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

package org.deeplearning4j.nn.conf;


import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.shade.jackson.annotation.JsonTypeInfo;

import java.io.Serializable;

@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "@class")
public interface InputPreProcessor extends Serializable, Cloneable {

    /**
     * Pre preProcess input/activations for a multi layer network
     * @param input the input to pre preProcess
     * @param miniBatchSize Minibatch size
     * @param workspaceMgr Workspace manager
     * @return the processed input. Note that the returned array should be placed in the
     *         {@link org.deeplearning4j.nn.workspace.ArrayType#ACTIVATIONS} workspace via the workspace manager
     */
    INDArray preProcess(INDArray input, int miniBatchSize, LayerWorkspaceMgr workspaceMgr);

    /**Reverse the preProcess during backprop. Process Gradient/epsilons before
     * passing them to the layer below.
     * @param output which is a pair of the gradient and epsilon
     * @param miniBatchSize Minibatch size
     * @param workspaceMgr Workspace manager
     * @return the reverse of the pre preProcess step (if any). Note that the returned array should be
     *         placed in {@link org.deeplearning4j.nn.workspace.ArrayType#ACTIVATION_GRAD} workspace via the
     *         workspace manager
     */
    INDArray backprop(INDArray output, int miniBatchSize, LayerWorkspaceMgr workspaceMgr);

    InputPreProcessor clone();

    /**
     * For a given type of input to this preprocessor, what is the type of the output?
     *
     * @param inputType    Type of input for the preprocessor
     * @return             Type of input after applying the preprocessor
     */
    InputType getOutputType(InputType inputType);


    Pair<INDArray, MaskState> feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState, int minibatchSize);
}
