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 *  * 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.
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 *  *  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
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package org.deeplearning4j.nn.modelimport.keras.layers.pooling;

import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer;
import org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor;

import java.util.Map;

/**
 * Imports a Keras Pooling layer as a DL4J Subsampling layer.
 *
 * @author dave@skymind.io, Max Pumperla
 */
@Slf4j
@Data
@EqualsAndHashCode(callSuper = false)
public class KerasGlobalPooling extends KerasLayer {

    private final int[] dimensions;

    /**
     * Constructor from parsed Keras layer configuration dictionary.
     *
     * @param layerConfig dictionary containing Keras layer configuration.
     * @throws InvalidKerasConfigurationException     Invalid Keras config
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public KerasGlobalPooling(Map<String, Object> layerConfig)
            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
        this(layerConfig, true);
    }

    /**
     * Constructor from parsed Keras layer configuration dictionary.
     *
     * @param layerConfig           dictionary containing Keras layer configuration
     * @param enforceTrainingConfig whether to enforce training-related configuration options
     * @throws InvalidKerasConfigurationException     Invalid Keras config
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public KerasGlobalPooling(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
        super(layerConfig, enforceTrainingConfig);
        this.dimensions = KerasPoolingUtils.mapGlobalPoolingDimensions(this.className, conf, dimOrder);
        GlobalPoolingLayer.Builder builder =
                new GlobalPoolingLayer.Builder(KerasPoolingUtils.mapPoolingType(this.className, conf))
                        .poolingDimensions(dimensions)
                        .collapseDimensions(true) // keras 2 collapses dimensions
                        .name(this.layerName)
                        .dropOut(this.dropout);
        this.layer = builder.build();
        this.vertex = null;
    }

    /**
     * Get DL4J SubsamplingLayer.
     *
     * @return SubsamplingLayer
     */
    public GlobalPoolingLayer getGlobalPoolingLayer() {
        return (GlobalPoolingLayer) this.layer;
    }

    /**
     * Gets appropriate DL4J InputPreProcessor for given InputTypes.
     *
     * @param inputType Array of InputTypes
     * @return DL4J InputPreProcessor
     * @throws InvalidKerasConfigurationException Invalid Keras config
     * @see org.deeplearning4j.nn.conf.InputPreProcessor
     */
    public InputPreProcessor getInputPreprocessor(InputType... inputType) throws InvalidKerasConfigurationException {
        if (inputType.length > 1)
            throw new InvalidKerasConfigurationException(
                    "Keras GlobalPooling layer accepts only one input (received " + inputType.length + ")");
        InputPreProcessor preprocessor;
        if (inputType[0].getType() == InputType.Type.FF && this.dimensions.length == 1) {
            preprocessor = new FeedForwardToRnnPreProcessor();
        } else {
            preprocessor = this.getGlobalPoolingLayer().getPreProcessorForInputType(inputType[0]);
        }
        return preprocessor;
    }

    /**
     * Get layer output type.
     *
     * @param inputType Array of InputTypes
     * @return output type as InputType
     * @throws InvalidKerasConfigurationException Invalid Keras config
     */
    @Override
    public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
        if (inputType.length > 1)
            throw new InvalidKerasConfigurationException(
                    "Keras Subsampling layer accepts only one input (received " + inputType.length + ")");

        /* Check whether layer requires a preprocessor for this InputType. */
        InputPreProcessor preprocessor = getInputPreprocessor(inputType[0]);
        if (preprocessor != null) {
            return this.getGlobalPoolingLayer().getOutputType(-1, preprocessor.getOutputType(inputType[0]));
        }
        return this.getGlobalPoolingLayer().getOutputType(-1, inputType[0]);
    }
}
