/* 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.distribute;

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.proto.framework.DataType;
import org.tensorflow.types.family.TNumber;

/**
 * Reduces {@code input} from {@code num_devices} using {@code reduction} to a single device.
 * Reduces {@code input} from {@code num_devices} using {@code reduction} to a single device.
 * <p>The graph should be constructed so that all inputs have a valid device
 * assignment, and the op itself is assigned one of these devices.
 * <p>input: The input to the reduction.
 * data: the value of the reduction across all {@code num_devices} devices.
 * reduction: the reduction operation to perform.
 *
 * @param <T> data type for {@code data} output
 */
@OpMetadata(
    opType = NcclReduce.OP_NAME,
    inputsClass = NcclReduce.Inputs.class
)
public final class NcclReduce<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 = "NcclReduce";

  private Output<T> data;

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

  /**
   * Factory method to create a class wrapping a new NcclReduce operation.
   *
   * @param scope current scope
   * @param input The input value
   * @param reduction The value of the reduction attribute
   * @param <T> data type for {@code NcclReduce} output and operands
   * @return a new instance of NcclReduce
   */
  @Endpoint(
      describeByClass = true
  )
  public static <T extends TNumber> NcclReduce<T> create(Scope scope, Iterable<Operand<T>> input,
      String reduction) {
    OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "NcclReduce");
    opBuilder.addInputList(Operands.asOutputs(input));
    opBuilder.setAttr("reduction", reduction);
    return new NcclReduce<>(opBuilder.build());
  }

  /**
   * Gets data.
   *
   * @return data.
   */
  public Output<T> data() {
    return data;
  }

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

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

    /**
     * The reduction attribute
     */
    public final String reduction;

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

    public Inputs(GraphOperation op) {
      super(new NcclReduce<>(op), op, Arrays.asList("reduction", "T"));
      int inputIndex = 0;
      int inputLength = op.inputListLength("input");
      input = Arrays.asList((Operand<T>[]) op.inputList(inputIndex, inputLength));
      inputIndex += inputLength;
      reduction = op.attributes().getAttrString("reduction");
      T = op.attributes().getAttrType("T");
    }
  }
}
