/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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.
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package org.eclipse.deeplearning4j.dl4jcore.nn.layers.custom.testclasses;

import lombok.Data;
import lombok.EqualsAndHashCode;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonProperty;

import java.util.Collection;
import java.util.Map;

@Data
@EqualsAndHashCode(callSuper = true)
public class CustomLayer extends FeedForwardLayer {

    private final double someCustomParameter;

    public CustomLayer(@JsonProperty("someCustomParameter") double someCustomParameter) {
        this.someCustomParameter = someCustomParameter;
        this.nIn = 10;
        this.nOut = 10;
    }

    @Override
    public org.deeplearning4j.nn.api.Layer instantiate(NeuralNetConfiguration conf,
                                                       Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView,
                                                       boolean initializeParams, DataType networkDataType) {
        CustomLayerImpl ret = new CustomLayerImpl(conf, networkDataType);
        ret.setListeners(trainingListeners);
        ret.setIndex(layerIndex);
        ret.setParamsViewArray(layerParamsView);
        Map<String, INDArray> paramTable = initializer().init(conf, layerParamsView, initializeParams);
        ret.setParamTable(paramTable);
        ret.setConf(conf);
        return ret;
    }

    @Override
    public ParamInitializer initializer() {
        return DefaultParamInitializer.getInstance();
    }

    @Override
    public InputType getOutputType(int layerIndex, InputType inputType) {
        return InputType.feedForward(10);
    }

    @Override
    public void setNIn(InputType inputType, boolean override) {
        //No op
    }

    @Override
    public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
        return null;
    }

    @Override
    public LayerMemoryReport getMemoryReport(InputType inputType) {
        throw new UnsupportedOperationException();
    }
}
