/*******************************************************************************
 * 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 GS <sgazeos@gmail.com>
//

#include <ops/declarable/CustomOperations.h>

namespace nd4j {
    namespace ops {
        CUSTOM_OP_IMPL(relu_layer, 3, 1, false, 0, 0) {
            auto x = INPUT_VARIABLE(0);
            auto w = INPUT_VARIABLE(1);
            auto b = INPUT_VARIABLE(2);

            REQUIRE_TRUE(x->isMatrix(), 0, "relu_layer: x argument should be a 2D tensor, but got rank %i instead!", x->rankOf());
            REQUIRE_TRUE(w->isMatrix(), 0, "relu_layer: weights argument should be a 2D tensor, but got rank %i instead!", w->rankOf());
            REQUIRE_TRUE(b->isVector(), 0, "relu_layer: biases argument should be a 1D tensor, but got rank %i instead!", b->rankOf());
            REQUIRE_TRUE(b->lengthOf() == w->sizeAt(1), 0, "relu_layer: biases array length should match to columns of weights matrix, however got length = %i and columns = %i!", b->lengthOf(), w->sizeAt(1));
            REQUIRE_TRUE(x->sizeAt(1) == w->sizeAt(0), 0, "relu_layer: number of x columns should match to row number of weights matrix, but got x_columns = %i and weights_rows = %i!", 
                x->sizeAt(1), w->sizeAt(0));

            
            auto output = OUTPUT_VARIABLE(0);
            //T bound = (T)0.f;
            //nd4j_printf("Matrix x(%ix%i), Matrix w(%ix%i), b(1x%i)\n", x->sizeAt(0), x->sizeAt(1), w->sizeAt(0), w->sizeAt(1), b->lengthOf());

            nd4j::ops::xw_plus_b op;
            std::unique_ptr<ResultSet> result(op.execute({x, w, b}, {}, {}, {}));
            REQUIRE_TRUE(Status::OK() == result->status(), 0, "relu_layer: xw_plus_b op failed on input data.");

            auto scalar = block.numT() > 0 ? block.getTArguments()->at(0) : 0.0;

            auto xw = result->at(0);
            xw->applyScalar(nd4j::scalar::RELU, scalar, output);

            return Status::OK();
        }

        DECLARE_SHAPE_FN(relu_layer) {
            auto inShape = inputShape->at(0);
            auto weightsShape = inputShape->at(1);
            auto outputShape = ShapeUtils::matrixProductShape(inShape, weightsShape, false, false, ArrayOptions::dataType(inShape), block.getWorkspace());
            
            return SHAPELIST(CONSTANT(outputShape));
        }

        DECLARE_TYPES(relu_layer) {
            getOpDescriptor()
                    ->setAllowedInputTypes(nd4j::DataType::ANY)
//                  ->setAllowedInputTypes(1, {ALL_FLOATS})
                    ->setAllowedOutputTypes({ALL_FLOATS});
        }
    }
}

