/* 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.quantization;

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.types.TFloat32;

/**
 * Fake-quantize the 'inputs' tensor of type float via per-channel floats
 * Fake-quantize the {@code inputs} tensor of type float per-channel and one of the
 * shapes: {@code [d]}, {@code [b, d]} {@code [b, h, w, d]} via per-channel floats {@code min} and {@code max}
 * of shape {@code [d]} to {@code outputs} tensor of same shape as {@code inputs}.
 * <p>Attributes
 * <ul>
 * <li>{@code [min; max]} define the clamping range for the {@code inputs} data.</li>
 * <li>{@code inputs} values are quantized into the quantization range (
 * {@code [0; 2^num_bits - 1]} when {@code narrow_range} is false and {@code [1; 2^num_bits - 1]}
 * when it is true) and then de-quantized and output as floats in {@code [min; max]}
 * interval.</li>
 * <li>{@code num_bits} is the bitwidth of the quantization; between 2 and 16, inclusive.</li>
 * </ul>
 * <p>Before quantization, {@code min} and {@code max} values are adjusted with the following
 * logic.
 * It is suggested to have {@code min <= 0 <= max}. If {@code 0} is not in the range of values,
 * the behavior can be unexpected:
 * <ul>
 * <li>If {@code 0 < min < max}: {@code min_adj = 0} and {@code max_adj = max - min}.</li>
 * <li>If {@code min < max < 0}: {@code min_adj = min - max} and {@code max_adj = 0}.</li>
 * <li>If {@code min <= 0 <= max}: {@code scale = (max - min) / (2^num_bits - 1) },
 * {@code min_adj = scale * round(min / scale)} and {@code max_adj = max + min_adj - min}.</li>
 * </ul>
 * <p>This operation has a gradient and thus allows for training {@code min} and {@code max}
 * values.
 */
@OpMetadata(
    opType = FakeQuantWithMinMaxVarsPerChannel.OP_NAME,
    inputsClass = FakeQuantWithMinMaxVarsPerChannel.Inputs.class
)
@Operator(
    group = "quantization"
)
public final class FakeQuantWithMinMaxVarsPerChannel extends RawOp implements Operand<TFloat32> {
  /**
   * The name of this op, as known by TensorFlow core engine
   */
  public static final String OP_NAME = "FakeQuantWithMinMaxVarsPerChannel";

  private Output<TFloat32> outputs;

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

  /**
   * Factory method to create a class wrapping a new FakeQuantWithMinMaxVarsPerChannel operation.
   *
   * @param scope current scope
   * @param inputs The inputs value
   * @param min The min value
   * @param max The max value
   * @param options carries optional attribute values
   * @return a new instance of FakeQuantWithMinMaxVarsPerChannel
   */
  @Endpoint(
      describeByClass = true
  )
  public static FakeQuantWithMinMaxVarsPerChannel create(Scope scope, Operand<TFloat32> inputs,
      Operand<TFloat32> min, Operand<TFloat32> max, Options... options) {
    OperationBuilder opBuilder = scope.opBuilder(OP_NAME, "FakeQuantWithMinMaxVarsPerChannel");
    opBuilder.addInput(inputs.asOutput());
    opBuilder.addInput(min.asOutput());
    opBuilder.addInput(max.asOutput());
    if (options != null) {
      for (Options opts : options) {
        if (opts.numBits != null) {
          opBuilder.setAttr("num_bits", opts.numBits);
        }
        if (opts.narrowRange != null) {
          opBuilder.setAttr("narrow_range", opts.narrowRange);
        }
      }
    }
    return new FakeQuantWithMinMaxVarsPerChannel(opBuilder.build());
  }

  /**
   * Sets the numBits option.
   *
   * @param numBits the numBits option
   * @return this Options instance.
   */
  public static Options numBits(Long numBits) {
    return new Options().numBits(numBits);
  }

  /**
   * Sets the narrowRange option.
   *
   * @param narrowRange the narrowRange option
   * @return this Options instance.
   */
  public static Options narrowRange(Boolean narrowRange) {
    return new Options().narrowRange(narrowRange);
  }

  /**
   * Gets outputs.
   *
   * @return outputs.
   */
  public Output<TFloat32> outputs() {
    return outputs;
  }

  @Override
  public Output<TFloat32> asOutput() {
    return outputs;
  }

  /**
   * Optional attributes for {@link org.tensorflow.op.quantization.FakeQuantWithMinMaxVarsPerChannel}
   */
  public static class Options {
    private Long numBits;

    private Boolean narrowRange;

    private Options() {
    }

    /**
     * Sets the numBits option.
     *
     * @param numBits the numBits option
     * @return this Options instance.
     */
    public Options numBits(Long numBits) {
      this.numBits = numBits;
      return this;
    }

    /**
     * Sets the narrowRange option.
     *
     * @param narrowRange the narrowRange option
     * @return this Options instance.
     */
    public Options narrowRange(Boolean narrowRange) {
      this.narrowRange = narrowRange;
      return this;
    }
  }

  @OpInputsMetadata(
      outputsClass = FakeQuantWithMinMaxVarsPerChannel.class
  )
  public static class Inputs extends RawOpInputs<FakeQuantWithMinMaxVarsPerChannel> {
    /**
     * The inputs input
     */
    public final Operand<TFloat32> inputs;

    /**
     * The min input
     */
    public final Operand<TFloat32> min;

    /**
     * The max input
     */
    public final Operand<TFloat32> max;

    /**
     * The numBits attribute
     */
    public final long numBits;

    /**
     * The narrowRange attribute
     */
    public final boolean narrowRange;

    public Inputs(GraphOperation op) {
      super(new FakeQuantWithMinMaxVarsPerChannel(op), op, Arrays.asList("num_bits", "narrow_range"));
      int inputIndex = 0;
      inputs = (Operand<TFloat32>) op.input(inputIndex++);
      min = (Operand<TFloat32>) op.input(inputIndex++);
      max = (Operand<TFloat32>) op.input(inputIndex++);
      numBits = op.attributes().getAttrInt("num_bits");
      narrowRange = op.attributes().getAttrBool("narrow_range");
    }
  }
}
