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package org.deeplearning4j.nn.conf.layers;

import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import lombok.ToString;
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.util.Convolution1DUtils;
import org.deeplearning4j.util.ConvolutionUtils;
import org.deeplearning4j.util.ValidationUtils;
import org.nd4j.common.util.ArrayUtil;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;

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

@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class Subsampling1DLayer extends SubsamplingLayer {
    /*
     * Currently, we just subclass off the SubsamplingLayer and hard code the "width" dimension to 1.
     * TODO: We will eventually want to NOT subclass off of SubsamplingLayer.
     * This approach treats a multivariate time series with L timesteps and
     * P variables as an L x 1 x P image (L rows high, 1 column wide, P
     * channels deep). The kernel should be H trainingListeners, int layerIndex, INDArray layerParamsView,
                                                       boolean initializeParams, DataType networkDataType) {
        org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer ret =
                        new org.deeplearning4j.nn.layers.convolution.subsampling.Subsampling1DLayer(conf, networkDataType);
        ret.setListeners(trainingListeners);
        ret.setIndex(layerIndex);
        ret.setParamsViewArray(layerParamsView);
        Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
        ret.setParamTable(paramTable);
        ret.setConf(conf);
        return ret;
    }

    @Override
    public InputType getOutputType(int layerIndex, InputType inputType) {
        if (inputType == null || inputType.getType() != InputType.Type.RNN) {
            throw new IllegalStateException("Invalid input for Subsampling1D layer (layer name=\"" + getLayerName()
                            + "\"): Expected RNN input, got " + inputType);
        }
        InputType.InputTypeRecurrent r = (InputType.InputTypeRecurrent) inputType;
        long inputTsLength = r.getTimeSeriesLength();
        long outLength;
        if (inputTsLength < 0) {
            //Probably: user did InputType.recurrent(x) without specifying sequence length
            outLength = -1;
        } else {
            outLength = Convolution1DUtils.getOutputSize(inputTsLength, kernelSize[0], stride[0], padding[0],
                            convolutionMode, dilation[0]);
        }
        return InputType.recurrent(r.getSize(), outLength, r.getFormat());
    }

    @Override
    public void setNIn(InputType inputType, boolean override) {
        //No op: subsampling layer doesn't have nIn value
        if(cnn2dDataFormat == null || override) {
            if(inputType.getType() == InputType.Type.RNN) {
                InputType.InputTypeRecurrent inputTypeConvolutional = (InputType.InputTypeRecurrent) inputType;
                this.cnn2dDataFormat = inputTypeConvolutional.getFormat() == RNNFormat.NCW ? CNN2DFormat.NCHW : CNN2DFormat.NHWC;

            } else if(inputType.getType() == InputType.Type.CNN) {
                InputType.InputTypeConvolutional inputTypeConvolutional = (InputType.InputTypeConvolutional) inputType;
                this.cnn2dDataFormat = inputTypeConvolutional.getFormat();
            }

        }
    }

    @Override
    public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
        if (inputType == null) {
            throw new IllegalStateException("Invalid input for Subsampling1D layer (layer name=\"" + getLayerName()
                            + "\"): input is null");
        }

        return InputTypeUtil.getPreprocessorForInputTypeRnnLayers(inputType, RNNFormat.NCW, getLayerName());
    }

    @Override
    public Subsampling1DLayer clone() {
        Subsampling1DLayer clone = (Subsampling1DLayer) super.clone();

        if (clone.kernelSize != null) {
            clone.kernelSize = clone.kernelSize.clone();
        }
        if (clone.stride != null) {
            clone.stride = clone.stride.clone();
        }
        if (clone.padding != null) {
            clone.padding = clone.padding.clone();
        }
        if (clone.dilation != null) {
            clone.dilation = clone.dilation.clone();
        }
        return clone;
    }

    public static class Builder extends BaseSubsamplingBuilder {

        private static final org.deeplearning4j.nn.conf.layers.PoolingType DEFAULT_POOLING =
                        org.deeplearning4j.nn.conf.layers.PoolingType.MAX;
        private static final int DEFAULT_KERNEL = 2;
        private static final int DEFAULT_STRIDE = 1;
        private static final int DEFAULT_PADDING = 0;

        public Builder(PoolingType poolingType, int kernelSize, int stride) {
            this(poolingType, kernelSize, stride, DEFAULT_PADDING);
        }

        public Builder(PoolingType poolingType, int kernelSize) {
            this(poolingType, kernelSize, DEFAULT_STRIDE, DEFAULT_PADDING);
        }

        public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int kernelSize) {
            this(poolingType, kernelSize, DEFAULT_STRIDE, DEFAULT_PADDING);
        }

        public Builder(int kernelSize, int stride, int padding) {
            this(DEFAULT_POOLING, kernelSize, stride, padding);
        }

        public Builder(int kernelSize, int stride) {
            this(DEFAULT_POOLING, kernelSize, stride, DEFAULT_PADDING);
        }

        public Builder(int kernelSize) {
            this(DEFAULT_POOLING, kernelSize, DEFAULT_STRIDE, DEFAULT_PADDING);
        }

        public Builder(PoolingType poolingType) {
            this(poolingType, DEFAULT_KERNEL, DEFAULT_STRIDE, DEFAULT_PADDING);
        }

        public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType) {
            this(poolingType, DEFAULT_KERNEL, DEFAULT_STRIDE, DEFAULT_PADDING);
        }

        @Override
        protected boolean allowCausal() {
            return true;
        }

        public Builder() {
            this(DEFAULT_POOLING, DEFAULT_KERNEL, DEFAULT_STRIDE, DEFAULT_PADDING);
        }

        public Builder(org.deeplearning4j.nn.conf.layers.PoolingType poolingType, int kernelSize, int stride,
                        int padding) {
            setKernelSize(kernelSize);
            setPadding(padding);
            setStride(stride);
        }

        public Builder(PoolingType poolingType, int kernelSize, int stride, int padding) {
            this.poolingType = poolingType.toPoolingType();
            setKernelSize(kernelSize);
            setStride(stride);
            setPadding(padding);
        }

        @SuppressWarnings("unchecked")
        public Subsampling1DLayer build() {
            if (poolingType == org.deeplearning4j.nn.conf.layers.PoolingType.PNORM && pnorm <= 0) {
                throw new IllegalStateException(
                                "Incorrect Subsampling config: p-norm must be set when using PoolingType.PNORM");
            }

            ConvolutionUtils.validateConvolutionModePadding(convolutionMode, padding);
            ConvolutionUtils.validateCnnKernelStridePadding(kernelSize, stride, padding);

            return new Subsampling1DLayer(this);
        }

        /**
         * Kernel size
         *
         * @param kernelSize kernel size
         */
        public Builder kernelSize(int kernelSize) {
            this.setKernelSize(kernelSize);
            return this;
        }

        /**
         * Stride
         *
         * @param stride stride value
         */
        public Builder stride(int stride) {
            this.setStride(stride);
            return this;
        }

        /**
         * Padding
         *
         * @param padding padding value
         */
        public Builder padding(int padding) {
            this.setPadding(padding);
            return this;
        }

        /**
         * Kernel size
         *
         * @param kernelSize kernel size
         */
        @Override
        public void setKernelSize(int... kernelSize) {
            this.kernelSize[0] = ValidationUtils.validate1NonNegative(kernelSize, "kernelSize")[0];
        }

        /**
         * Stride
         *
         * @param stride stride value
         */
        @Override
        public void setStride(int... stride) {
            this.stride = ConvolutionUtils.getIntConfig(stride,1);
        }

        /**
         * Padding
         *
         * @param padding padding value
         */
        @Override
        public void setPadding(int... padding) {
            this.padding = ConvolutionUtils.getIntConfig(padding,1);
        }
    }
}