {
namespace {

class ScatterNdUpdateOpTest : public OpsTestBase {
 protected:
  void MakeOp(DataType variable_ref_type, DataType index_type) {
    TF_ASSERT_OK(NodeDefBuilder("myop", "ScatterNdUpdate")
                     .Input(FakeInput(variable_ref_type))
                     .Input(FakeInput(index_type))
                     .Input(FakeInput(RemoveRefType(variable_ref_type)))
                     .Finalize(node_def()));
    TF_ASSERT_OK(InitOp());
  }
};

// TODO(simister): Re-enable this once binary size is under control.
// TEST_F(ScatterNdUpdateOpTest, Simple_StringType) {
//   MakeOp(DT_STRING_REF, DT_INT32);
//   AddInputFromArray<tstring>(TensorShape({1}), {"Brain"});
//   AddInputFromArray<int32>(TensorShape({1}), {0});
//   AddInputFromArray<tstring>(TensorShape({1}), {"TensorFlow"});
//   TF_ASSERT_OK(RunOpKernel());
//   // Check the new state of the input
//   Tensor params_tensor = *mutable_input(0).tensor;
//   Tensor expected(allocator(), DT_STRING, TensorShape({1}));
//   test::FillValues<tstring>(&expected, {"TensorFlow"});
//   test::ExpectTensorEqual<tstring>(expected, params_tensor);
// }

// TEST_F(ScatterNdUpdateOpTest, Simple_BoolType) {
//   MakeOp(DT_BOOL_REF, DT_INT32);
//   AddInputFromArray<bool>(TensorShape({1}), {false});
//   AddInputFromArray<int32>(TensorShape({1}), {0});
//   AddInputFromArray<bool>(TensorShape({1}), {true});
//   TF_ASSERT_OK(RunOpKernel());
//   // Check the new state of the input
//   Tensor params_tensor = *mutable_input(0).tensor;
//   Tensor expected(allocator(), DT_BOOL, TensorShape({1}));
//   test::FillValues<bool>(&expected, {true});
//   test::ExpectTensorEqual<bool>(expected, params_tensor);
// }

TEST_F(ScatterNdUpdateOpTest, Simple_TwoD32) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3, 1}), {0, 4, 2});
  AddInputFromArray<float>(TensorShape({3, 3}),
                           {100, 101, 102, 777, 778, 779, 10000, 10001, 10002});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 3}));
  test::FillValues<float>(&expected, {100, 101, 102, 0, 0, 0, 10000, 10001,
                                      10002, 0, 0, 0, 777, 778, 779});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterNdUpdateOpTest, Simple_Two64) {
  MakeOp(DT_FLOAT_REF, DT_INT64);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int64>(TensorShape({3, 1}), {0, 4, 2});
  AddInputFromArray<float>(TensorShape({3, 3}),
                           {100, 101, 102, 777, 778, 779, 10000, 10001, 10002});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({5, 3}));
  test::FillValues<float>(&expected, {100, 101, 102, 0, 0, 0, 10000, 10001,
                                      10002, 0, 0, 0, 777, 778, 779});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

/*TEST_F(ScatterNdUpdateOpTest, Simple_ZeroElements) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({0}), {});
  AddInputFromArray<int32>(TensorShape({0}), {});
  AddInputFromArray<float>(TensorShape({0}), {});
  Status s = RunOpKernel();
  EXPECT_TRUE(StringPiece(s.ToString())
                  .contains("Output must not have 0 elements, got shape: "))
      << s;
}*/

TEST_F(ScatterNdUpdateOpTest, Simple_ZeroD) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5}), {0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({1}), {3});
  AddInputFromArray<float>(TensorShape({1}), {101});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({5}));
  test::FillValues<float>(&expected, {0, 0, 0, 101, 0});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterNdUpdateOpTest, Simple_OneD) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5}), {0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3, 1}), {0, 4, 2});
  AddInputFromArray<float>(TensorShape({3}), {100, 101, 102});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({5}));
  test::FillValues<float>(&expected, {100, 0, 102, 0, 101});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterNdUpdateOpTest, HigherRank) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({8}), {0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({2, 3, 1}), {0, 4, 2, 1, 3, 6});
  AddInputFromArray<float>(TensorShape({2, 3}), {10, 20, 30, 40, 50, 60});
  TF_ASSERT_OK(RunOpKernel());

  // Check the new state of the input
  Tensor params_tensor = *mutable_input(0).tensor;
  Tensor expected(allocator(), DT_FLOAT, TensorShape({8}));
  test::FillValues<float>(&expected, {10, 40, 30, 50, 20, 0, 60, 0});
  test::ExpectTensorEqual<float>(expected, params_tensor);
}

TEST_F(ScatterNdUpdateOpTest, Error_IndexOutOfRange) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3, 1}), {0, 4, 99});
  AddInputFromArray<float>(TensorShape({3, 3}),
                           {100, 101, 102, 777, 778, 779, 10000, 10001, 10002});
  Status s = RunOpKernel();
  EXPECT_TRUE(absl::StrContains(
      s.ToString(), "indices[2] = [99] does not index into shape [5,3]"))
      << s;
}

TEST_F(ScatterNdUpdateOpTest, Error_WrongDimsIndices) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({2, 3}), {0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({1, 3, 1}), {0, 4, 99});
  AddInputFromArray<float>(TensorShape({3, 3}),
                           {100, 101, 102, 777, 778, 779, 10000, 10001, 10002});
  Status s = RunOpKernel();
  EXPECT_TRUE(absl::StrContains(
      s.ToString(),
      "The outermost dimension of updates and indices must match. Got "
      "indices.shape [1,3,1], updates.shape [3,3]"))
      << s;
}

TEST_F(ScatterNdUpdateOpTest, Error_MismatchedParamsAndUpdateDimensions) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3, 1}), {0, 4, 2});
  AddInputFromArray<float>(
      TensorShape({3, 4}),
      {100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004});
  Status s = RunOpKernel();
  EXPECT_TRUE(absl::StrContains(
      s.ToString(), "Must have updates.shape = indices.shape[:batch_dim]"))
      << s;
}

TEST_F(ScatterNdUpdateOpTest, Error_MismatchedIndicesAndUpdateDimensions) {
  MakeOp(DT_FLOAT_REF, DT_INT32);

  // Feed and run
  AddInputFromArray<float>(TensorShape({5, 3}),
                           {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});
  AddInputFromArray<int32>(TensorShape({3, 1}), {0, 4, 2});
  AddInputFromArray<float>(TensorShape({2, 3}),
                           {100, 101, 102, 10000, 10001, 10002});
  Status s = RunOpKernel();
  EXPECT_TRUE(absl::StrContains(
      s.ToString(),
      "The outermost dimension of updates and indices must match."))
      << s;
}

class ScatterNdUpdateBM : public ScatterNdUpdateOpTest {
 public:
  void TestBody() override {}
  void MakeBenchmarkOp(const char* op, DataType index_type) {
    TF_ASSERT_OK(NodeDefBuilder("myop", op)
                     .Input(FakeInput(DT_FLOAT_REF))
                     .Input(FakeInput(index_type))
                     .Input(FakeInput(DT_FLOAT))
                     .Finalize(node_def()));
    TF_CHECK_OK(InitOp());
  }
};

template <typename Index>
static void BM_ScatterNdHelper(int iters, int embedding_size, const char* op) {
  testing::StopTiming();
  const int kRows = 10000000 / embedding_size;
  std::vector<float> values;
  values.reserve(kRows);
  for (int i = 0; i < kRows * embedding_size; i++) {
    values.push_back(i);
  }
  const int kNumUpdates = 1000;
  random::PhiloxRandom philox(301, 17);
  random::SimplePhilox rnd(&philox);
  std::vector<Index> indices;
  std::vector<float> updates;
  for (int i = 0; i < kNumUpdates; i++) {
    indices.push_back(rnd.Uniform(kRows));
    for (int j = 0; j < embedding_size; j++) {
      updates.push_back(i * 10 + j);
    }
  }

  ScatterNdUpdateBM bm;
  bm.MakeBenchmarkOp(op, DataTypeToEnum<Index>::v());
  bm.AddInputFromArray<float>(TensorShape({kRows, embedding_size}), values);
  bm.AddInputFromArray<Index>(TensorShape({kNumUpdates}), indices);
  bm.AddInputFromArray<float>(TensorShape({kNumUpdates, embedding_size}),
                              updates);
  testing::ItemsProcessed((static_cast<int64>(kNumUpdates) * embedding_size) *
                          iters);
  testing::StartTiming();
  while (iters-- > 0) {
    Status s = bm.RunOpKernel();
  }
  testing::StopTiming();
}

static void BM_ScatterNdUpdateInt32(int iters, int embedding_size) {
  BM_ScatterNdHelper<int32>(iters, embedding_size, "ScatterNdUpdate");
}
static void BM_ScatterNdUpdateInt64(int iters, int embedding_size) {
  BM_ScatterNdHelper<int64>(iters, embedding_size, "ScatterNdUpdate");
}

static void BM_ScatterNdAddInt32(int iters, int embedding_size) {
  BM_ScatterNdHelper<int32>(iters, embedding_size, "ScatterNdAdd");
}
static void BM_ScatterNdAddInt64(int iters, int embedding_size) {
  BM_ScatterNdHelper<int64>(iters, embedding_size, "ScatterNdAdd");
}

BENCHMARK(BM_ScatterNdUpdateInt32)
    ->Arg(1)
    ->Arg(10)
    ->Arg(64)
    ->Arg(256)
    ->Arg(1024);
BENCHMARK(BM_ScatterNdUpdateInt64)
    ->Arg(1)
    ->Arg(10)
    ->Arg(64)
    ->Arg(256)
    ->Arg(1024);

BENCHMARK(BM_ScatterNdAddInt32)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);
BENCHMARK(BM_ScatterNdAddInt64)->Arg(1)->Arg(10)->Arg(64)->Arg(256)->Arg(1024);

}  // namespace
}  // namespace tensorflow
