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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.
 *  *
 *  *  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.nd4j.linalg.api.ops.impl.layers.convolution;

import lombok.Builder;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.converters.DifferentialFunctionClassHolder;
import org.nd4j.imports.descriptors.properties.AttributeAdapter;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.descriptors.properties.adapters.*;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv2DConfig;
import org.nd4j.common.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

import java.lang.reflect.Field;
import java.util.*;


@Slf4j
@Getter
@NoArgsConstructor
public class DeConv2DTF extends DynamicCustomOp {

    protected DeConv2DConfig config;

    @Builder(builderMethodName = "sameDiffBuilder")
    public DeConv2DTF(SameDiff sameDiff,
                      SDVariable[] inputs,
                      DeConv2DConfig config) {
        super(sameDiff, inputs);

        this.config = config;
        addArgs();
    }

    public DeConv2DTF(INDArray[] inputs, INDArray[] outputs, DeConv2DConfig config){
        super(inputs, outputs);

        this.config = config;
        addArgs();
    }

    @Override
    public long[] iArgs() {
        if (iArguments.size() == 0)
            addArgs();

        return super.iArgs();
    }

    @Override
    public Map<String, Object> propertiesForFunction() {
        if(config == null && !iArguments.isEmpty()){
            config = DeConv2DConfig.builder()
                    .kH(iArguments.get(0))
                    .kW(iArguments.get(1))
                    .sH(iArguments.get(2))
                    .sW(iArguments.get(3))
                    .pH(iArguments.get(4))
                    .pW(iArguments.get(5))
                    .dH(iArguments.get(6))
                    .dW(iArguments.get(7))
                    .isSameMode(iArguments.get(8) == 1)
                    .dataFormat(iArguments.get(9) == 1 ? DeConv2DConfig.NHWC : Conv2DConfig.NCHW)
                    .build();
        }
        return config.toProperties();
    }

    private void addArgs() {
        addIArgument(config.getKH());
        addIArgument(config.getKW());
        addIArgument(config.getSH());
        addIArgument(config.getSW());
        addIArgument(config.getPH());
        addIArgument(config.getPW());
        addIArgument(config.getDH());
        addIArgument(config.getDW());
        addIArgument(ArrayUtil.fromBoolean(config.isSameMode()));
        addIArgument(config.getDataFormat().equalsIgnoreCase(DeConv2DConfig.NCHW) ? 0 : 1);
    }

    @Override
    public boolean isConfigProperties() {
        return true;
    }

    @Override
    public String configFieldName() {
        return "config";
    }


    @Override
    public Object getValue(Field property) {
        if (config == null) {
            config = DeConv2DConfig.builder().build();
        }

        return config.getValue(property);
    }


    @Override
    public Map<String, Map<String, PropertyMapping>> mappingsForFunction() {
        Map<String, Map<String, PropertyMapping>> ret = new HashMap<>();
        Map<String, PropertyMapping> map = new HashMap<>();
        val strideMapping = PropertyMapping.builder()
                .tfAttrName("strides")
                .onnxAttrName("strides")
                .propertyNames(new String[]{"sH", "sW"})
                .build();

        val kernelMapping = PropertyMapping.builder()
                .propertyNames(new String[]{"kH", "kW"})
                .tfInputPosition(1)
                .onnxAttrName("kernel_shape")
                .build();

        val dilationMapping = PropertyMapping.builder()
                .onnxAttrName("dilations")
                .propertyNames(new String[]{"dW", "dH"})
                .tfAttrName("rates")
                .build();

        val sameMode = PropertyMapping.builder()
                .onnxAttrName("auto_pad")
                .propertyNames(new String[]{"isSameMode"})
                .tfAttrName("padding")
                .build();

        val dataFormat = PropertyMapping.builder()
                .onnxAttrName("data_format")
                .tfAttrName("data_format")
                .propertyNames(new String[]{"dataFormat"})
                .build();


        map.put("sW", strideMapping);
        map.put("sH", strideMapping);
        map.put("kH", kernelMapping);
        map.put("kW", kernelMapping);
        map.put("dW", dilationMapping);
        map.put("dH", dilationMapping);
        map.put("isSameMode", sameMode);
        map.put("dataFormat", dataFormat);

        ret.put(tensorflowName(), map);
        return ret;
    }

    @Override
    public Map<String, Map<String, AttributeAdapter>> attributeAdaptersForFunction() {
        Map<String, Map<String, AttributeAdapter>> ret = new HashMap<>();
        Map<String, AttributeAdapter> tfMappings = new LinkedHashMap<>();
        val fields = DifferentialFunctionClassHolder.getInstance().getFieldsForFunction(this);


        //TF uses [kH, kW, outC, inC] always for weights
        tfMappings.put("kH", new NDArrayShapeAdapter(0));
        tfMappings.put("kW", new NDArrayShapeAdapter(1));
//        tfMappings.put("sH", new IntArrayIntIndexAdpater(1));
//        tfMappings.put("sW", new IntArrayIntIndexAdpater(2));
        tfMappings.put("sH", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 2, 1, fields.get("dataFormat")));
        tfMappings.put("sW", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 3, 2, fields.get("dataFormat")));
        tfMappings.put("isSameMode", new StringEqualsAdapter("SAME"));
        tfMappings.put("isNHWC", new StringEqualsAdapter("NHWC"));


        Map<String, AttributeAdapter> onnxMappings = new HashMap<>();
        onnxMappings.put("kH", new SizeThresholdIntArrayIntIndexAdapter(0, 2, 0));
        onnxMappings.put("kW", new SizeThresholdIntArrayIntIndexAdapter(1, 2, 0));
        onnxMappings.put("dH", new SizeThresholdIntArrayIntIndexAdapter(0, 2, 0));
        onnxMappings.put("dW", new SizeThresholdIntArrayIntIndexAdapter(1, 2, 0));
        onnxMappings.put("sH", new SizeThresholdIntArrayIntIndexAdapter(0, 2, 0));
        onnxMappings.put("sW", new SizeThresholdIntArrayIntIndexAdapter(1, 2, 0));
        onnxMappings.put("isSameMode", new StringEqualsAdapter("SAME"));
        onnxMappings.put("isNHWC", new StringEqualsAdapter("NHWC"));

        ret.put(tensorflowName(), tfMappings);
        return ret;
    }


    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
        throw new UnsupportedOperationException("Use the new Tensorflow Importer instead. This method is now removed.");

    }

    @Override
    public String opName() {
        return "deconv2d_tf";
    }

    @Override
    public String onnxName() {
        return "ConvTranspose-Absent";
    }

    @Override
    public String tensorflowName() {
        return "Conv2DBackpropInput";
    }


    @Override
    public List<SDVariable> doDiff(List<SDVariable> f1) {
        throw new UnsupportedOperationException("To be implemented yet");
    }

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){ //inShape, weights, input
        int n = args().length;
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
        if(!dArguments.isEmpty()) {
            return Arrays.asList(dArguments.get(0));
        }
        return Collections.singletonList(inputDataTypes.get(2));
    }
}
