/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://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.
=======================================================================*/

// This class has been generated, DO NOT EDIT!

package org.tensorflow.op.train;

import java.util.Arrays;
import org.tensorflow.GraphOperation;
import org.tensorflow.Operand;
import org.tensorflow.Operation;
import org.tensorflow.OperationBuilder;
import org.tensorflow.Output;
import org.tensorflow.op.RawOp;
import org.tensorflow.op.RawOpInputs;
import org.tensorflow.op.Scope;
import org.tensorflow.op.annotation.Endpoint;
import org.tensorflow.op.annotation.OpInputsMetadata;
import org.tensorflow.op.annotation.OpMetadata;
import org.tensorflow.op.annotation.Operator;
import org.tensorflow.proto.framework.DataType;
import org.tensorflow.types.family.TNumber;
import org.tensorflow.types.family.TType;

/**
 * Update '*var' according to the RMSProp algorithm.
 * Note that in dense implementation of this algorithm, ms and mom will
 * update even if the grad is zero, but in this sparse implementation, ms
 * and mom will not update in iterations during which the grad is zero.
 * <p>mean_square = decay * mean_square + (1-decay) * gradient ** 2
 * Delta = learning_rate * gradient / sqrt(mean_square + epsilon)
 * <p>$$ms &lt;- rho * ms_{t-1} + (1-rho) * grad * grad$$
 * $$mom &lt;- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)$$
 * $$var &lt;- var - mom$$
 *
 * @param <T> data type for {@code out} output
 */
@OpMetadata(
    opType = SparseApplyRmsProp.OP_NAME,
    inputsClass = SparseApplyRmsProp.Inputs.class
)
@Operator(
    group = "train"
)
public final class SparseApplyRmsProp<T extends TType> extends RawOp implements Operand<T> {
  /**
   * The name of this op, as known by TensorFlow core engine
   */
  public static final String OP_NAME = "SparseApplyRMSProp";

  private Output<T> out;

  public SparseApplyRmsProp(Operation operation) {
    super(operation, OP_NAME);
    int outputIdx = 0;
    out = operation.output(outputIdx++);
  }

  /**
   * Factory method to create a class wrapping a new SparseApplyRMSProp operation.
   *
   * @param scope current scope
   * @param var Should be from a Variable().
   * @param ms Should be from a Variable().
   * @param mom Should be from a Variable().
   * @param lr Scaling factor. Must be a scalar.
   * @param rho Decay rate. Must be a scalar.
   * @param momentum The momentum value
   * @param epsilon Ridge term. Must be a scalar.
   * @param grad The gradient.
   * @param indices A vector of indices into the first dimension of var, ms and mom.
   * @param options carries optional attribute values
   * @param <T> data type for {@code SparseApplyRMSProp} output and operands
   * @return a new instance of SparseApplyRmsProp
   */
  @Endpoint(
      describeByClass = true
  )
  public static <T extends TType> SparseApplyRmsProp<T> create(Scope scope, Operand<T> var,
      Operand<T> ms, Operand<T> mom, Operand<T> lr, Operand<T> rho, Operand<T> momentum,
      Operand<T> epsilon, Operand<T> grad, Operand<? extends TNumber> indices, Options... options) {
    OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "SparseApplyRmsProp");
    opBuilder.addInput(var.asOutput());
    opBuilder.addInput(ms.asOutput());
    opBuilder.addInput(mom.asOutput());
    opBuilder.addInput(lr.asOutput());
    opBuilder.addInput(rho.asOutput());
    opBuilder.addInput(momentum.asOutput());
    opBuilder.addInput(epsilon.asOutput());
    opBuilder.addInput(grad.asOutput());
    opBuilder.addInput(indices.asOutput());
    if (options != null) {
      for (Options opts : options) {
        if (opts.useLocking != null) {
          opBuilder.setAttr("use_locking", opts.useLocking);
        }
      }
    }
    return new SparseApplyRmsProp<>(opBuilder.build());
  }

  /**
   * Sets the useLocking option.
   *
   * @param useLocking If {@code True}, updating of the var, ms, and mom tensors is protected
   * by a lock; otherwise the behavior is undefined, but may exhibit less
   * contention.
   * @return this Options instance.
   */
  public static Options useLocking(Boolean useLocking) {
    return new Options().useLocking(useLocking);
  }

  /**
   * Gets out.
   * Same as &quot;var&quot;.
   * @return out.
   */
  public Output<T> out() {
    return out;
  }

  @Override
  public Output<T> asOutput() {
    return out;
  }

  /**
   * Optional attributes for {@link org.tensorflow.op.train.SparseApplyRmsProp}
   */
  public static class Options {
    private Boolean useLocking;

    private Options() {
    }

    /**
     * Sets the useLocking option.
     *
     * @param useLocking If {@code True}, updating of the var, ms, and mom tensors is protected
     * by a lock; otherwise the behavior is undefined, but may exhibit less
     * contention.
     * @return this Options instance.
     */
    public Options useLocking(Boolean useLocking) {
      this.useLocking = useLocking;
      return this;
    }
  }

  @OpInputsMetadata(
      outputsClass = SparseApplyRmsProp.class
  )
  public static class Inputs<T extends TType> extends RawOpInputs<SparseApplyRmsProp<T>> {
    /**
     * Should be from a Variable().
     */
    public final Operand<T> var;

    /**
     * Should be from a Variable().
     */
    public final Operand<T> ms;

    /**
     * Should be from a Variable().
     */
    public final Operand<T> mom;

    /**
     * Scaling factor. Must be a scalar.
     */
    public final Operand<T> lr;

    /**
     * Decay rate. Must be a scalar.
     */
    public final Operand<T> rho;

    /**
     * The momentum input
     */
    public final Operand<T> momentum;

    /**
     * Ridge term. Must be a scalar.
     */
    public final Operand<T> epsilon;

    /**
     * The gradient.
     */
    public final Operand<T> grad;

    /**
     * A vector of indices into the first dimension of var, ms and mom.
     */
    public final Operand<? extends TNumber> indices;

    /**
     * The T attribute
     */
    public final DataType T;

    /**
     * The Tindices attribute
     */
    public final DataType Tindices;

    /**
     * If `True`, updating of the var, ms, and mom tensors is protected
     * by a lock; otherwise the behavior is undefined, but may exhibit less
     * contention.
     */
    public final boolean useLocking;

    public Inputs(GraphOperation op) {
      super(new SparseApplyRmsProp<>(op), op, Arrays.asList("T", "Tindices", "use_locking"));
      int inputIndex = 0;
      var = (Operand<T>) op.input(inputIndex++);
      ms = (Operand<T>) op.input(inputIndex++);
      mom = (Operand<T>) op.input(inputIndex++);
      lr = (Operand<T>) op.input(inputIndex++);
      rho = (Operand<T>) op.input(inputIndex++);
      momentum = (Operand<T>) op.input(inputIndex++);
      epsilon = (Operand<T>) op.input(inputIndex++);
      grad = (Operand<T>) op.input(inputIndex++);
      indices = (Operand<? extends TNumber>) op.input(inputIndex++);
      T = op.attributes().getAttrType("T");
      Tindices = op.attributes().getAttrType("Tindices");
      useLocking = op.attributes().getAttrBool("use_locking");
    }
  }
}
