/* Copyright 2021 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 <initializer_list>
#include <memory>
#include <vector>

#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/test_util.h"
#include "tensorflow/lite/schema/schema_generated.h"

namespace tflite {

namespace ops {
namespace builtin {

TfLiteRegistration* Register_CONV_3D_GENERIC_OPT();

}  // namespace builtin
}  // namespace ops

namespace {

using ::testing::ElementsAre;
using ::testing::ElementsAreArray;

class Conv3dOpModel : public SingleOpModel {
 public:
  Conv3dOpModel(const TensorData& input, const TensorData& filter,
                const TensorData& bias, const TensorData& output,
                Padding padding = Padding_VALID, int32_t stride_depth = 1,
                int32_t stride_width = 1, int32_t stride_height = 1,
                ActivationFunctionType activation = ActivationFunctionType_NONE,
                int32_t dilation_depth = 1, int32_t dilation_width = 1,
                int32_t dilation_height = 1) {
    input_ = AddInput(input);
    filter_ = AddInput(filter);
    bias_ = AddInput(bias);
    output_ = AddOutput(output);
    SetBuiltinOp(
        BuiltinOperator_CONV_3D, BuiltinOptions_Conv3DOptions,
        CreateConv3DOptions(builder_, padding, stride_depth, stride_width,
                            stride_height, activation, dilation_depth,
                            dilation_width, dilation_height)
            .Union());
    BuildInterpreter({GetShape(input_), GetShape(filter_), GetShape(bias_)});
  }

  Conv3dOpModel(const TensorData& input, const TensorData& filter,
                const TensorData& output, Padding padding = Padding_VALID,
                int32_t stride_depth = 1, int32_t stride_width = 1,
                int32_t stride_height = 1,
                ActivationFunctionType activation = ActivationFunctionType_NONE,
                int32_t dilation_depth = 1, int32_t dilation_width = 1,
                int32_t dilation_height = 1) {
    input_ = AddInput(input);
    filter_ = AddInput(filter);
    output_ = AddOutput(output);
    SetBuiltinOp(
        BuiltinOperator_CONV_3D, BuiltinOptions_Conv3DOptions,
        CreateConv3DOptions(builder_, padding, stride_depth, stride_width,
                            stride_height, activation, dilation_depth,
                            dilation_width, dilation_height)
            .Union());
    BuildInterpreter({GetShape(input_), GetShape(filter_)});
  }

  void SetFilter(std::vector<float> f) { PopulateTensor(filter_, f); }

  void SetBias(std::initializer_list<float> f) { PopulateTensor(bias_, f); }

  void SetInput(std::vector<float> data) { PopulateTensor(input_, data); }

  std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
  std::vector<int> GetOutputShape() { return GetTensorShape(output_); }

 private:
  int input_;
  int filter_;
  int bias_;
  int output_;
};

class PrepareOnlyConv3dOpModel : public SingleOpModel {
 public:
  PrepareOnlyConv3dOpModel(
      const TensorData& input, const TensorData& filter,
      const TensorData& output, Padding padding = Padding_VALID,
      int32_t stride_depth = 1, int32_t stride_width = 1,
      int32_t stride_height = 1,
      ActivationFunctionType activation = ActivationFunctionType_NONE,
      int32_t dilation_depth = 1, int32_t dilation_width = 1,
      int32_t dilation_height = 1) {
    input_ = AddInput(input);
    filter_ = AddInput(filter);
    output_ = AddOutput(output);
    SetBuiltinOp(
        BuiltinOperator_CONV_3D, BuiltinOptions_Conv3DOptions,
        CreateConv3DOptions(builder_, padding, stride_depth, stride_width,
                            stride_height, activation, dilation_depth,
                            dilation_width, dilation_height)
            .Union());
    resolver_ = std::make_unique<SingleOpResolver>(
        BuiltinOperator_CONV_3D, ops::builtin::Register_CONV_3D_GENERIC_OPT());
    BuildInterpreter({GetShape(input_), GetShape(filter_)},
                     /*num_threads=*/1, /*allow_fp32_relax_to_fp16=*/false,
                     /*apply_delegate=*/false,
                     /*allocate_and_delegate=*/false);
  }

 private:
  int input_;
  int filter_;
  int output_;
};

template <typename T>
std::vector<T> CreateRangeVector(int N) {
  std::vector<T> result;
  for (int i = 0; i < N; ++i) result.push_back(i);
  return result;
}

TEST(Conv3dOpModel, InvalidInputDimsTest) {
  EXPECT_DEATH_IF_SUPPORTED(Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 4, 1}},
                                            {TensorType_FLOAT32, {3, 2, 2, 1}},
                                            {TensorType_FLOAT32, {}}),
                            "input->dims->size != 5");
}

TEST(Conv3dOpModel, InvalidFilterDimsTest) {
  EXPECT_DEATH_IF_SUPPORTED(
      Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 4, 1}},
                      {TensorType_FLOAT32, {3, 2, 2, 1}},
                      {TensorType_FLOAT32, {}}),
      "filter->dims->size != 5");
}

TEST(Conv3dOpModel, MismatchChannelSizeTest) {
  EXPECT_DEATH_IF_SUPPORTED(
      Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 4, 1}},
                      {TensorType_FLOAT32, {1, 3, 2, 2, 2}},
                      {TensorType_FLOAT32, {}}),
      "input->dims->data.4. != filter->dims->data.3.");
}

TEST(Conv3dOpModel, MismatchBiasSizeTest) {
  EXPECT_DEATH_IF_SUPPORTED(
      Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 4, 2}},
                      {TensorType_FLOAT32, {1, 3, 2, 2, 1}},
                      {TensorType_FLOAT32, {2}}, {TensorType_FLOAT32, {}}),
      "NumElements.bias. != SizeOfDimension.filter, 4.");
}

TEST(Conv3dPrepareSecurityTest, RejectsIm2ColDepthOverflow) {
  if (sizeof(void*) <= 4) {
    GTEST_SKIP() << "Interpreter construction overflows before kernel Prepare "
                    "on 32-bit.";
  }
  constexpr int kHugeDim = 46341;
  PrepareOnlyConv3dOpModel m(
      {TensorType_FLOAT32, {1, 1, 1, 1, kHugeDim}},
      {TensorType_FLOAT32, {kHugeDim, 1, 1, kHugeDim, 1}},
      {TensorType_FLOAT32, {}}, Padding_SAME);

  // On non-mobile platforms, need_im2col is always true, so the overflow is
  // detected and the allocation is rejected, resulting in kTfLiteError.
  // On mobile platforms, it goes to a fallback execution path that does not
  // require im2col, thus does not overflow and returns kTfLiteOk.
  if (IsMobilePlatform()) {
    EXPECT_EQ(m.AllocateTensors(), kTfLiteOk);
  } else {
    EXPECT_EQ(m.AllocateTensors(), kTfLiteError);
  }
}

TEST(Conv3dOpModel, SimpleFloat32Test) {
  Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 4, 2}},
                  {TensorType_FLOAT32, {2, 2, 2, 2, 2}},
                  {TensorType_FLOAT32, {}});

  m.SetInput(CreateRangeVector<float>(32));
  m.SetFilter({-1, -1, -1, -1, -1, 1, -1, 1, -1, 1,  1,  1, 1, 1,  -1, -1,
               1,  -1, 1,  1,  1,  1, -1, 1, -1, -1, -1, 1, 1, -1, 1,  -1});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  EXPECT_THAT(m.GetOutputShape(), ElementsAre(1, 1, 1, 3, 2));
  EXPECT_THAT(m.GetOutput(), ElementsAreArray({30, 6, 26, 10, 22, 14}));
}

TEST(Conv3dOpModel, PaddingValidTest) {
  Conv3dOpModel m({TensorType_FLOAT32, {1, 3, 4, 5, 2}},
                  {TensorType_FLOAT32, {2, 2, 2, 2, 2}},
                  {TensorType_FLOAT32, {}});

  m.SetInput(CreateRangeVector<float>(120));
  m.SetFilter({-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1,  1, -1, -1,
               1,  1,  -1, 1,  -1, 1,  -1, 1,  -1, -1, -1, 1, -1, 1, 1,  1});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  EXPECT_THAT(m.GetOutputShape(), ElementsAre(1, 2, 3, 4, 2));
  EXPECT_THAT(
      m.GetOutput(),
      ElementsAreArray({-214, 266, -234, 270, -254, 274, -274, 278, -314, 286,
                        -334, 290, -354, 294, -374, 298, -414, 306, -434, 310,
                        -454, 314, -474, 318, -614, 346, -634, 350, -654, 354,
                        -674, 358, -714, 366, -734, 370, -754, 374, -774, 378,
                        -814, 386, -834, 390, -854, 394, -874, 398}));
}

TEST(Conv3dOpModel, PaddingSameTest) {
  Conv3dOpModel m({TensorType_FLOAT32, {1, 3, 4, 5, 2}},
                  {TensorType_FLOAT32, {2, 2, 2, 2, 2}},
                  {TensorType_FLOAT32, {}}, Padding_SAME);

  m.SetInput(CreateRangeVector<float>(120));
  m.SetFilter({1,  -1, 1,  -1, 1,  -1, -1, 1, 1, -1, -1, 1, 1,  -1, -1, 1,
               -1, 1,  -1, 1,  -1, -1, -1, 1, 1, 1,  1,  1, -1, 1,  -1, 1});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  EXPECT_THAT(m.GetOutputShape(), ElementsAre(1, 3, 4, 5, 2));
  EXPECT_THAT(
      m.GetOutput(),
      ElementsAreArray(
          {-172, 290,  -176, 298,  -180, 306,  -184, 314,  36,   198,  -192,
           330,  -196, 338,  -200, 346,  -204, 354,  56,   218,  -212, 370,
           -216, 378,  -220, 386,  -224, 394,  76,   238,  -226, 82,   -230,
           82,   -234, 82,   -238, 82,   -80,  80,   -252, 450,  -256, 458,
           -260, 466,  -264, 474,  116,  278,  -272, 490,  -276, 498,  -280,
           506,  -284, 514,  136,  298,  -292, 530,  -296, 538,  -300, 546,
           -304, 554,  156,  318,  -306, 82,   -310, 82,   -314, 82,   -318,
           82,   -80,  80,   158,  -158, 162,  -162, 166,  -166, 170,  -170,
           176,  -176, 178,  -178, 182,  -182, 186,  -186, 190,  -190, 196,
           -196, 198,  -198, 202,  -202, 206,  -206, 210,  -210, 216,  -216,
           220,  -220, 224,  -224, 228,  -228, 232,  -232, 237,  -237}));
}

TEST(Conv3dOpModel, StrideTest) {
  Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 3, 4, 2}},
                  {TensorType_FLOAT32, {2, 2, 2, 2, 2}},
                  {TensorType_FLOAT32, {}}, Padding_VALID, /*stride_depth=*/2,
                  /*stride_width=*/2, /*stride_height=*/2);

  m.SetInput(CreateRangeVector<float>(96));
  m.SetFilter({1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1,
               1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  EXPECT_THAT(m.GetOutputShape(), ElementsAre(2, 1, 1, 2, 2));
  EXPECT_THAT(m.GetOutput(), ElementsAreArray({52, 8, 68, 8, 244, 8, 260, 8}));
}

TEST(Conv3dOpModel, StrideAndPaddingSameTest) {
  Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 3, 4, 2}},
                  {TensorType_FLOAT32, {2, 2, 2, 2, 2}},
                  {TensorType_FLOAT32, {}}, Padding_SAME, /*stride_depth=*/2,
                  /*stride_width=*/2, /*stride_height=*/2);

  m.SetInput(CreateRangeVector<float>(96));
  m.SetFilter({-1, 1, -1, 1,  1,  1,  1,  1,  -1, 1, -1, -1, -1, 1,  1,  1,
               1,  1, -1, -1, -1, -1, -1, -1, 1,  1, 1,  -1, -1, -1, -1, 1});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  EXPECT_THAT(m.GetOutputShape(), ElementsAre(2, 1, 2, 2, 2));
  EXPECT_THAT(m.GetOutput(),
              ElementsAreArray({-70, -28, -86, -12, -82, -16, -90, -8, -262,
                                164, -278, 180, -178, 80, -186, 88}));
}

TEST(Conv3dOpModel, DilationTest) {
  Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 3, 4, 2}},
                  {TensorType_FLOAT32, {2, 2, 2, 2, 2}},
                  {TensorType_FLOAT32, {}}, Padding_VALID, /*stride_depth=*/1,
                  /*stride_width=*/1, /*stride_height=*/1,
                  /*activation=*/ActivationFunctionType_NONE,
                  /*dilation_depth=*/1, /*dilation_width=*/1,
                  /*dilation_height=*/2);

  m.SetInput(CreateRangeVector<float>(96));
  m.SetFilter(CreateRangeVector<float>(32));
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  EXPECT_THAT(m.GetOutputShape(), ElementsAre(2, 1, 1, 3, 2));
  EXPECT_THAT(m.GetOutput(),
              ElementsAreArray({7248, 7592, 7728, 8104, 8208, 8616, 18768,
                                19880, 19248, 20392, 19728, 20904}));
}

TEST(Conv3dOpModel, BiasTest) {
  Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 3, 4, 2}},
                  {TensorType_FLOAT32, {2, 2, 2, 2, 2}},
                  {TensorType_FLOAT32, {2}}, {TensorType_FLOAT32, {}},
                  Padding_VALID, /*stride_depth=*/2,
                  /*stride_width=*/2, /*stride_height=*/2);

  m.SetInput(CreateRangeVector<float>(96));
  m.SetFilter({1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1,
               1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1});
  m.SetBias({1, 2});
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  EXPECT_THAT(m.GetOutputShape(), ElementsAre(2, 1, 1, 2, 2));
  EXPECT_THAT(m.GetOutput(),
              ElementsAreArray({53, 10, 69, 10, 245, 10, 261, 10}));
}

TEST(Conv3dOpModel, NoIm2ColTensorTest) {
  Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 2, 4}},
                  {TensorType_FLOAT32, {1, 1, 1, 4, 4}},
                  {TensorType_FLOAT32, {}}, Padding_VALID);

  m.SetInput(CreateRangeVector<float>(32));
  m.SetFilter(CreateRangeVector<float>(16));
  ASSERT_EQ(m.Invoke(), kTfLiteOk);

  EXPECT_THAT(m.GetOutputShape(), ElementsAre(1, 2, 2, 2, 4));
  EXPECT_THAT(
      m.GetOutput(),
      ElementsAreArray({56,  62,  68,  74,  152, 174, 196, 218, 248, 286, 324,
                        362, 344, 398, 452, 506, 440, 510, 580, 650, 536, 622,
                        708, 794, 632, 734, 836, 938, 728, 846, 964, 1082}));
}

}  // namespace
}  // namespace tflite