/* ******************************************************************************
 *
 *
 * 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.
 *
 *  See the NOTICE file distributed with this work for additional
 *  information regarding copyright ownership.
 * 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 Yurii Shyrma (iuriish@yahoo.com)
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_depthwise_conv2d)

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

namespace sd {
namespace ops {

CUSTOM_OP_IMPL(depthwise_conv2d, 2, 1, false, 0, 9) {
  auto input = INPUT_VARIABLE(0);                               // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
  auto weights = INPUT_VARIABLE(1);                             // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
  auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;  // [oC] = iC*mC

  auto output = OUTPUT_NULLIFIED(0);  // [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)

  REQUIRE_TRUE(input->rankOf() == 4, 0,
               "CUSTOM DEPTHWISECONV2D OP: rank of input array must be equal to 4, but got %i instead !",
               input->rankOf());
  REQUIRE_TRUE(weights->rankOf() == 4, 0,
               "CUSTOM DEPTHWISECONV2D OP: rank of weights array must be equal to 4, but got %i instead !",
               weights->rankOf());

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

  LongType bS, iC, iH, iW, mC, oC, oH, oW;  // batch size, input channels, input height/width, channels multiplier(oC =
                                       // iC*mC), output channels, output height/width
  LongType indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH;  // corresponding indexes
  ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
                                             indIiH, indWiC, indWmC, indWkH, indOoH);
  mC = weights->sizeAt(indWmC);  // channels multiplier

  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
  REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
               "CUSTOM DEPTHWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
  REQUIRE_TRUE(
      output->sizeAt(indIOioC) == iC * mC, 0,
      "CUSTOM DEPTHWISECONV2D OP: the output_channels must be equal to input_channels * channels_multiplier = %i !",
      iC * mC);
  if (bias)
    REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
                 "CUSTOM DEPTHWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got "
                 "%i, %i instead !",
                 oC, bias->rankOf(), bias->lengthOf());

  ConvolutionUtils::depthwiseConv2d(block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode,
                                    isNCHW, wFormat);

  return sd::Status::OK;
}

DECLARE_TYPES(depthwise_conv2d) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(depthwise_conv2d) {
  auto inputShapeInfo = inputShape->at(0);    // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
  auto weightsShapeInfo = inputShape->at(1);  // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
  auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr;  // [oC] = iC*mC

  const int rank = 4;
  REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0,
               "CUSTOM DEPTHWISECONV2D OP: rank of input array must be equal to %i, but got %i instead !", rank,
               inputShapeInfo[0]);
  REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0,
               "CUSTOM DEPTHWISECONV2D OP: rank of weights array must be equal to %i, but got %i instead !", rank,
               weightsShapeInfo[0]);

  LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0)));  // filter(kernel) height
  LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1)));  // filter(kernel) width
  LongType sH = INT_ARG(2);                                                                          // strides height
  LongType sW = INT_ARG(3);                                                                          // strides width
  LongType pH = INT_ARG(4);                                                                          // paddings height
  LongType pW = INT_ARG(5);                                                                          // paddings width
  LongType dH = INT_ARG(6);                                                                          // dilations height
  LongType dW = INT_ARG(7);                                                                          // dilations width
  int isSameMode = INT_ARG(8);                                                                  // 0-VALID, 1-SAME
  int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1;  // INT_ARG(9): 1-NHWC, 0-NCHW
  int wFormat = block.getIArguments()->size() > 10
                    ? INT_ARG(10)
                    : 0;  // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]

  int indIOioC, indIiH, indWmC(0 == wFormat ? 3 : 0);
  if (!isNCHW) {
    indIOioC = 3;
    indIiH = 1;
  } else {
    indIOioC = 1;
    indIiH = 2;
  }

  const LongType bS = shape::sizeAt(inputShapeInfo, static_cast<sd::LongType>(0));           // batch size
  const LongType iH = shape::sizeAt(inputShapeInfo, static_cast<sd::LongType>(indIiH));      // input height
  const LongType iW = shape::sizeAt(inputShapeInfo, static_cast<sd::LongType>(indIiH + 1));  // input width
  const LongType iC = shape::sizeAt(inputShapeInfo, static_cast<sd::LongType>(indIOioC));    // input channels
  const LongType mC = shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(indWmC));    // channels multiplier(oC = iC*mC)
  const LongType oC = iC * mC;                                    // output channels

  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
  REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo),
                                  shape::shapeOf(weightsShapeInfo)),
               0, "DEPTHWISECONV2D OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
               ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
  if (biasShapeInfo)
    REQUIRE_TRUE(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0,
                 "DEPTHWISECONV2D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i "
                 "instead !",
                 oC, shape::rank(biasShapeInfo), shape::length(biasShapeInfo));

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

  sd::LongType* outputShapeInfo = nullptr;
  ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), sd::LongType);

  outputShapeInfo[0] = rank;
  outputShapeInfo[1] = bS;

  if (isNCHW) {
    outputShapeInfo[2] = oC;
    outputShapeInfo[3] = oH;
    outputShapeInfo[4] = oW;
  } else {
    outputShapeInfo[2] = oH;
    outputShapeInfo[3] = oW;
    outputShapeInfo[4] = oC;
  }

  ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));

  return SHAPELIST(CONSTANT(outputShapeInfo));
}

DECLARE_TYPES(depthwise_conv2d_bp) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(depthwise_conv2d_bp, 3, 2, false, 0, 9) {
  auto input = INPUT_VARIABLE(0);                               // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
  auto weights = INPUT_VARIABLE(1);                             // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
  auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr;  // [oC] = [iC*mC]
  auto gradO = block.width() > 3
                   ? INPUT_VARIABLE(3)
                   : INPUT_VARIABLE(2);  // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next

  auto gradI = OUTPUT_NULLIFIED(0);  // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW), epsilon
  auto gradW = OUTPUT_NULLIFIED(1);  // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
  auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr;  // [oC]

  REQUIRE_TRUE(input->rankOf() == 4, 0,
               "CUSTOM DEPTHWISECONV2D_BP OP: rank of input array must be equal to 4, but got %i instead !",
               input->rankOf());
  REQUIRE_TRUE(weights->rankOf() == 4, 0,
               "CUSTOM DEPTHWISECONV2D_BP OP: rank of weights array must be equal to 4, but got %i instead !",
               weights->rankOf());
  REQUIRE_TRUE(gradO->rankOf() == 4, 0,
               "CUSTOM DEPTHWISECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to 4, but "
               "got %i instead !",
               gradO->rankOf());

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

  LongType bS, iC, iH, iW, mC, oC, oH, oW;  // batch size, input channels, input height/width, channels multiplier(oC =
                                       // iC*mC), output channels, output height/width
  LongType indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH;  // corresponding indexes
  ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
                                             indIiH, indWiC, indWmC, indWkH, indOoH);
  mC = weights->sizeAt(indWmC);  // channels multiplier

  LongType trueoH, trueoW;  // correct output height, width
  ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);

  std::vector<sd::LongType> expectedGradOShape =
      ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
  REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
               "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, "
               "but got %s instead !",
               ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
  REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
               "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
  if (bias)
    REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
                 "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but "
                 "got %i, %i instead !",
                 oC, bias->rankOf(), bias->lengthOf());

  ConvolutionUtils::depthwiseConv2dBP(block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW,
                                      dH, dW, isSameMode, isNCHW, wFormat);

  return sd::Status::OK;
}

//////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(depthwise_conv2d_bp) {
  auto inputShapeInfo = inputShape->at(0);
  auto weightsShapeInfo = inputShape->at(1);
  auto biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr;
  auto gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2);

  const LongType rank = 4;
  REQUIRE_TRUE(shape::rank(inputShapeInfo) == rank, 0,
               "CUSTOM DEPTHWISECONV2D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank,
               shape::rank(inputShapeInfo));
  REQUIRE_TRUE(shape::rank(weightsShapeInfo) == rank, 0,
               "CUSTOM DEPTHWISECONV2D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank,
               shape::rank(weightsShapeInfo));
  REQUIRE_TRUE(shape::rank(gradOShapeInfo) == rank, 0,
               "CUSTOM DEPTHWISECONV2D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but "
               "got %i instead !",
               rank, shape::rank(gradOShapeInfo));

  LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(0)));  // filter(kernel) height
  LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<LongType>(shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(1)));  // filter(kernel) width
  LongType sH = INT_ARG(2);                                                                          // strides height
  LongType sW = INT_ARG(3);                                                                          // strides width
  LongType pH = INT_ARG(4);                                                                          // paddings height
  LongType pW = INT_ARG(5);                                                                          // paddings width
  LongType dH = INT_ARG(6);                                                                          // dilations height
  LongType dW = INT_ARG(7);                                                                          // dilations width
  int isSameMode = INT_ARG(8);                                                                  // 0-VALID, 1-SAME
  int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1;  // INT_ARG(9): 1-NHWC, 0-NCHW
  int wFormat = block.getIArguments()->size() > 10
                    ? INT_ARG(10)
                    : 0;  // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]

  int indIOioC, indIiH, indWmC(0 == wFormat ? 3 : 0);
  if (!isNCHW) {
    indIOioC = 3;
    indIiH = 1;
  } else {
    indIOioC = 1;
    indIiH = 2;
  }

  const LongType bS = shape::sizeAt(inputShapeInfo, static_cast<sd::LongType>(0));           // batch size
  const LongType iH = shape::sizeAt(inputShapeInfo, static_cast<sd::LongType>(indIiH));      // input height
  const LongType iW = shape::sizeAt(inputShapeInfo, static_cast<sd::LongType>(indIiH + 1));  // input width
  const LongType iC = shape::sizeAt(inputShapeInfo, static_cast<sd::LongType>(indIOioC));    // input channels
  const LongType mC = shape::sizeAt(weightsShapeInfo, static_cast<sd::LongType>(indWmC));    // channels multiplier(oC = iC*mC)
  const LongType oC = iC * mC;                                    // output channels

  LongType trueoH, trueoW;  // correct output height, width
  ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);

  std::vector<sd::LongType> expectedGradOShape =
      ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indIiH, indIiH + 1});
  std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
  REQUIRE_TRUE(
      shape::shapeEquals(4, expectedGradOShape.data(), shape::rank(gradOShapeInfo), shape::shapeOf(gradOShapeInfo)), 0,
      "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s "
      "instead !",
      ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
  REQUIRE_TRUE(shape::shapeEquals(4, expectedWeightsShape.data(), shape::rank(weightsShapeInfo),
                                  shape::shapeOf(weightsShapeInfo)),
               0, "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(expectedWeightsShape).c_str(),
               ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
  if (biasShapeInfo)
    REQUIRE_TRUE(shape::rank(biasShapeInfo) <= 2 && oC == shape::length(biasShapeInfo), 0,
                 "CUSTOM DEPTHWISECONV2D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but "
                 "got %i, %i instead !",
                 oC, shape::rank(biasShapeInfo), shape::length(biasShapeInfo));

  auto gradIshapeInfo =
      ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
  auto gradWshapeInfo =
      ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());

  if (biasShapeInfo) {
    sd::LongType* gradBshapeInfo =
        ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
    return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
  }

  return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
}

}  // namespace ops
}  // namespace sd
#endif