/* 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.random;

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.Operands;
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.TInt32;
import org.tensorflow.types.TInt64;
import org.tensorflow.types.family.TNumber;

/**
 * Draws samples from a multinomial distribution.
 *
 * @param <U> data type for {@code output} output
 */
@OpMetadata(
    opType = Multinomial.OP_NAME,
    inputsClass = Multinomial.Inputs.class
)
@Operator(
    group = "random"
)
public final class Multinomial<U extends TNumber> extends RawOp implements Operand<U> {
  /**
   * The name of this op, as known by TensorFlow core engine
   */
  public static final String OP_NAME = "Multinomial";

  private Output<U> output;

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

  /**
   * Factory method to create a class wrapping a new Multinomial operation.
   *
   * @param scope current scope
   * @param logits 2-D Tensor with shape {@code [batch_size, num_classes]}.  Each slice {@code [i, :]}
   * represents the unnormalized log probabilities for all classes.
   * @param numSamples 0-D.  Number of independent samples to draw for each row slice.
   * @param outputDtype The value of the outputDtype attribute
   * @param options carries optional attribute values
   * @param <U> data type for {@code Multinomial} output and operands
   * @return a new instance of Multinomial
   */
  @Endpoint(
      describeByClass = true
  )
  public static <U extends TNumber> Multinomial<U> create(Scope scope,
      Operand<? extends TNumber> logits, Operand<TInt32> numSamples, Class<U> outputDtype,
      Options... options) {
    OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "Multinomial");
    opBuilder.addInput(logits.asOutput());
    opBuilder.addInput(numSamples.asOutput());
    opBuilder.setAttr("output_dtype", Operands.toDataType(outputDtype));
    if (options != null) {
      for (Options opts : options) {
        if (opts.seed != null) {
          opBuilder.setAttr("seed", opts.seed);
        }
        if (opts.seed2 != null) {
          opBuilder.setAttr("seed2", opts.seed2);
        }
      }
    }
    return new Multinomial<>(opBuilder.build());
  }

  /**
   * Factory method to create a class wrapping a new Multinomial operation, with the default output types.
   *
   * @param scope current scope
   * @param logits 2-D Tensor with shape {@code [batch_size, num_classes]}.  Each slice {@code [i, :]}
   * represents the unnormalized log probabilities for all classes.
   * @param numSamples 0-D.  Number of independent samples to draw for each row slice.
   * @param options carries optional attribute values
   * @return a new instance of Multinomial, with default output types
   */
  @Endpoint(
      describeByClass = true
  )
  public static Multinomial<TInt64> create(Scope scope, Operand<? extends TNumber> logits,
      Operand<TInt32> numSamples, Options[] options) {
    return create(scope, logits, numSamples, TInt64.class, options);
  }

  /**
   * Sets the seed option.
   *
   * @param seed If either seed or seed2 is set to be non-zero, the internal random number
   * generator is seeded by the given seed.  Otherwise, a random seed is used.
   * @return this Options instance.
   */
  public static Options seed(Long seed) {
    return new Options().seed(seed);
  }

  /**
   * Sets the seed2 option.
   *
   * @param seed2 A second seed to avoid seed collision.
   * @return this Options instance.
   */
  public static Options seed2(Long seed2) {
    return new Options().seed2(seed2);
  }

  /**
   * Gets output.
   * 2-D Tensor with shape {@code [batch_size, num_samples]}.  Each slice {@code [i, :]}
   * contains the drawn class labels with range {@code [0, num_classes)}.
   * @return output.
   */
  public Output<U> output() {
    return output;
  }

  @Override
  public Output<U> asOutput() {
    return output;
  }

  /**
   * Optional attributes for {@link org.tensorflow.op.random.Multinomial}
   */
  public static class Options {
    private Long seed;

    private Long seed2;

    private Options() {
    }

    /**
     * Sets the seed option.
     *
     * @param seed If either seed or seed2 is set to be non-zero, the internal random number
     * generator is seeded by the given seed.  Otherwise, a random seed is used.
     * @return this Options instance.
     */
    public Options seed(Long seed) {
      this.seed = seed;
      return this;
    }

    /**
     * Sets the seed2 option.
     *
     * @param seed2 A second seed to avoid seed collision.
     * @return this Options instance.
     */
    public Options seed2(Long seed2) {
      this.seed2 = seed2;
      return this;
    }
  }

  @OpInputsMetadata(
      outputsClass = Multinomial.class
  )
  public static class Inputs extends RawOpInputs<Multinomial<?>> {
    /**
     * 2-D Tensor with shape {@code [batch_size, num_classes]}.  Each slice {@code [i, :]}
     * represents the unnormalized log probabilities for all classes.
     */
    public final Operand<? extends TNumber> logits;

    /**
     * 0-D.  Number of independent samples to draw for each row slice.
     */
    public final Operand<TInt32> numSamples;

    /**
     * If either seed or seed2 is set to be non-zero, the internal random number
     * generator is seeded by the given seed.  Otherwise, a random seed is used.
     */
    public final long seed;

    /**
     * A second seed to avoid seed collision.
     */
    public final long seed2;

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

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

    public Inputs(GraphOperation op) {
      super(new Multinomial<>(op), op, Arrays.asList("seed", "seed2", "T", "output_dtype"));
      int inputIndex = 0;
      logits = (Operand<? extends TNumber>) op.input(inputIndex++);
      numSamples = (Operand<TInt32>) op.input(inputIndex++);
      seed = op.attributes().getAttrInt("seed");
      seed2 = op.attributes().getAttrInt("seed2");
      T = op.attributes().getAttrType("T");
      outputDtype = op.attributes().getAttrType("output_dtype");
    }
  }
}
