/*
 * Copyright (C) 2019 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 <tensorflow/lite/kernels/internal/reference/reference_ops.h>

#include <algorithm>
#include <functional>
#include <vector>

#include "CpuOperationUtils.h"
#include "OperationResolver.h"
#include "Tracing.h"
#include "nnapi/Validation.h"

namespace android {
namespace nn {

namespace resize_image {

constexpr uint32_t kNumInputs = 4;
constexpr uint32_t kInputTensor = 0;
// The following two scalars represent output shape if INT32, scale if floating point.
constexpr uint32_t kOutputWidthParamScalar = 1;
constexpr uint32_t kOutputHeightParamScalar = 2;
constexpr uint32_t kLayoutScalar = 3;
constexpr uint32_t kNumOptionalInputs = 2;
constexpr uint32_t kAlignCornersScalar = 4;
constexpr uint32_t kHalfPixelCentersScalar = 5;

constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;

namespace {

inline float scaleHalfPixel(const int x, const float scale) {
    return (static_cast<float>(x) + 0.5f) * scale;
}

inline float scaleLegacy(const int x, const float scale) {
    return static_cast<float>(x) * scale;
}

inline float calculateResizeScale(int32_t inSize, int32_t outSize, bool alignCorners) {
    return (alignCorners && outSize > 1) ? (inSize - 1) / static_cast<float>(outSize - 1)
                                         : inSize / static_cast<float>(outSize);
}

template <typename T>
bool resizeNearestNeighbor(const T* inputData, const Shape& inputShape, bool alignCorners,
                           bool halfPixelCenters, T* outputData, const Shape& outputShape) {
    const int batchSize = getSizeOfDimension(inputShape, 0);
    const int inHeight = getSizeOfDimension(inputShape, 1);
    const int inWidth = getSizeOfDimension(inputShape, 2);
    const int channels = getSizeOfDimension(inputShape, 3);
    const int outHeight = getSizeOfDimension(outputShape, 1);
    const int outWidth = getSizeOfDimension(outputShape, 2);

    const float heightScale = calculateResizeScale(inHeight, outHeight, alignCorners);
    const float widthScale = calculateResizeScale(inWidth, outWidth, alignCorners);

    const std::function<float(const int, const float)> scaler =
            halfPixelCenters ? scaleHalfPixel : scaleLegacy;

    for (int b = 0; b < batchSize; ++b) {
        for (int y = 0; y < outHeight; ++y) {
            int inY = std::min((alignCorners) ? static_cast<int>(roundf(scaler(y, heightScale)))
                                              : static_cast<int>(floorf(scaler(y, heightScale))),
                               inHeight - 1);
            if (halfPixelCenters) {
                inY = std::max(static_cast<int>(0), inY);
            }
            for (int x = 0; x < outWidth; ++x) {
                int inX = std::min((alignCorners) ? static_cast<int>(roundf(scaler(x, widthScale)))
                                                  : static_cast<int>(floorf(scaler(x, widthScale))),
                                   inWidth - 1);
                if (halfPixelCenters) {
                    inX = std::max(static_cast<int>(0), inX);
                }
                std::copy_n(inputData + b * inHeight * inWidth * channels +
                                    inY * inWidth * channels + inX * channels,
                            channels,
                            outputData + b * outHeight * outWidth * channels +
                                    y * outWidth * channels + x * channels);
            }
        }
    }

    return true;
}

template <typename T>
bool resizeImageOpNhwc(OperationType opType, const T* inputData, const Shape& inputShape,
                       bool alignCorners, bool halfPixelCenters, T* outputData,
                       const Shape& outputShape) {
    NNTRACE_TRANS("resizeImageOpNhwc");
    int32_t height = static_cast<int32_t>(getSizeOfDimension(outputShape, 1));
    int32_t width = static_cast<int32_t>(getSizeOfDimension(outputShape, 2));
    // We have to fake a tensor here, to satisfy tflite implementation.
    int32_t outDimData[2] = {height, width};
    Shape outDimShape;
    outDimShape.dimensions = {2};

    if (opType == OperationType::RESIZE_BILINEAR) {
        NNTRACE_COMP_SWITCH("optimized_ops::ResizeBilinear");
        tflite::reference_ops::ResizeBilinear(
                {.align_corners = alignCorners, .half_pixel_centers = halfPixelCenters},
                convertShapeToTflshape(inputShape), inputData, convertShapeToTflshape(outDimShape),
                outDimData, convertShapeToTflshape(outputShape), outputData);
    } else if (opType == OperationType::RESIZE_NEAREST_NEIGHBOR) {
        // Align corners = true is not supported.
        NNTRACE_COMP_SWITCH("ResizeNearestNeighbor");
        resizeNearestNeighbor(inputData, inputShape, alignCorners, halfPixelCenters, outputData,
                              outputShape);
    }
    return true;
}

template <>
bool resizeImageOpNhwc<_Float16>(OperationType opType, const _Float16* inputData,
                                 const Shape& inputShape, bool alignCorners, bool halfPixelCenters,
                                 _Float16* outputData, const Shape& outputShape) {
    NNTRACE_TRANS("resizeImageOpNhwcFloat16");
    std::vector<float> inputData_float32(getNumberOfElements(inputShape));
    convertFloat16ToFloat32(inputData, &inputData_float32);
    std::vector<float> outputData_float32(getNumberOfElements(outputShape));
    NN_RET_CHECK(resizeImageOpNhwc(opType, inputData_float32.data(), inputShape, alignCorners,
                                   halfPixelCenters, outputData_float32.data(), outputShape));
    convertFloat32ToFloat16(outputData_float32, outputData);
    return true;
}

template <typename T>
bool resizeImageOp(OperationType opType, const T* inputData, const Shape& inputShape, bool useNchw,
                   bool alignCorners, bool halfPixelCenters, T* outputData,
                   const Shape& outputShape) {
    InputWithLayout<T> input(useNchw);
    OutputWithLayout<T> output(useNchw);
    NN_RET_CHECK(input.initialize(inputData, inputShape));
    NN_RET_CHECK(output.initialize(outputData, outputShape));
    NN_RET_CHECK(resizeImageOpNhwc(opType, input.getNhwcBuffer(), input.getNhwcShape(),
                                   alignCorners, halfPixelCenters, output.getNhwcBuffer(),
                                   output.getNhwcShape()));
    NN_RET_CHECK(output.commit());
    return true;
}

inline bool getOptionalScalar(const IOperationExecutionContext* context, uint32_t scalarIndex) {
    bool scalarValue = false;
    if (context->getNumInputs() > scalarIndex) {
        scalarValue = context->getInputValue<bool>(scalarIndex);
    }
    return scalarValue;
}

}  // namespace

Result<Version> validate(OperationType opType, const IOperationValidationContext* context) {
    const auto numInputs = context->getNumInputs();
    if (opType == OperationType::RESIZE_BILINEAR) {
        NN_RET_CHECK(numInputs >= kNumInputs - 1 && numInputs <= kNumInputs + kNumOptionalInputs);
    } else if (opType == OperationType::RESIZE_NEAREST_NEIGHBOR) {
        NN_RET_CHECK(numInputs >= kNumInputs && numInputs <= kNumInputs + kNumOptionalInputs);
    } else {
        NN_RET_CHECK_FAIL() << "Unsupported operation " << opType;
    }
    NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
    auto inputType = context->getInputType(kInputTensor);
    auto scalarType = context->getInputType(kOutputHeightParamScalar);
    std::vector<OperandType> inExpectedTypes = {inputType, scalarType, scalarType};
    auto minSupportedVersion = Version::ANDROID_OC_MR1;
    NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
                 inputType == OperandType::TENSOR_FLOAT32 ||
                 inputType == OperandType::TENSOR_QUANT8_ASYMM ||
                 inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
            << "Unsupported tensor type for operation " << opType;
    if (inputType == OperandType::TENSOR_FLOAT16 || inputType == OperandType::TENSOR_QUANT8_ASYMM) {
        minSupportedVersion = combineVersions(minSupportedVersion, Version::ANDROID_Q);
    }
    if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
        minSupportedVersion = combineVersions(minSupportedVersion, Version::ANDROID_R);
    }
    if (scalarType != OperandType::INT32) {
        minSupportedVersion = combineVersions(minSupportedVersion, Version::ANDROID_Q);
        if (inputType == OperandType::TENSOR_FLOAT32) {
            NN_RET_CHECK(scalarType == OperandType::FLOAT32);
        } else if (inputType == OperandType::TENSOR_FLOAT16) {
            NN_RET_CHECK(scalarType == OperandType::FLOAT16);
        } else if (inputType == OperandType::TENSOR_QUANT8_ASYMM ||
                   inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
            NN_RET_CHECK(scalarType == OperandType::FLOAT32);
        }
    }
    if (numInputs < kNumInputs) {
        minSupportedVersion = combineVersions(minSupportedVersion, Version::ANDROID_OC_MR1);
    } else if (numInputs == kNumInputs) {
        inExpectedTypes.push_back(OperandType::BOOL);
        minSupportedVersion = combineVersions(minSupportedVersion, Version::ANDROID_Q);
    } else {
        while (inExpectedTypes.size() < numInputs) {
            inExpectedTypes.push_back(OperandType::BOOL);
        }
        minSupportedVersion = combineVersions(minSupportedVersion, Version::ANDROID_R);
    }
    NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
    NN_RET_CHECK(validateOutputTypes(context, {inputType}));
    return minSupportedVersion;
}

bool prepare(OperationType opType, IOperationExecutionContext* context) {
    Shape input = context->getInputShape(kInputTensor);
    NN_RET_CHECK_EQ(getNumberOfDimensions(input), 4);
    const auto numInputs = context->getNumInputs();
    const bool useNchw = getOptionalScalar(context, kLayoutScalar);
    const bool alignCorners = getOptionalScalar(context, kAlignCornersScalar);
    const bool halfPixelCenters = getOptionalScalar(context, kHalfPixelCentersScalar);

    NN_RET_CHECK(!halfPixelCenters || (halfPixelCenters && !alignCorners));

    // Only batches can be zero.
    uint32_t batches = getSizeOfDimension(input, 0);
    uint32_t inHeight = getSizeOfDimension(input, useNchw ? 2 : 1);
    uint32_t inWidth = getSizeOfDimension(input, useNchw ? 3 : 2);
    uint32_t channels = getSizeOfDimension(input, useNchw ? 1 : 3);
    NN_RET_CHECK_GT(inHeight, 0);
    NN_RET_CHECK_GT(inWidth, 0);
    NN_RET_CHECK_GT(channels, 0);

    int32_t height, width;
    auto scalarType = context->getInputType(kOutputHeightParamScalar);
    if (scalarType == OperandType::INT32) {
        height = context->getInputValue<int32_t>(kOutputHeightParamScalar);
        width = context->getInputValue<int32_t>(kOutputWidthParamScalar);
    } else if (scalarType == OperandType::FLOAT32) {
        height = std::floor(static_cast<float>(inHeight) *
                            context->getInputValue<float>(kOutputHeightParamScalar));
        width = std::floor(static_cast<float>(inWidth) *
                           context->getInputValue<float>(kOutputWidthParamScalar));
    } else if (scalarType == OperandType::FLOAT16) {
        height = std::floor(
                static_cast<float>(inHeight) *
                static_cast<float>(context->getInputValue<_Float16>(kOutputHeightParamScalar)));
        width = std::floor(
                static_cast<float>(inWidth) *
                static_cast<float>(context->getInputValue<_Float16>(kOutputWidthParamScalar)));
    } else {
        NN_RET_CHECK_FAIL() << "Unsupported scalar type for operation " << opType;
    }
    NN_RET_CHECK_GT(height, 0);
    NN_RET_CHECK_GT(width, 0);

    Shape output = input;
    if (useNchw) {
        output.dimensions = {batches, channels, (uint32_t)height, (uint32_t)width};
    } else {
        output.dimensions = {batches, (uint32_t)height, (uint32_t)width, channels};
    }
    return context->setOutputShape(kOutputTensor, output);
}

bool execute(OperationType opType, IOperationExecutionContext* context) {
    // Bypass execution in the case of zero-sized input.
    if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;

    const bool useNchw = getOptionalScalar(context, kLayoutScalar);
    const bool alignCorners = getOptionalScalar(context, kAlignCornersScalar);
    const bool halfPixelCenters = getOptionalScalar(context, kHalfPixelCentersScalar);

    switch (context->getInputType(kInputTensor)) {
        case OperandType::TENSOR_FLOAT16:
            return resizeImageOp(opType, context->getInputBuffer<_Float16>(kInputTensor),
                                 context->getInputShape(kInputTensor), useNchw, alignCorners,
                                 halfPixelCenters,
                                 context->getOutputBuffer<_Float16>(kOutputTensor),
                                 context->getOutputShape(kOutputTensor));
        case OperandType::TENSOR_FLOAT32:
            return resizeImageOp(opType, context->getInputBuffer<float>(kInputTensor),
                                 context->getInputShape(kInputTensor), useNchw, alignCorners,
                                 halfPixelCenters, context->getOutputBuffer<float>(kOutputTensor),
                                 context->getOutputShape(kOutputTensor));
        case OperandType::TENSOR_QUANT8_ASYMM:
            return resizeImageOp(opType, context->getInputBuffer<uint8_t>(kInputTensor),
                                 context->getInputShape(kInputTensor), useNchw, alignCorners,
                                 halfPixelCenters, context->getOutputBuffer<uint8_t>(kOutputTensor),
                                 context->getOutputShape(kOutputTensor));
        case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
            return resizeImageOp(opType, context->getInputBuffer<int8_t>(kInputTensor),
                                 context->getInputShape(kInputTensor), useNchw, alignCorners,
                                 halfPixelCenters, context->getOutputBuffer<int8_t>(kOutputTensor),
                                 context->getOutputShape(kOutputTensor));

        default:
            NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << opType;
    }
}

}  // namespace resize_image

using std::placeholders::_1;

NN_REGISTER_OPERATION(RESIZE_BILINEAR, "RESIZE_BILINEAR",
                      std::bind(resize_image::validate, OperationType::RESIZE_BILINEAR, _1),
                      std::bind(resize_image::prepare, OperationType::RESIZE_BILINEAR, _1),
                      std::bind(resize_image::execute, OperationType::RESIZE_BILINEAR, _1),
                      .allowZeroSizedInput = true);

NN_REGISTER_OPERATION(RESIZE_NEAREST_NEIGHBOR, "RESIZE_NEAREST_NEIGHBOR",
                      std::bind(resize_image::validate, OperationType::RESIZE_NEAREST_NEIGHBOR, _1),
                      std::bind(resize_image::prepare, OperationType::RESIZE_NEAREST_NEIGHBOR, _1),
                      std::bind(resize_image::execute, OperationType::RESIZE_NEAREST_NEIGHBOR, _1),
                      .allowZeroSizedInput = true);

}  // namespace nn
}  // namespace android