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/*******************************************************************************
 * 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
 ******************************************************************************/

package org.nd4j.linalg.api.ops.impl.transforms.custom;

import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.factory.Nd4j;

import java.util.Arrays;
import java.util.Collections;
import java.util.List;

/**
 * Bit mask over the ndarrays as to whether
 * the components are greater than or equal or not
 *
 * @author Adam Gibson
 */
public class GreaterThanOrEqual extends BaseDynamicTransformOp {
    public GreaterThanOrEqual() {}

    public GreaterThanOrEqual( SameDiff sameDiff, SDVariable[] args, boolean inPlace) {
        super(sameDiff, args, inPlace);
    }

    public GreaterThanOrEqual( INDArray[] inputs, INDArray[] outputs) {
        super(inputs, outputs);
    }

    public GreaterThanOrEqual(INDArray x, INDArray y, INDArray z){
        this(new INDArray[]{x, y}, new INDArray[]{z});
    }

    @Override
    public int opNum() {
        return 11;
    }

    @Override
    public String opName() {
        return "greater_equal";
    }

    @Override
    public String onnxName() {
        return "GreaterEqual";
    }

    @Override
    public String tensorflowName() {
       return "GreaterEqual";
    }

    @Override
    public List doDiff(List f1) {
        //2 inputs, not continuously differentiable but 0s almost everywhere
        return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes){
        Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got %s", getClass(), dataTypes);
        Preconditions.checkState(dataTypes.get(0) == dataTypes.get(1), "Input datatypes must be same type: got %s", dataTypes);
        return Collections.singletonList(DataType.BOOL);
    }
}