/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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.
 *  *
 *  * SPDX-License-Identifier: Apache-2.0
 *  *****************************************************************************
 */

package org.deeplearning4j.text.movingwindow;

import org.apache.commons.lang3.StringUtils;
import org.deeplearning4j.text.tokenization.tokenizer.Tokenizer;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
import org.nd4j.common.base.Preconditions;
import org.nd4j.common.collection.MultiDimensionalMap;
import org.nd4j.common.primitives.Pair;

import java.util.ArrayList;
import java.util.List;

public class ContextLabelRetriever {


    private static String BEGIN_LABEL = "<([A-Za-z]+|\\d+)>";
    private static String END_LABEL = "</([A-Za-z]+|\\d+)>";


    private ContextLabelRetriever() {}


    /**
     * Returns a stripped sentence with the indices of words
     * with certain kinds of labels.
     * @param sentence the sentence to process
     * @return a pair of a post processed sentence
     * with labels stripped and the spans of
     * the labels
     */
    public static Pair<String, MultiDimensionalMap<Integer, Integer, String>> stringWithLabels(String sentence,
                                                                   TokenizerFactory tokenizerFactory) {
        MultiDimensionalMap<Integer, Integer, String> map = MultiDimensionalMap.newHashBackedMap();
        Tokenizer t = tokenizerFactory.create(sentence);
        List<String> currTokens = new ArrayList<>();
        String currLabel = null;
        String endLabel = null;
        List<Pair<String, List<String>>> tokensWithSameLabel = new ArrayList<>();
        while (t.hasMoreTokens()) {
            String token = t.nextToken();
            if (token.matches(BEGIN_LABEL)) {
                if (endLabel != null)
                    throw new IllegalStateException(
                                    "Tried parsing sentence; found an end label when the begin label has not been cleared");
                currLabel = token;

                //no labels; add these as NONE and begin the new label
                if (!currTokens.isEmpty()) {
                    tokensWithSameLabel.add(new Pair<>("NONE", (List<String>) new ArrayList<>(currTokens)));
                    currTokens.clear();

                }

            } else if (token.matches(END_LABEL)) {
                if (currLabel == null)
                    throw new IllegalStateException("Found an ending label with no matching begin label");
                endLabel = token;
            } else
                currTokens.add(token);

            if (currLabel != null && endLabel != null) {
                currLabel = currLabel.replaceAll("[<>/]", "");
                endLabel = endLabel.replaceAll("[<>/]", "");
                Preconditions.checkState(!currLabel.isEmpty(), "Current label is empty!");
                Preconditions.checkState(!endLabel.isEmpty(), "End label is empty!");
                Preconditions.checkState(currLabel.equals(endLabel), "Current label begin and end did not match for the parse. Was: %s ending with %s", currLabel, endLabel);

                tokensWithSameLabel.add(new Pair<>(currLabel, (List<String>) new ArrayList<>(currTokens)));
                currTokens.clear();

                //clear out the tokens
                currLabel = null;
                endLabel = null;
            }
        }

        //no labels; add these as NONE and begin the new label
        if (!currTokens.isEmpty()) {
            tokensWithSameLabel.add(new Pair<>("none", (List<String>) new ArrayList<>(currTokens)));
            currTokens.clear();

        }

        //now join the output
        StringBuilder strippedSentence = new StringBuilder();
        for (Pair<String, List<String>> tokensWithLabel : tokensWithSameLabel) {
            String joinedSentence = StringUtils.join(tokensWithLabel.getSecond(), " ");
            //spaces between separate parts of the sentence
            if (!(strippedSentence.length() < 1))
                strippedSentence.append(" ");
            strippedSentence.append(joinedSentence);
            int begin = strippedSentence.toString().indexOf(joinedSentence);
            int end = begin + joinedSentence.length();
            map.put(begin, end, tokensWithLabel.getFirst());
        }

        return new Pair<>(strippedSentence.toString(), map);
    }
}
