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/*******************************************************************************
 * 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.shape;

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.descriptors.properties.PropertyMapping;
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.Op;
import org.nd4j.linalg.api.ops.impl.shape.bp.ConcatBp;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

import java.util.*;

@Slf4j
public class Concat extends DynamicCustomOp {
    private int concatDimension = -1;
    private boolean isDynamicAxis = false;

    public Concat(){

    }

    public Concat(int concatDimension, INDArray... arrays) {
        super(null, arrays, new INDArray[0]);
        this.concatDimension = concatDimension;
        addIArgument(concatDimension);
    }

    public Concat(SameDiff sameDiff, int concatDimension, SDVariable... inputs){
        super(null, sameDiff, inputs);
        addIArgument(concatDimension);
        this.concatDimension = concatDimension;
    }

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

    @Override
    public void assertValidForExecution() {
        val descriptor = getDescriptor();
        if(descriptor == null)
            throw new NoOpNameFoundException("No descriptor found for op name " + opName());


        if(descriptor.getNumInputs() > 0 && numInputArguments() < 2)
            throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of inputs is invalid for execution. Specified " + numInputArguments() + " but should be " + descriptor.getNumInputs());

        if(descriptor.getNumOutputs() > 0 && numOutputArguments() != descriptor.getNumOutputs())
            throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of outputs is invalid for execution. Specified " + numOutputArguments() + " but should be " + descriptor.getNumOutputs());

        //< 0 means dynamic size
        if(descriptor.getNumIArgs() >= 0 && numIArguments() != descriptor.getNumIArgs())
            throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of integer arguments is invalid for execution. Specified " + numIArguments() + " but should be " + descriptor.getNumIArgs());

        if(descriptor.getNumTArgs() >= 0 && numTArguments() != descriptor.getNumTArgs())
            throw new ND4JIllegalStateException("Op failure for " + opName() + " Number of inputs is invalid for execution. Specified " + numTArguments() + " but should be " + descriptor.getNumTArgs());

    }

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
        //TF uses dynamic axis - last argument is a scalar integer array for axis
        addBArgument(true);
        isDynamicAxis = true;
    }

    @Override
    public Map propertiesForFunction() {
        Map ret = new LinkedHashMap<>();
        ret.put("concatDimension",concatDimension);
        return ret;
    }

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

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

    @Override
    public String[] tensorflowNames() {
        return new String[]  {"Concat","ConcatV2"};
    }

    @Override
    public Op.Type opType() {
        return Op.Type.CUSTOM;
    }

    @Override
    public List doDiff(List i_v) {
        SDVariable[] args = args();
        SDVariable[] bpArgs;
        if(isDynamicAxis){
            bpArgs = Arrays.copyOf(args, args.length + 2);
            bpArgs[bpArgs.length - 1] = bpArgs[bpArgs.length - 3];      //Last input is axis -> move to end of bp args too
            bpArgs[bpArgs.length - 2] = i_v.get(0);
            return Arrays.asList(new ConcatBp(sameDiff, concatDimension, bpArgs).outputVariables());
        } else {
            bpArgs = Arrays.copyOf(args, args.length + 1);
            bpArgs[bpArgs.length - 1] = i_v.get(0);
            return Arrays.asList(new ConcatBp(sameDiff, concatDimension, bpArgs).outputVariables());
        }
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes){
        DataType first = dataTypes.get(0);

        for( int i=1; i