/* 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.math;

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;

/**
 * Computes the minimum along segments of a tensor.
 * Read
 *  <a href="https://tensorflow.org/api_docs/python/tf/math#Segmentation">the section on segmentation</a> 
 * for an explanation of segments.
 * <p>This operator is similar to the unsorted segment sum operator found
 *  <a href="../../../api_docs/python/math_ops.md#UnsortedSegmentSum">(here)</a> .
 * Instead of computing the sum over segments, it computes the minimum such that:
 * <p>\(output_i = \min_{j...} data_[j...]\) where min is over tuples {@code j...} such
 * that {@code segment_ids[j...] == i}.
 * <p>If the minimum is empty for a given segment ID {@code i}, it outputs the largest
 * possible value for the specific numeric type,
 * {@code output[i] = numeric_limits<T>::max()}.
 * <p>For example:
 * <pre>
 * c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
 * tf.unsorted_segment_min(c, tf.constant([0, 1, 0]), num_segments=2)
 * # ==&gt; [[ 1,  2, 2, 1],
 * #       [5,  6, 7, 8]]
 * </pre>
 * <p>If the given segment ID {@code i} is negative, then the corresponding value is
 * dropped, and will not be included in the result.
 *
 * @param <T> data type for {@code output} output
 */
@OpMetadata(
    opType = UnsortedSegmentMin.OP_NAME,
    inputsClass = UnsortedSegmentMin.Inputs.class
)
@Operator(
    group = "math"
)
public final class UnsortedSegmentMin<T extends TNumber> extends RawOp implements Operand<T> {
  /**
   * The name of this op, as known by TensorFlow core engine
   */
  public static final String OP_NAME = "UnsortedSegmentMin";

  private Output<T> output;

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

  /**
   * Factory method to create a class wrapping a new UnsortedSegmentMin operation.
   *
   * @param scope current scope
   * @param data The data value
   * @param segmentIds A tensor whose shape is a prefix of {@code data.shape}.
   * @param numSegments The numSegments value
   * @param <T> data type for {@code UnsortedSegmentMin} output and operands
   * @return a new instance of UnsortedSegmentMin
   */
  @Endpoint(
      describeByClass = true
  )
  public static <T extends TNumber> UnsortedSegmentMin<T> create(Scope scope, Operand<T> data,
      Operand<? extends TNumber> segmentIds, Operand<? extends TNumber> numSegments) {
    OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "UnsortedSegmentMin");
    opBuilder.addInput(data.asOutput());
    opBuilder.addInput(segmentIds.asOutput());
    opBuilder.addInput(numSegments.asOutput());
    return new UnsortedSegmentMin<>(opBuilder.build());
  }

  /**
   * Gets output.
   * Has same shape as data, except for the first {@code segment_ids.rank}
   * dimensions, which are replaced with a single dimension which has size
   * {@code num_segments}.
   * @return output.
   */
  public Output<T> output() {
    return output;
  }

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

  @OpInputsMetadata(
      outputsClass = UnsortedSegmentMin.class
  )
  public static class Inputs<T extends TNumber> extends RawOpInputs<UnsortedSegmentMin<T>> {
    /**
     * The data input
     */
    public final Operand<T> data;

    /**
     * A tensor whose shape is a prefix of {@code data.shape}.
     */
    public final Operand<? extends TNumber> segmentIds;

    /**
     * The numSegments input
     */
    public final Operand<? extends TNumber> numSegments;

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

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

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

    public Inputs(GraphOperation op) {
      super(new UnsortedSegmentMin<>(op), op, Arrays.asList("T", "Tindices", "Tnumsegments"));
      int inputIndex = 0;
      data = (Operand<T>) op.input(inputIndex++);
      segmentIds = (Operand<? extends TNumber>) op.input(inputIndex++);
      numSegments = (Operand<? extends TNumber>) op.input(inputIndex++);
      T = op.attributes().getAttrType("T");
      Tindices = op.attributes().getAttrType("Tindices");
      Tnumsegments = op.attributes().getAttrType("Tnumsegments");
    }
  }
}
