/*
 * Copyright (C) 2018 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.
 */

#define LOG_TAG "Operations"

#include "Comparisons.h"

#include <functional>
#include <vector>

#include "IndexedShapeWrapper.h"
#include "OperationResolver.h"
#include "OperationsExecutionUtils.h"

namespace android {
namespace nn {
namespace comparisons {
namespace {

template <typename DataType, typename ComparisonType>
bool compute(const std::function<bool(ComparisonType, ComparisonType)>& func, const DataType* aData,
             const Shape& aShape, const DataType* bData, const Shape& bShape, bool8* outputData,
             const Shape& outputShape) {
    IndexedShapeWrapper aShapeIndexed(aShape);
    IndexedShapeWrapper bShapeIndexed(bShape);
    IndexedShapeWrapper outputShapeIndexed(outputShape);
    std::vector<uint32_t> curIndex(outputShape.dimensions.size(), 0);
    bool lastIndex = false;
    do {
        uint32_t outputFlatIndex;
        NN_RET_CHECK(outputShapeIndexed.indexToFlatIndex(curIndex, &outputFlatIndex));
        uint32_t aFlatIndex;
        NN_RET_CHECK(aShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &aFlatIndex));
        uint32_t bFlatIndex;
        NN_RET_CHECK(bShapeIndexed.broadcastedIndexToFlatIndex(curIndex, &bFlatIndex));

        if (aShape.type == OperandType::TENSOR_QUANT8_ASYMM ||
            aShape.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
            const float realA = (aData[aFlatIndex] - aShape.offset) * aShape.scale;
            const float realB = (bData[bFlatIndex] - bShape.offset) * bShape.scale;
            outputData[outputFlatIndex] = func(realA, realB);
        } else {
            outputData[outputFlatIndex] = func(aData[aFlatIndex], bData[bFlatIndex]);
        }

        NN_RET_CHECK(outputShapeIndexed.nextIndexInplace(&curIndex, &lastIndex));
    } while (!lastIndex);
    return true;
}

template <typename DataType, typename ComparisonType>
bool executeLessTyped(IOperationExecutionContext* context) {
    return compute<DataType, ComparisonType>(
            std::less<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
            context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
            context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
            context->getOutputShape(kOutputTensor));
}

template <typename DataType, typename ComparisonType>
bool executeLessEqualTyped(IOperationExecutionContext* context) {
    return compute<DataType, ComparisonType>(
            std::less_equal<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
            context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
            context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
            context->getOutputShape(kOutputTensor));
}

template <typename DataType, typename ComparisonType>
bool executeEqualTyped(IOperationExecutionContext* context) {
    return compute<DataType, ComparisonType>(
            std::equal_to<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
            context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
            context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
            context->getOutputShape(kOutputTensor));
}

template <typename DataType, typename ComparisonType>
bool executeNotEqualTyped(IOperationExecutionContext* context) {
    return compute<DataType, ComparisonType>(
            std::not_equal_to<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
            context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
            context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
            context->getOutputShape(kOutputTensor));
}

template <typename DataType, typename ComparisonType>
bool executeGreaterEqualTyped(IOperationExecutionContext* context) {
    return compute<DataType, ComparisonType>(
            std::greater_equal<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
            context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
            context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
            context->getOutputShape(kOutputTensor));
}

template <typename DataType, typename ComparisonType>
bool executeGreaterTyped(IOperationExecutionContext* context) {
    return compute<DataType, ComparisonType>(
            std::greater<ComparisonType>(), context->getInputBuffer<DataType>(kInputTensor1),
            context->getInputShape(kInputTensor1), context->getInputBuffer<DataType>(kInputTensor2),
            context->getInputShape(kInputTensor2), context->getOutputBuffer<bool8>(kOutputTensor),
            context->getOutputShape(kOutputTensor));
}

}  // namespace

bool prepare(IOperationExecutionContext* context) {
    Shape input1 = context->getInputShape(kInputTensor1);
    Shape input2 = context->getInputShape(kInputTensor2);
    Shape output = context->getOutputShape(kOutputTensor);
    NN_RET_CHECK(calculateBroadcastedShape(input1, input2, &output));
    return context->setOutputShape(kOutputTensor, output);
}

bool executeLess(IOperationExecutionContext* context) {
    switch (context->getInputType(kInputTensor1)) {
        case OperandType::TENSOR_FLOAT16:
            return executeLessTyped<_Float16, _Float16>(context);
        case OperandType::TENSOR_FLOAT32:
            return executeLessTyped<float, float>(context);
        case OperandType::TENSOR_INT32:
            return executeLessTyped<int32_t, int32_t>(context);
        case OperandType::TENSOR_QUANT8_ASYMM:
            return executeLessTyped<uint8_t, float>(context);
        case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
            return executeLessTyped<int8_t, float>(context);
        case OperandType::TENSOR_BOOL8:
            return executeLessTyped<bool8, bool8>(context);
        default:
            NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
    }
}

bool executeLessEqual(IOperationExecutionContext* context) {
    switch (context->getInputType(kInputTensor1)) {
        case OperandType::TENSOR_FLOAT16:
            return executeLessEqualTyped<_Float16, _Float16>(context);
        case OperandType::TENSOR_FLOAT32:
            return executeLessEqualTyped<float, float>(context);
        case OperandType::TENSOR_INT32:
            return executeLessEqualTyped<int32_t, int32_t>(context);
        case OperandType::TENSOR_QUANT8_ASYMM:
            return executeLessEqualTyped<uint8_t, float>(context);
        case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
            return executeLessEqualTyped<int8_t, float>(context);
        case OperandType::TENSOR_BOOL8:
            return executeLessEqualTyped<bool8, bool8>(context);
        default:
            NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
    }
}

bool executeEqual(IOperationExecutionContext* context) {
    switch (context->getInputType(kInputTensor1)) {
        case OperandType::TENSOR_FLOAT16:
            return executeEqualTyped<_Float16, _Float16>(context);
        case OperandType::TENSOR_FLOAT32:
            return executeEqualTyped<float, float>(context);
        case OperandType::TENSOR_INT32:
            return executeEqualTyped<int32_t, int32_t>(context);
        case OperandType::TENSOR_QUANT8_ASYMM:
            return executeEqualTyped<uint8_t, float>(context);
        case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
            return executeEqualTyped<int8_t, float>(context);
        case OperandType::TENSOR_BOOL8:
            return executeEqualTyped<bool8, bool8>(context);
        default:
            NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
    }
}

bool executeNotEqual(IOperationExecutionContext* context) {
    switch (context->getInputType(kInputTensor1)) {
        case OperandType::TENSOR_FLOAT16:
            return executeNotEqualTyped<_Float16, _Float16>(context);
        case OperandType::TENSOR_FLOAT32:
            return executeNotEqualTyped<float, float>(context);
        case OperandType::TENSOR_INT32:
            return executeNotEqualTyped<int32_t, int32_t>(context);
        case OperandType::TENSOR_QUANT8_ASYMM:
            return executeNotEqualTyped<uint8_t, float>(context);
        case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
            return executeNotEqualTyped<int8_t, float>(context);
        case OperandType::TENSOR_BOOL8:
            return executeNotEqualTyped<bool8, bool8>(context);
        default:
            NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
    }
}

bool executeGreaterEqual(IOperationExecutionContext* context) {
    switch (context->getInputType(kInputTensor1)) {
        case OperandType::TENSOR_FLOAT16:
            return executeGreaterEqualTyped<_Float16, _Float16>(context);
        case OperandType::TENSOR_FLOAT32:
            return executeGreaterEqualTyped<float, float>(context);
        case OperandType::TENSOR_INT32:
            return executeGreaterEqualTyped<int32_t, int32_t>(context);
        case OperandType::TENSOR_QUANT8_ASYMM:
            return executeGreaterEqualTyped<uint8_t, float>(context);
        case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
            return executeGreaterEqualTyped<int8_t, float>(context);
        case OperandType::TENSOR_BOOL8:
            return executeGreaterEqualTyped<bool8, bool8>(context);
        default:
            NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
    }
}

bool executeGreater(IOperationExecutionContext* context) {
    switch (context->getInputType(kInputTensor1)) {
        case OperandType::TENSOR_FLOAT16:
            return executeGreaterTyped<_Float16, _Float16>(context);
        case OperandType::TENSOR_FLOAT32:
            return executeGreaterTyped<float, float>(context);
        case OperandType::TENSOR_INT32:
            return executeGreaterTyped<int32_t, int32_t>(context);
        case OperandType::TENSOR_QUANT8_ASYMM:
            return executeGreaterTyped<uint8_t, float>(context);
        case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
            return executeGreaterTyped<int8_t, float>(context);
        case OperandType::TENSOR_BOOL8:
            return executeGreaterTyped<bool8, bool8>(context);
        default:
            NN_RET_CHECK_FAIL() << "Unsupported tensor type for comparison";
    }
}

}  // namespace comparisons

NN_REGISTER_OPERATION_DEFAULT_VALIDATION(LESS, comparisons::prepare, comparisons::executeLess);
NN_REGISTER_OPERATION_DEFAULT_VALIDATION(LESS_EQUAL, comparisons::prepare,
                                         comparisons::executeLessEqual);
NN_REGISTER_OPERATION_DEFAULT_VALIDATION(EQUAL, comparisons::prepare, comparisons::executeEqual);
NN_REGISTER_OPERATION_DEFAULT_VALIDATION(NOT_EQUAL, comparisons::prepare,
                                         comparisons::executeNotEqual);
NN_REGISTER_OPERATION_DEFAULT_VALIDATION(GREATER_EQUAL, comparisons::prepare,
                                         comparisons::executeGreaterEqual);
NN_REGISTER_OPERATION_DEFAULT_VALIDATION(GREATER, comparisons::prepare,
                                         comparisons::executeGreater);

}  // namespace nn
}  // namespace android