/* 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 <stdint.h>

#include <vector>

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

namespace tflite {
namespace {

using ::testing::ElementsAreArray;

template <typename T, typename I>
class UniqueOpModel : public SingleOpModel {
 public:
  UniqueOpModel(const TensorData& input, TensorType input_type,
                TensorType index_out_type) {
    input_id_ = AddInput(input);
    output_id_ = AddOutput(input_type);
    output_index_id_ = AddOutput(index_out_type);
    SetBuiltinOp(BuiltinOperator_UNIQUE, BuiltinOptions_UniqueOptions,
                 CreateUniqueOptions(builder_, index_out_type).Union());
    BuildInterpreter({GetShape(input_id_)});
  }

  int input_tensor_id() { return input_id_; }

  std::vector<T> GetOutput() { return ExtractVector<T>(output_id_); }
  std::vector<I> GetIndexesOutput() {
    return ExtractVector<I>(output_index_id_);
  }

 protected:
  int input_id_;
  int output_id_;
  int output_index_id_;
};

class PrepareOnlyUniqueOpModel : public SingleOpModel {
 public:
  PrepareOnlyUniqueOpModel(const TensorData& input, TensorType output_type,
                           TensorType index_tensor_type,
                           TensorType index_out_type) {
    input_id_ = AddInput(input);
    output_id_ = AddOutput(output_type);
    output_index_id_ = AddOutput(index_tensor_type);
    SetBuiltinOp(BuiltinOperator_UNIQUE, BuiltinOptions_UniqueOptions,
                 CreateUniqueOptions(builder_, index_out_type).Union());
    BuildInterpreter({GetShape(input_id_)}, /*num_threads=*/-1,
                     /*allow_fp32_relax_to_fp16=*/false,
                     /*apply_delegate=*/false,
                     /*allocate_and_delegate=*/false);
  }

 protected:
  int input_id_;
  int output_id_;
  int output_index_id_;
};

TEST(UniqueOpModelTest, OneElement) {
  UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {1}},
                                      TensorType_FLOAT32, TensorType_INT32);
  model.PopulateTensor<float>(model.input_tensor_id(), {5});
  ASSERT_EQ(model.Invoke(), kTfLiteOk);
  EXPECT_THAT(model.GetOutput(), ElementsAreArray({5}));
  EXPECT_THAT(model.GetIndexesOutput(), ElementsAreArray({0}));
}

TEST(UniqueOpModelTest, MultipleElements_AllUnique) {
  UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {8}},
                                      TensorType_FLOAT32, TensorType_INT32);
  model.PopulateTensor<float>(model.input_tensor_id(),
                              {5, 2, 3, 51, 6, 72, 7, 8});
  ASSERT_EQ(model.Invoke(), kTfLiteOk);
  EXPECT_THAT(model.GetOutput(), ElementsAreArray({5, 2, 3, 51, 6, 72, 7, 8}));
  EXPECT_THAT(model.GetIndexesOutput(),
              ElementsAreArray({0, 1, 2, 3, 4, 5, 6, 7}));
}

TEST(UniqueOpModelTest, MultipleElements_AllDuplicates) {
  UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {7}},
                                      TensorType_FLOAT32, TensorType_INT32);
  model.PopulateTensor<float>(model.input_tensor_id(), {5, 5, 5, 5, 5, 5, 5});
  ASSERT_EQ(model.Invoke(), kTfLiteOk);
  EXPECT_THAT(model.GetOutput(), ElementsAreArray({5}));
  EXPECT_THAT(model.GetIndexesOutput(),
              ElementsAreArray({0, 0, 0, 0, 0, 0, 0}));
}

TEST(UniqueOpModelTest, MultipleElements_SomeDuplicates) {
  UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {7}},
                                      TensorType_FLOAT32, TensorType_INT32);
  model.PopulateTensor<float>(model.input_tensor_id(), {2, 3, 5, 7, 2, 7, 3});
  ASSERT_EQ(model.Invoke(), kTfLiteOk);
  EXPECT_THAT(model.GetOutput(), ElementsAreArray({2, 3, 5, 7}));
  EXPECT_THAT(model.GetIndexesOutput(),
              ElementsAreArray({0, 1, 2, 3, 0, 3, 1}));
}

TEST(UniqueOpModelTest, MultipleElements_RepeatedDuplicates) {
  UniqueOpModel<float, int32_t> model({TensorType_FLOAT32, {6}},
                                      TensorType_FLOAT32, TensorType_INT32);
  model.PopulateTensor<float>(model.input_tensor_id(),
                              {-1, -1, -2, -2, -3, -3});
  ASSERT_EQ(model.Invoke(), kTfLiteOk);
  EXPECT_THAT(model.GetOutput(), ElementsAreArray({-1, -2, -3}));
  EXPECT_THAT(model.GetIndexesOutput(), ElementsAreArray({0, 0, 1, 1, 2, 2}));
}

TEST(UniqueOpModelTest, MultipleElements_SomeDuplicates_IndexInt64) {
  UniqueOpModel<float, int64_t> model({TensorType_FLOAT32, {7}},
                                      TensorType_FLOAT32, TensorType_INT64);
  model.PopulateTensor<float>(model.input_tensor_id(), {2, 3, 5, 7, 2, 7, 3});
  ASSERT_EQ(model.Invoke(), kTfLiteOk);
  EXPECT_THAT(model.GetOutput(), ElementsAreArray({2, 3, 5, 7}));
  EXPECT_THAT(model.GetIndexesOutput(),
              ElementsAreArray({0, 1, 2, 3, 0, 3, 1}));
}

TEST(UniqueOpSecurityTest, MismatchedOutputUniqueType) {
  PrepareOnlyUniqueOpModel model({TensorType_FLOAT32, {7}}, TensorType_INT8,
                                 TensorType_INT32, TensorType_INT32);
  EXPECT_EQ(model.AllocateTensors(), kTfLiteError);
}

TEST(UniqueOpSecurityTest, MismatchedOutputIndexType) {
  PrepareOnlyUniqueOpModel model({TensorType_FLOAT32, {7}}, TensorType_FLOAT32,
                                 TensorType_INT8, TensorType_INT32);
  EXPECT_EQ(model.AllocateTensors(), kTfLiteError);
}

TEST(UniqueOpSecurityTest, InvalidIndexOutType) {
  PrepareOnlyUniqueOpModel model({TensorType_FLOAT32, {7}}, TensorType_FLOAT32,
                                 TensorType_INT8, TensorType_INT8);
  EXPECT_EQ(model.AllocateTensors(), kTfLiteError);
}

}  // namespace
}  // namespace tflite