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

package org.deeplearning4j.models.embeddings;

import org.deeplearning4j.models.sequencevectors.sequence.SequenceElement;
import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
import org.deeplearning4j.core.ui.UiConnectionInfo;
import org.nd4j.linalg.api.ndarray.INDArray;

import java.io.File;
import java.io.Serializable;
import java.util.Iterator;
import java.util.concurrent.atomic.AtomicLong;

/**
 * General weight lookup table
 *
 * @author Adam Gibson
 */
public interface WeightLookupTable extends Serializable {

    /**
     * Returns unique ID of this table.
     * Used for JointStorage/DistributedLookupTable mechanics
     *
     * @return ID of this table
     */
    Long getTableId();

    /**
     * Set's table Id.
     * Please note, it should be unique withing Joint/Distributed LookupTable
     *
     * @param tableId
     */
    void setTableId(Long tableId);

    /**
     * The layer size for the lookup table
     * @return the layer size for the lookup table
     */
    int layerSize();

    /**
     * Returns gradient for specified word
     * @param column
     * @param gradient
     * @return
     */
    double getGradient(int column, double gradient);

    /**
     * Clear out all weights regardless
     * @param reset
     */
    void resetWeights(boolean reset);



    /**
     *
     * @param codeIndex
     * @param code
     */
    void putCode(int codeIndex, INDArray code);

    /**
     * Loads the co-occurrences for the given codes
     * @param codes the codes to load
     * @return an ndarray of code.length by layerSize
     */
    INDArray loadCodes(int[] codes);

    /**
     * Iterate on the given 2 vocab words
     * @param w1 the first word to iterate on
     * @param w2 the second word to iterate on
     */
    @Deprecated
    void iterate(T w1, T w2);

    /**
     * Iterate on the given 2 vocab words
     * @param w1 the first word to iterate on
     * @param w2 the second word to iterate on
     * @param nextRandom nextRandom for sampling
     * @param alpha the alpha to use for learning
     */
    @Deprecated
    void iterateSample(T w1, T w2, AtomicLong nextRandom, double alpha);


    /**
     * Inserts a word vector
     * @param word the word to insert
     * @param vector the vector to insert
     */
    void putVector(String word, INDArray vector);

    /**
     *
     * @param word
     * @return
     */
    INDArray vector(String word);

    /**
     * Reset the weights of the cache
     */
    void resetWeights();


    /**
     * Sets the learning rate
     * @param lr
     */
    void setLearningRate(double lr);

    /**
     * Iterates through all of the vectors in the cache
     * @return an iterator for all vectors in the cache
     */
    Iterator vectors();

    INDArray getWeights();

    /**
     * Returns corresponding vocabulary
     */
    VocabCache getVocabCache();
}