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package org.deeplearning4j.nn.conf.layers;

import lombok.*;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;

@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public abstract class BaseRecurrentLayer extends FeedForwardLayer {

    protected BaseRecurrentLayer(Builder builder) {
        super(builder);
    }

    @Override
    public InputType getOutputType(int layerIndex, InputType inputType) {
        if (inputType == null || inputType.getType() != InputType.Type.RNN) {
            throw new IllegalStateException("Invalid input for RNN layer (layer index = " + layerIndex +
                    ", layer name = \"" + getLayerName() + "\"): expect RNN input type with size > 0. Got: " + inputType);
        }

        return InputType.recurrent(nOut);
    }

    @Override
    public void setNIn(InputType inputType, boolean override) {
        if (inputType == null || inputType.getType() != InputType.Type.RNN) {
            throw new IllegalStateException("Invalid input for RNN layer (layer name = \"" + getLayerName() + "\"): expect RNN input type with size > 0. Got: " + inputType);
        }

        if (nIn <= 0 || override) {
            InputType.InputTypeRecurrent r = (InputType.InputTypeRecurrent) inputType;
            this.nIn = r.getSize();
        }
    }

    @Override
    public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
        return InputTypeUtil.getPreprocessorForInputTypeRnnLayers(inputType, getLayerName());
    }


    @AllArgsConstructor
    public static abstract class Builder> extends FeedForwardLayer.Builder> {

    }

}