/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * 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.
 *
 * 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.nd4j.linalg.api.ops.impl.layers.convolution;

import lombok.Builder;
import lombok.Getter;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
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.imports.graphmapper.onnx.OnnxGraphMapper;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
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.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.*;


/**
 * Depthwise Conv2D operation
 */
@Slf4j
@Getter
public class DepthwiseConv2D extends DynamicCustomOp {

    protected Conv2DConfig config;

    @Builder(builderMethodName = "builder")
    public DepthwiseConv2D(SameDiff sameDiff,
                           SDVariable[] inputFunctions,
                           INDArray[] inputArrays, INDArray[] outputs,
                           Conv2DConfig config) {
        super(null, inputArrays, outputs);
        this.sameDiff = sameDiff;
        this.config = config;
        addArgs();
        sameDiff.putOpForId(this.getOwnName(), this);    //Normally called in DynamicCustomOp constructor, via setInstanceId - but sameDiff field is null at that point
        sameDiff.addArgsFor(inputFunctions, this);
    }

    public DepthwiseConv2D() {
    }

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

        return super.iArgs();
    }

    protected void addArgs() {
        addIArgument(config.getKH(),
                config.getKW(),
                config.getSH(),
                config.getSW(),
                config.getPH(),
                config.getPW(),
                config.getDH(),
                config.getDW(),
                ArrayUtil.fromBoolean(config.isSameMode()),
                config.getDataFormat().equalsIgnoreCase(Conv2DConfig.NCHW) ? 0 : 1);

    }

    @Override
    public Object getValue(Field property) {
        if (config == null) {
            config = Conv2DConfig.builder().build();
        }

        try {
            val t = config.getValue(property);
            return t;
        } catch (Exception e) {
            throw new RuntimeException(e);
        }
    }

    @Override
    public Map<String, Object> propertiesForFunction() {
        if(config == null && !iArguments.isEmpty()){
            config = Conv2DConfig.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 ? Conv2DConfig.NHWC : Conv2DConfig.NCHW)
                    .build();
        }
        return config.toProperties();
    }

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
        TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
        addArgs();

        /*
        // we must permute weights once during import
        val weightsName = nodeDef.getInput(1);
        val variable = initWith.getVariable(weightsName);
        val tmp = initWith.getArrForVarName(weightsName);
        val array = tmp.permute(3, 2, 0, 1).dup('c');

        initWith.associateArrayWithVariable(array, variable);
        */
    }

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

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

    @Override
    public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map<String, Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph) {
        OnnxGraphMapper.getInstance().initFunctionFromProperties(node.getOpType(), this, attributesForNode, node, graph);
        addArgs();
    }


    @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, inC, outC] always for weights
        tfMappings.put("kH", new NDArrayShapeAdapter(0));
        tfMappings.put("kW", new NDArrayShapeAdapter(1));
        tfMappings.put("sH", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 2, 1, fields.get("dataFormat")));
        tfMappings.put("sW", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 3, 2, fields.get("dataFormat")));
        tfMappings.put("dH", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 2, 1, fields.get("dataFormat")));
        tfMappings.put("dW", new ConditionalFieldValueIntIndexArrayAdapter("NCHW", 3, 2, fields.get("dataFormat")));
        tfMappings.put("isSameMode", new StringEqualsAdapter("SAME"));


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


        try {
            ret.put(tensorflowName(), tfMappings);
        } catch (NoOpNameFoundException e) {
            //
        }

        try {
            ret.put(onnxName(), onnxMappings);
        } catch (NoOpNameFoundException e) {
            //
        }

        return ret;
    }

    @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[]{"sW", "sH"})
                .build();


        val kernelMappingH = PropertyMapping.builder()
                .propertyNames(new String[]{"kH"})
                .tfInputPosition(1)
                .shapePosition(0)
                .onnxAttrName("kernel_shape")
                .build();

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

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

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

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

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

        val paddingWidthHeight = PropertyMapping.builder()
                .onnxAttrName("padding")
                .propertyNames(new String[]{"pH", "pW"})
                .build();


        map.put("sW", strideMapping);
        map.put("sH", strideMapping);
        map.put("kH", kernelMappingH);
        map.put("kW", kernelMappingW);
        map.put("dW", dilationMapping);
        map.put("dH", dilationMapping);
        map.put("isSameMode", sameMode);
        map.put("pH", paddingWidthHeight);
        map.put("pW", paddingWidthHeight);
        map.put("dataFormat", dataFormat);

        try {
            ret.put(onnxName(), map);
        } catch (NoOpNameFoundException e) {
            //ignore
        }


        try {
            ret.put(tensorflowName(), map);
        } catch (NoOpNameFoundException e) {
            //ignore
        }

        return ret;
    }


    @Override
    public String opName() {
        return "depthwise_conv2d";
    }

    @Override
    public List<SDVariable> doDiff(List<SDVariable> f1) {
        throw new UnsupportedOperationException("Not implemented yet");
    }


    @Override
    public String onnxName() {
        return "depth_conv";
    }

    @Override
    public String tensorflowName() {
        return "DepthwiseConv2dNative";
    }

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