/*******************************************************************************
 * Copyright (c) 2015-2019 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 Paul Dubs
//

#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_standardize)

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


namespace nd4j {
namespace ops  {

    CONFIGURABLE_OP_IMPL(standardize, 1, 1, true, 0, -2) {

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

        std::vector<int> axis;

        if (block.width() > 1)
            axis = INPUT_VARIABLE(1)->template asVectorT<int>();
        else if (block.numI() > 0)
            axis = *block.getIArguments();

        REQUIRE_TRUE(!axis.empty(), 0, "STANDARDIZE OP: axis has to be non-empty")

        shape::checkDimensions(input->rankOf(), axis);

        auto means = input->reduceAlongDimension(reduce::Mean, axis, true);
        auto stdev = input->varianceAlongDimension(variance::SummaryStatsStandardDeviation, false, axis);
        stdev.reshapei(means.getShapeAsVector());

        input->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), means, *output, false);
        output->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), stdev, *output, false);
        output->applyScalar(nd4j::scalar::ReplaceNans, 0, *output);

        return Status::OK();
    }


    DECLARE_TYPES(standardize) {
        getOpDescriptor()->setAllowedInputTypes(0, {ALL_FLOATS});
        getOpDescriptor()->setAllowedInputTypes(1, {DataType::INT32, DataType::INT64});
        getOpDescriptor()->setAllowedOutputTypes(0, DataType::INHERIT);
    }

    CUSTOM_OP_IMPL(standardize_bp, 2, 1, false, 0, -2) {
        auto input = INPUT_VARIABLE(0);
        auto eps = block.width() == 3 ? INPUT_VARIABLE(2) : INPUT_VARIABLE(1);

        auto output = OUTPUT_VARIABLE(0);
        std::vector<int> axis;

        if (block.width() == 3)
            axis = INPUT_VARIABLE(1)->template asVectorT<int>();
        else if (block.numI() > 0)
            axis = *block.getIArguments();

        REQUIRE_TRUE(!axis.empty(), 0, "STANDARDIZE OP: axis has to be non-empty")


        shape::checkDimensions(input->rankOf(), axis);
        auto longAxis = ArrayUtils::toLongVector(axis);

        auto means = input->reduceAlongDimension(reduce::Mean, axis, true);
        auto stdev = input->varianceAlongDimension(variance::SummaryStatsStandardDeviation, false, axis);
        stdev.reshapei(means.getShapeAsVector());

        eps->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), stdev, *output, false);

        NDArray dldu_sum = -output->reduceAlongDimension(reduce::Sum, axis, true);

        NDArray dldx_u(input->shapeInfo(), false, block.launchContext());
        std::vector<NDArray*> meanBpArgs = {input, &dldu_sum};
        std::vector<NDArray*> meanBpOutput = {&dldx_u};
        std::vector<double> meanBpTArgs = {};
        std::vector<bool> meanBpBArgs = {};

        nd4j::ops::reduce_mean_bp meanBp;
        meanBp.execute(meanBpArgs, meanBpOutput, meanBpTArgs, longAxis, meanBpBArgs);
        *output += dldx_u;

        // (eps * (means - input) / (stdev * stdev))
        NDArray tmp(eps->shapeInfo(), false, block.launchContext());
        means.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Subtract(), *input, tmp, false);
        tmp.applyPairwiseTransform(nd4j::pairwise::Multiply, *eps, tmp);
        stdev.applyPairwiseTransform(nd4j::pairwise::Multiply, stdev, stdev);
        tmp.applyTrueBroadcast(nd4j::BroadcastOpsTuple::Divide(), stdev, tmp, false);

        auto dlds_sum = tmp.reduceAlongDimension(reduce::Sum, axis, true);
        NDArray dldx_s(input->shapeInfo(), false, block.launchContext());
        std::vector<NDArray*> stdevBpArgs = {input, &dlds_sum};
        std::vector<NDArray*> stdevBpOutput = {&dldx_s};
        std::vector<double> stdevBpTArgs = {};
        std::vector<bool> stdevBpBArgs = {};
        nd4j::ops::reduce_stdev_bp stdevBp;
        stdevBp.execute(stdevBpArgs,  stdevBpOutput, stdevBpTArgs, longAxis, stdevBpBArgs);
        *output += dldx_s;

        output->applyScalar(nd4j::scalar::ReplaceNans, 0, *output);

        return Status::OK();
    }

    DECLARE_TYPES(standardize_bp) {
        getOpDescriptor()
                ->setAllowedInputTypes(nd4j::DataType::ANY)
                ->setAllowedOutputTypes({ALL_FLOATS});
    }

    DECLARE_SHAPE_FN(standardize_bp) {
        auto in = inputShape->at(0);
        Nd4jLong *out;
        COPY_SHAPE(in, out);

        return SHAPELIST(CONSTANT(out));
    }

}
}

#endif