/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

//
// @author raver119@gmail.com, created on 29/10/17.
// @author Yurii Shyrma (iuriish@yahoo.com), changed on 14.05.2018
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_avgpool2d)

#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/convolutions.h>

namespace sd {
namespace ops  {

CUSTOM_OP_IMPL(avgpool2d, 1, 1, false, 0, 10) {

    auto input = INPUT_VARIABLE(0);
    auto output = OUTPUT_NULLIFIED(0);

    // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;

    const auto kH = INT_ARG(0);
    const auto kW = INT_ARG(1);
    const auto sH = INT_ARG(2);
    const auto sW = INT_ARG(3);
          auto pH = INT_ARG(4);
          auto pW = INT_ARG(5);
    const auto dH = INT_ARG(6);
    const auto dW = INT_ARG(7);
    const auto isSameMode = static_cast<bool>(INT_ARG(8));
    const auto extraParam0 = INT_ARG(9);
    const int isNCHW  = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1;       // INT_ARG(10): 0-NCHW, 1-NHWC

    REQUIRE_TRUE(input->rankOf() == 4, 0, "AVGPOOL2D op: input should have rank of 4, but got %i instead", input->rankOf());
    REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D op: dilation must not be zero, but got instead {%i, %i}", dH, dW);

    int oH = 0;
    int oW = 0;

    const int iH = static_cast<int>(isNCHW ? input->sizeAt(2) : input->sizeAt(1));
    const int iW = static_cast<int>(isNCHW ? input->sizeAt(3) : input->sizeAt(2));

    if(!isNCHW) {
        input  = new NDArray(input->permute({0, 3, 1, 2}));                // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
        output = new NDArray(output->permute({0, 3, 1, 2}));               // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
    }

    ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);

    if (isSameMode)
        ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);

    // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor;
    ConvolutionUtils::pooling2d(block, *input, *output, kH, kW, sH, sW, pH, pW, dH, dW, PoolingType::AVG_POOL, extraParam0);

    if(!isNCHW) {
        delete input;
        delete output;
    }

    return Status::OK();
}

DECLARE_SYN(AvgPool2D, avgpool2d);
DECLARE_SYN(AvgPool, avgpool2d);
DECLARE_SYN(avgpool, avgpool2d);

    DECLARE_TYPES(avgpool2d) {
        getOpDescriptor()
                ->setAllowedInputTypes(sd::DataType::ANY)
                ->setAllowedOutputTypes({ALL_FLOATS});
    }

DECLARE_SHAPE_FN(avgpool2d) {

    auto inShape = inputShape->at(0);
    auto shapeOf = shape::shapeOf(inShape);

    // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
    auto argI = *(block.getIArguments());
    const int kH = INT_ARG(0);
    const int kW = INT_ARG(1);
    const int sH = INT_ARG(2);
    const int sW = INT_ARG(3);
    const int pH = INT_ARG(4);
    const int pW = INT_ARG(5);
    const int dH = INT_ARG(6);
    const int dW = INT_ARG(7);
    const int isSameMode = INT_ARG(8);

    const int isNCHW  = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1;       // INT_ARG(10): 0-NCHW, 1-NHWC

    REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D op: dilation must not be zero, but got instead {%i, %i}", dH, dW);

    const int bS = shapeOf[0];
    const int iD = isNCHW ? shapeOf[1] : shapeOf[3];
    const int iH = isNCHW ? shapeOf[2] : shapeOf[1];
    const int iW = isNCHW ? shapeOf[3] : shapeOf[2];

    const char order = shape::order(inShape); // output order must be equal to input order

    // calculate output Height/Width
    int oH, oW;
    ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);

    // allocate memory for new shape
    Nd4jLong newShape[4];
    if (isNCHW) {
        newShape[0] = bS;
        newShape[1] = iD;
        newShape[2] = oH;
        newShape[3] = oW;
    } else {
        newShape[0] = bS;
        newShape[1] = oH;
        newShape[2] = oW;
        newShape[3] = iD;
    }

    return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(ArrayOptions::dataType(inShape), shape::order(inShape), newShape, 4)));
}

    DECLARE_TYPES(avgpool2d_bp) {
        getOpDescriptor()
                ->setAllowedInputTypes(sd::DataType::ANY)
                ->setAllowedOutputTypes({ALL_FLOATS});
    }

//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(avgpool2d_bp, 2, 1, false, 0, 10) {

    auto input = INPUT_VARIABLE(0);                          // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
    auto gradO = INPUT_VARIABLE(1);                          // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
    auto gradI = OUTPUT_NULLIFIED(0);                         // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon

    int kH = INT_ARG(0);                                                        // filter(kernel) height
    int kW = INT_ARG(1);                                                        // filter(kernel) width
    int sH = INT_ARG(2);                                                        // strides height
    int sW = INT_ARG(3);                                                        // strides width
    int pH = INT_ARG(4);                                                        // paddings height
    int pW = INT_ARG(5);                                                        // paddings width
    int dH = INT_ARG(6);                                                        // dilations height
    int dW = INT_ARG(7);                                                        // dilations width
    int isSameMode = INT_ARG(8);                                                // 0-VALID, 1-SAME
    int extraParam0 = INT_ARG(9);
    int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1;         // INT_ARG(10): 0-NCHW, 1-NHWC

    REQUIRE_TRUE(input->rankOf() == 4, 0, "AVGPOOL2D_BP op: input should have rank of 4, but got %i instead", input->rankOf());
    REQUIRE_TRUE(dH != 0 && dW != 0, 0, "AVGPOOL2D_BP op: dilation must not be zero, but got instead {%i, %i}", dH, dW);

    int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
    int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH;       // corresponding indexes
    ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);

    std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,oH,oW,  0,indIOioC,indIiH,indIiH+1});
    std::vector<Nd4jLong> expectedGradIShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,iC,iH,iW,  0,indIOioC,indIiH,indIiH+1});
    REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "AVGPOOL2D_BP op: wrong shape of output's gradients array (next epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
    REQUIRE_TRUE(gradI->isSameShape(expectedGradIShape), 0, "AVGPOOL2D_BP op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str());

    if(!isNCHW) {
        input = new NDArray(input->permute({0, 3, 1, 2}));                                   // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
        gradI = new NDArray(gradI->permute({0, 3, 1, 2}));                                   // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
        gradO = new NDArray(gradO->permute({0, 3, 1, 2}));                                   // [bS, oH, oW, iC] -> [bS, iC, oH, oW]
    }

    if(isSameMode)                       // SAME
        ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);

    // NDArray<T> columnsWrongShape(input->ordering(), {bS, iC, oH, oW, kH, kW}, input->getWorkspace());
    // NDArray<T>* columns = columnsWrongShape.permute({0, 1, 4, 5, 2, 3});                                // [bS, iC, oH, oW, kH, kW] -> [bS, iC, kH, kW, oH, oW]
    // NDArray<T>* gradOVector = gradO->reshape('c', {(int) gradO->lengthOf(), 1});
    // NDArray<T>* columns2d = columnsWrongShape.reshape('c', {bS*iC*oH*oW, kH*kW});

    // columns2d->addiColumnVector(gradOVector);

    // columns->template applyTransform<simdOps::Col2Im<T>>(gradI, std::vector<T>({(T)sH, (T)sW, (T)pH, (T)pW, (T)iH, (T)iW, (T)dH, (T)dW}).data());

    // *gradI /= kH*kW;

    // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - poolingMode; 9 - divisor;
    ConvolutionUtils::pooling2dBP(block, *input, *gradO, *gradI, kH, kW, sH, sW, pH, pW, dH, dW, 1, extraParam0);

    if(!isNCHW) {
        delete input;
        delete gradI;
        delete gradO;
    }

    return Status::OK();
}

DECLARE_SHAPE_FN(avgpool2d_bp) {

    REQUIRE_TRUE(inputShape->at(0)[0] == 4, 0, "AVGPOOL2D_BP op: input array must be 4D, but got %i instead!", inputShape->at(0)[0]);
    REQUIRE_TRUE(inputShape->at(1)[0] == 4, 0, "AVGPOOL2D_BP op: output's gradient array (next epsilon) must be 4D, but got %i instead!", inputShape->at(1)[0]);

    return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(inputShape->at(0), ArrayOptions::dataType(inputShape->at(1)))));
}


}
}

#endif