/*
 * Copyright (C) 2017 The Android Open Source Project
 *
 * 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 <gmock/gmock-matchers.h>
#include <gtest/gtest.h>

#include <vector>

#include "HashtableLookup.h"
#include "NeuralNetworksWrapper.h"

using ::testing::FloatNear;
using ::testing::Matcher;

namespace android {
namespace nn {
namespace wrapper {

namespace {

std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values,
                                           float max_abs_error = 1.e-6) {
    std::vector<Matcher<float>> matchers;
    matchers.reserve(values.size());
    for (const float& v : values) {
        matchers.emplace_back(FloatNear(v, max_abs_error));
    }
    return matchers;
}

}  // namespace

using ::testing::ElementsAreArray;

#define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \
    ACTION(Lookup, int)                          \
    ACTION(Key, int)                             \
    ACTION(Value, float)

// For all output and intermediate states
#define FOR_ALL_OUTPUT_TENSORS(ACTION) \
    ACTION(Output, float)              \
    ACTION(Hits, uint8_t)

class HashtableLookupOpModel {
   public:
    HashtableLookupOpModel(std::initializer_list<uint32_t> lookup_shape,
                           std::initializer_list<uint32_t> key_shape,
                           std::initializer_list<uint32_t> value_shape) {
        auto it_vs = value_shape.begin();
        rows_ = *it_vs++;
        features_ = *it_vs;

        std::vector<uint32_t> inputs;

        // Input and weights
        OperandType LookupTy(Type::TENSOR_INT32, lookup_shape);
        inputs.push_back(model_.addOperand(&LookupTy));

        OperandType KeyTy(Type::TENSOR_INT32, key_shape);
        inputs.push_back(model_.addOperand(&KeyTy));

        OperandType ValueTy(Type::TENSOR_FLOAT32, value_shape);
        inputs.push_back(model_.addOperand(&ValueTy));

        // Output and other intermediate state
        std::vector<uint32_t> outputs;

        std::vector<uint32_t> out_dim(lookup_shape.begin(), lookup_shape.end());
        out_dim.push_back(features_);

        OperandType OutputOpndTy(Type::TENSOR_FLOAT32, out_dim);
        outputs.push_back(model_.addOperand(&OutputOpndTy));

        OperandType HitsOpndTy(Type::TENSOR_QUANT8_ASYMM, lookup_shape, 1.f, 0);
        outputs.push_back(model_.addOperand(&HitsOpndTy));

        auto multiAll = [](const std::vector<uint32_t>& dims) -> uint32_t {
            uint32_t sz = 1;
            for (uint32_t d : dims) {
                sz *= d;
            }
            return sz;
        };

        Value_.insert(Value_.end(), multiAll(value_shape), 0.f);
        Output_.insert(Output_.end(), multiAll(out_dim), 0.f);
        Hits_.insert(Hits_.end(), multiAll(lookup_shape), 0);

        model_.addOperation(ANEURALNETWORKS_HASHTABLE_LOOKUP, inputs, outputs);
        model_.identifyInputsAndOutputs(inputs, outputs);

        model_.finish();
    }

    void Invoke() {
        ASSERT_TRUE(model_.isValid());

        Compilation compilation(&model_);
        compilation.finish();
        Execution execution(&compilation);

#define SetInputOrWeight(X, T)                                               \
    ASSERT_EQ(execution.setInput(HashtableLookup::k##X##Tensor, X##_.data(), \
                                 sizeof(T) * X##_.size()),                   \
              Result::NO_ERROR);

        FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight);

#undef SetInputOrWeight

#define SetOutput(X, T)                                                       \
    ASSERT_EQ(execution.setOutput(HashtableLookup::k##X##Tensor, X##_.data(), \
                                  sizeof(T) * X##_.size()),                   \
              Result::NO_ERROR);

        FOR_ALL_OUTPUT_TENSORS(SetOutput);

#undef SetOutput

        ASSERT_EQ(execution.compute(), Result::NO_ERROR);
    }

#define DefineSetter(X, T) \
    void Set##X(const std::vector<T>& f) { X##_.insert(X##_.end(), f.begin(), f.end()); }

    FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineSetter);

#undef DefineSetter

    void SetHashtableValue(const std::function<float(uint32_t, uint32_t)>& function) {
        for (uint32_t i = 0; i < rows_; i++) {
            for (uint32_t j = 0; j < features_; j++) {
                Value_[i * features_ + j] = function(i, j);
            }
        }
    }

    const std::vector<float>& GetOutput() const { return Output_; }
    const std::vector<uint8_t>& GetHits() const { return Hits_; }

   private:
    Model model_;
    uint32_t rows_;
    uint32_t features_;

#define DefineTensor(X, T) std::vector<T> X##_;

    FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineTensor);
    FOR_ALL_OUTPUT_TENSORS(DefineTensor);

#undef DefineTensor
};

TEST(HashtableLookupOpTest, BlackBoxTest) {
    HashtableLookupOpModel m({4}, {3}, {3, 2});

    m.SetLookup({1234, -292, -11, 0});
    m.SetKey({-11, 0, 1234});
    m.SetHashtableValue([](int i, int j) { return i + j / 10.0f; });

    m.Invoke();

    EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({
                                       2.0, 2.1,  // 2-rd item
                                       0, 0,      // Not found
                                       0.0, 0.1,  // 0-th item
                                       1.0, 1.1,  // 1-st item
                               })));
    EXPECT_EQ(m.GetHits(), std::vector<uint8_t>({
                                   1,
                                   0,
                                   1,
                                   1,
                           }));
}

}  // namespace wrapper
}  // namespace nn
}  // namespace android