/*
 * Copyright 2023 Google LLC
 *
 * 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
 *
 *     https://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.
 */
// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source:
// google/cloud/aiplatform/v1beta1/schema/trainingjob/definition/automl_time_series_forecasting.proto

package com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition;

/**
 * Protobuf type {@code
 * google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs}
 */
public final class AutoMlForecastingInputs extends com.google.protobuf.GeneratedMessageV3
    implements
    // @@protoc_insertion_point(message_implements:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs)
    AutoMlForecastingInputsOrBuilder {
  private static final long serialVersionUID = 0L;
  // Use AutoMlForecastingInputs.newBuilder() to construct.
  private AutoMlForecastingInputs(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) {
    super(builder);
  }

  private AutoMlForecastingInputs() {
    targetColumn_ = "";
    timeSeriesIdentifierColumn_ = "";
    timeColumn_ = "";
    transformations_ = java.util.Collections.emptyList();
    optimizationObjective_ = "";
    weightColumn_ = "";
    timeSeriesAttributeColumns_ = com.google.protobuf.LazyStringArrayList.emptyList();
    unavailableAtForecastColumns_ = com.google.protobuf.LazyStringArrayList.emptyList();
    availableAtForecastColumns_ = com.google.protobuf.LazyStringArrayList.emptyList();
    quantiles_ = emptyDoubleList();
    validationOptions_ = "";
    additionalExperiments_ = com.google.protobuf.LazyStringArrayList.emptyList();
  }

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  protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
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  }

  public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
    return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMLForecastingProto
        .internal_static_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_AutoMlForecastingInputs_descriptor;
  }

  @java.lang.Override
  protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
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        .ensureFieldAccessorsInitialized(
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                .AutoMlForecastingInputs.Builder.class);
  }

  public interface TransformationOrBuilder
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      // @@protoc_insertion_point(interface_extends:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation)
      com.google.protobuf.MessageOrBuilder {

    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
     * </code>
     *
     * @return Whether the auto field is set.
     */
    boolean hasAuto();
    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
     * </code>
     *
     * @return The auto.
     */
    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.AutoTransformation
        getAuto();
    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
     * </code>
     */
    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.AutoTransformationOrBuilder
        getAutoOrBuilder();

    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
     * </code>
     *
     * @return Whether the numeric field is set.
     */
    boolean hasNumeric();
    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
     * </code>
     *
     * @return The numeric.
     */
    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
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        getNumeric();
    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
     * </code>
     */
    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.NumericTransformationOrBuilder
        getNumericOrBuilder();

    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
     * </code>
     *
     * @return Whether the categorical field is set.
     */
    boolean hasCategorical();
    /**
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     * </code>
     *
     * @return The categorical.
     */
    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.CategoricalTransformation
        getCategorical();
    /**
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     * </code>
     */
    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.CategoricalTransformationOrBuilder
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    /**
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     *
     * @return Whether the timestamp field is set.
     */
    boolean hasTimestamp();
    /**
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     *
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     */
    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
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    /**
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     */
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    /**
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     */
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     */
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    /**
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    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
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  /**
   * Protobuf type {@code
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      TransformationOrBuilder {
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    public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
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          .internal_static_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_AutoMlForecastingInputs_Transformation_descriptor;
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    @java.lang.Override
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    public interface AutoTransformationOrBuilder
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    /**
     *
     *
     * <pre>
     * Training pipeline will infer the proper transformation based on the
     * statistic of dataset.
     * </pre>
     *
     * Protobuf type {@code
     * google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation}
     */
    public static final class AutoTransformation extends com.google.protobuf.GeneratedMessageV3
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        // @@protoc_insertion_point(message_implements:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation)
        AutoTransformationOrBuilder {
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      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
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          parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
        return PARSER.parseFrom(data);
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          parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
              throws com.google.protobuf.InvalidProtocolBufferException {
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       * </pre>
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     *
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       *
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       *
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        // @@protoc_insertion_point(builder_scope:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation)
      }

      // @@protoc_insertion_point(class_scope:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation)
      private static final com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformation
          DEFAULT_INSTANCE;

      static {
        DEFAULT_INSTANCE =
            new com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.CategoricalTransformation();
      }

      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformation
          getDefaultInstance() {
        return DEFAULT_INSTANCE;
      }

      private static final com.google.protobuf.Parser<CategoricalTransformation> PARSER =
          new com.google.protobuf.AbstractParser<CategoricalTransformation>() {
            @java.lang.Override
            public CategoricalTransformation parsePartialFrom(
                com.google.protobuf.CodedInputStream input,
                com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                throws com.google.protobuf.InvalidProtocolBufferException {
              Builder builder = newBuilder();
              try {
                builder.mergeFrom(input, extensionRegistry);
              } catch (com.google.protobuf.InvalidProtocolBufferException e) {
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                    .setUnfinishedMessage(builder.buildPartial());
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                throw new com.google.protobuf.InvalidProtocolBufferException(e)
                    .setUnfinishedMessage(builder.buildPartial());
              }
              return builder.buildPartial();
            }
          };

      public static com.google.protobuf.Parser<CategoricalTransformation> parser() {
        return PARSER;
      }

      @java.lang.Override
      public com.google.protobuf.Parser<CategoricalTransformation> getParserForType() {
        return PARSER;
      }

      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformation
          getDefaultInstanceForType() {
        return DEFAULT_INSTANCE;
      }
    }

    public interface TimestampTransformationOrBuilder
        extends
        // @@protoc_insertion_point(interface_extends:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation)
        com.google.protobuf.MessageOrBuilder {

      /**
       * <code>string column_name = 1;</code>
       *
       * @return The columnName.
       */
      java.lang.String getColumnName();
      /**
       * <code>string column_name = 1;</code>
       *
       * @return The bytes for columnName.
       */
      com.google.protobuf.ByteString getColumnNameBytes();

      /**
       *
       *
       * <pre>
       * The format in which that time field is expressed. The time_format must
       * either be one of:
       *
       * * `unix-seconds`
       *
       * * `unix-milliseconds`
       *
       * * `unix-microseconds`
       *
       * * `unix-nanoseconds`
       *
       * (for respectively number of seconds, milliseconds, microseconds and
       * nanoseconds since start of the Unix epoch);
       *
       * or be written in `strftime` syntax.
       *
       * If time_format is not set, then the
       * default format is RFC 3339 `date-time` format, where
       * `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z)
       * </pre>
       *
       * <code>string time_format = 2;</code>
       *
       * @return The timeFormat.
       */
      java.lang.String getTimeFormat();
      /**
       *
       *
       * <pre>
       * The format in which that time field is expressed. The time_format must
       * either be one of:
       *
       * * `unix-seconds`
       *
       * * `unix-milliseconds`
       *
       * * `unix-microseconds`
       *
       * * `unix-nanoseconds`
       *
       * (for respectively number of seconds, milliseconds, microseconds and
       * nanoseconds since start of the Unix epoch);
       *
       * or be written in `strftime` syntax.
       *
       * If time_format is not set, then the
       * default format is RFC 3339 `date-time` format, where
       * `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z)
       * </pre>
       *
       * <code>string time_format = 2;</code>
       *
       * @return The bytes for timeFormat.
       */
      com.google.protobuf.ByteString getTimeFormatBytes();
    }
    /**
     *
     *
     * <pre>
     * Training pipeline will perform following transformation functions.
     *
     * *  Apply the transformation functions for Numerical columns.
     *
     * *  Determine the year, month, day,and weekday. Treat each value from the
     *    timestamp as a Categorical column.
     *
     * *  Invalid numerical values (for example, values that fall outside of a
     *    typical timestamp range, or are extreme values) receive no special
     *    treatment and are not removed.
     * </pre>
     *
     * Protobuf type {@code
     * google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation}
     */
    public static final class TimestampTransformation extends com.google.protobuf.GeneratedMessageV3
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        // @@protoc_insertion_point(message_implements:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation)
        TimestampTransformationOrBuilder {
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      private TimestampTransformation(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) {
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        timeFormat_ = "";
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      @SuppressWarnings({"unused"})
      protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
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      }

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
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       *
       *
       * <pre>
       * The format in which that time field is expressed. The time_format must
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       *
       * * `unix-milliseconds`
       *
       * * `unix-microseconds`
       *
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       * (for respectively number of seconds, milliseconds, microseconds and
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       * The format in which that time field is expressed. The time_format must
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       * * `unix-seconds`
       *
       * * `unix-milliseconds`
       *
       * * `unix-microseconds`
       *
       * * `unix-nanoseconds`
       *
       * (for respectively number of seconds, milliseconds, microseconds and
       * nanoseconds since start of the Unix epoch);
       *
       * or be written in `strftime` syntax.
       *
       * If time_format is not set, then the
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      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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          parseFrom(java.nio.ByteBuffer data)
              throws com.google.protobuf.InvalidProtocolBufferException {
        return PARSER.parseFrom(data);
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      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformation
          parseFrom(
              java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
              throws com.google.protobuf.InvalidProtocolBufferException {
        return PARSER.parseFrom(data, extensionRegistry);
      }

      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformation
          parseFrom(com.google.protobuf.ByteString data)
              throws com.google.protobuf.InvalidProtocolBufferException {
        return PARSER.parseFrom(data);
      }

      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformation
          parseFrom(
              com.google.protobuf.ByteString data,
              com.google.protobuf.ExtensionRegistryLite extensionRegistry)
              throws com.google.protobuf.InvalidProtocolBufferException {
        return PARSER.parseFrom(data, extensionRegistry);
      }

      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformation
          parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
        return PARSER.parseFrom(data);
      }

      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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          parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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      }

      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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        return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
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      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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          parseFrom(
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              com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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          parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException {
        return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
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      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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              com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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      }

      @java.lang.Override
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      @java.lang.Override
      protected Builder newBuilderForType(
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        Builder builder = new Builder(parent);
        return builder;
      }
      /**
       *
       *
       * <pre>
       * Training pipeline will perform following transformation functions.
       *
       * *  Apply the transformation functions for Numerical columns.
       *
       * *  Determine the year, month, day,and weekday. Treat each value from the
       *    timestamp as a Categorical column.
       *
       * *  Invalid numerical values (for example, values that fall outside of a
       *    typical timestamp range, or are extreme values) receive no special
       *    treatment and are not removed.
       * </pre>
       *
       * Protobuf type {@code
       * google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation}
       */
      public static final class Builder
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          // @@protoc_insertion_point(builder_implements:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation)
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
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          columnName_ = "";
          timeFormat_ = "";
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         * * `unix-milliseconds`
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         * * `unix-nanoseconds`
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       *
       * *  Convert the category name to a dictionary lookup index and generate an
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      }

      // @@protoc_insertion_point(class_scope:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TextTransformation)
      private static final com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TextTransformation
          DEFAULT_INSTANCE;

      static {
        DEFAULT_INSTANCE =
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                .AutoMlForecastingInputs.Transformation.TextTransformation();
      }

      public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TextTransformation
          getDefaultInstance() {
        return DEFAULT_INSTANCE;
      }

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      }
    }

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    private java.lang.Object transformationDetail_;

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      AUTO(1),
      NUMERIC(2),
      CATEGORICAL(3),
      TIMESTAMP(4),
      TEXT(5),
      TRANSFORMATIONDETAIL_NOT_SET(0);
      private final int value;

      private TransformationDetailCase(int value) {
        this.value = value;
      }
      /**
       * @param value The number of the enum to look for.
       * @return The enum associated with the given number.
       * @deprecated Use {@link #forNumber(int)} instead.
       */
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      public static TransformationDetailCase valueOf(int value) {
        return forNumber(value);
      }

      public static TransformationDetailCase forNumber(int value) {
        switch (value) {
          case 1:
            return AUTO;
          case 2:
            return NUMERIC;
          case 3:
            return CATEGORICAL;
          case 4:
            return TIMESTAMP;
          case 5:
            return TEXT;
          case 0:
            return TRANSFORMATIONDETAIL_NOT_SET;
          default:
            return null;
        }
      }

      public int getNumber() {
        return this.value;
      }
    };

    public TransformationDetailCase getTransformationDetailCase() {
      return TransformationDetailCase.forNumber(transformationDetailCase_);
    }

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     * <code>
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     * </code>
     *
     * @return Whether the auto field is set.
     */
    @java.lang.Override
    public boolean hasAuto() {
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    }
    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
     * </code>
     *
     * @return The auto.
     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.AutoTransformation
        getAuto() {
      if (transformationDetailCase_ == 1) {
        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.AutoTransformation)
            transformationDetail_;
      }
      return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .AutoMlForecastingInputs.Transformation.AutoTransformation.getDefaultInstance();
    }
    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
     * </code>
     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.AutoTransformationOrBuilder
        getAutoOrBuilder() {
      if (transformationDetailCase_ == 1) {
        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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            transformationDetail_;
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      return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .AutoMlForecastingInputs.Transformation.AutoTransformation.getDefaultInstance();
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    public static final int NUMERIC_FIELD_NUMBER = 2;
    /**
     * <code>
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     * </code>
     *
     * @return Whether the numeric field is set.
     */
    @java.lang.Override
    public boolean hasNumeric() {
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    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
     * </code>
     *
     * @return The numeric.
     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.NumericTransformation
        getNumeric() {
      if (transformationDetailCase_ == 2) {
        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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            transformationDetail_;
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      return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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     * <code>
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     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
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     * <code>
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     * </code>
     *
     * @return Whether the categorical field is set.
     */
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    /**
     * <code>
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     * </code>
     *
     * @return The categorical.
     */
    @java.lang.Override
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        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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            transformationDetail_;
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    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
     * </code>
     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.CategoricalTransformationOrBuilder
        getCategoricalOrBuilder() {
      if (transformationDetailCase_ == 3) {
        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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            transformationDetail_;
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    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
     * </code>
     *
     * @return Whether the timestamp field is set.
     */
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    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
     * </code>
     *
     * @return The timestamp.
     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
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      if (transformationDetailCase_ == 4) {
        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.TimestampTransformation)
            transformationDetail_;
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      return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .AutoMlForecastingInputs.Transformation.TimestampTransformation.getDefaultInstance();
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    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
     * </code>
     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
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        getTimestampOrBuilder() {
      if (transformationDetailCase_ == 4) {
        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.TimestampTransformation)
            transformationDetail_;
      }
      return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .AutoMlForecastingInputs.Transformation.TimestampTransformation.getDefaultInstance();
    }

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    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TextTransformation text = 5;
     * </code>
     *
     * @return Whether the text field is set.
     */
    @java.lang.Override
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      return transformationDetailCase_ == 5;
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    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TextTransformation text = 5;
     * </code>
     *
     * @return The text.
     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.TextTransformation
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      if (transformationDetailCase_ == 5) {
        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.TextTransformation)
            transformationDetail_;
      }
      return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .AutoMlForecastingInputs.Transformation.TextTransformation.getDefaultInstance();
    }
    /**
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TextTransformation text = 5;
     * </code>
     */
    @java.lang.Override
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.TextTransformationOrBuilder
        getTextOrBuilder() {
      if (transformationDetailCase_ == 5) {
        return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.TextTransformation)
            transformationDetail_;
      }
      return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .AutoMlForecastingInputs.Transformation.TextTransformation.getDefaultInstance();
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      if (isInitialized == 1) return true;
      if (isInitialized == 0) return false;

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    @java.lang.Override
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        output.writeMessage(
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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        parseFrom(com.google.protobuf.ByteString data)
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      return PARSER.parseFrom(data);
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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      return PARSER.parseFrom(data, extensionRegistry);
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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      return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
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    public static Builder newBuilder(
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    @java.lang.Override
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    protected Builder newBuilderForType(
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    /**
     * Protobuf type {@code
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    public static final class Builder
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        implements
        // @@protoc_insertion_point(builder_implements:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation)
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .TransformationOrBuilder {
      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
        return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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      }

      // Construct using
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      private Builder() {}

      private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
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      @java.lang.Override
      public Builder clear() {
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        if (numericBuilder_ != null) {
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        if (categoricalBuilder_ != null) {
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        if (timestampBuilder_ != null) {
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        if (textBuilder_ != null) {
          textBuilder_.clear();
        }
        transformationDetailCase_ = 0;
        transformationDetail_ = null;
        return this;
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      @java.lang.Override
      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
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            .AutoMLForecastingProto
            .internal_static_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_AutoMlForecastingInputs_Transformation_descriptor;
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      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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        buildPartialOneofs(result);
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              result) {
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        result.transformationDetail_ = this.transformationDetail_;
        if (transformationDetailCase_ == 1 && autoBuilder_ != null) {
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        if (transformationDetailCase_ == 2 && numericBuilder_ != null) {
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        if (transformationDetailCase_ == 3 && categoricalBuilder_ != null) {
          result.transformationDetail_ = categoricalBuilder_.build();
        }
        if (transformationDetailCase_ == 4 && timestampBuilder_ != null) {
          result.transformationDetail_ = timestampBuilder_.build();
        }
        if (transformationDetailCase_ == 5 && textBuilder_ != null) {
          result.transformationDetail_ = textBuilder_.build();
        }
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      @java.lang.Override
      public Builder clone() {
        return super.clone();
      }

      @java.lang.Override
      public Builder setField(
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      public Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field) {
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      public Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) {
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                      .AutoMlForecastingInputs.Transformation)
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        } else {
          super.mergeFrom(other);
          return this;
        }
      }

      public Builder mergeFrom(
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                  .Transformation
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        if (other
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                .AutoMlForecastingInputs.Transformation.getDefaultInstance()) return this;
        switch (other.getTransformationDetailCase()) {
          case AUTO:
            {
              mergeAuto(other.getAuto());
              break;
            }
          case NUMERIC:
            {
              mergeNumeric(other.getNumeric());
              break;
            }
          case CATEGORICAL:
            {
              mergeCategorical(other.getCategorical());
              break;
            }
          case TIMESTAMP:
            {
              mergeTimestamp(other.getTimestamp());
              break;
            }
          case TEXT:
            {
              mergeText(other.getText());
              break;
            }
          case TRANSFORMATIONDETAIL_NOT_SET:
            {
              break;
            }
        }
        this.mergeUnknownFields(other.getUnknownFields());
        onChanged();
        return this;
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      @java.lang.Override
      public final boolean isInitialized() {
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      public Builder mergeFrom(
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          throws java.io.IOException {
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          throw new java.lang.NullPointerException();
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        try {
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          while (!done) {
            int tag = input.readTag();
            switch (tag) {
              case 0:
                done = true;
                break;
              case 10:
                {
                  input.readMessage(getAutoFieldBuilder().getBuilder(), extensionRegistry);
                  transformationDetailCase_ = 1;
                  break;
                } // case 10
              case 18:
                {
                  input.readMessage(getNumericFieldBuilder().getBuilder(), extensionRegistry);
                  transformationDetailCase_ = 2;
                  break;
                } // case 18
              case 26:
                {
                  input.readMessage(getCategoricalFieldBuilder().getBuilder(), extensionRegistry);
                  transformationDetailCase_ = 3;
                  break;
                } // case 26
              case 34:
                {
                  input.readMessage(getTimestampFieldBuilder().getBuilder(), extensionRegistry);
                  transformationDetailCase_ = 4;
                  break;
                } // case 34
              case 42:
                {
                  input.readMessage(getTextFieldBuilder().getBuilder(), extensionRegistry);
                  transformationDetailCase_ = 5;
                  break;
                } // case 42
              default:
                {
                  if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                    done = true; // was an endgroup tag
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          } // while (!done)
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          throw e.unwrapIOException();
        } finally {
          onChanged();
        } // finally
        return this;
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      private int transformationDetailCase_ = 0;
      private java.lang.Object transformationDetail_;

      public TransformationDetailCase getTransformationDetailCase() {
        return TransformationDetailCase.forNumber(transformationDetailCase_);
      }

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        transformationDetailCase_ = 0;
        transformationDetail_ = null;
        onChanged();
        return this;
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      private int bitField0_;

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       *
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       *
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       */
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      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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          if (transformationDetailCase_ == 1) {
            return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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                transformationDetail_;
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.AutoTransformation.getDefaultInstance();
        } else {
          if (transformationDetailCase_ == 1) {
            return autoBuilder_.getMessage();
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.AutoTransformation.getDefaultInstance();
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      }
      /**
       * <code>
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       * </code>
       */
      public Builder setAuto(
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                  .Transformation.AutoTransformation
              value) {
        if (autoBuilder_ == null) {
          if (value == null) {
            throw new NullPointerException();
          }
          transformationDetail_ = value;
          onChanged();
        } else {
          autoBuilder_.setMessage(value);
        }
        transformationDetailCase_ = 1;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
       * </code>
       */
      public Builder setAuto(
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                  .Transformation.AutoTransformation.Builder
              builderForValue) {
        if (autoBuilder_ == null) {
          transformationDetail_ = builderForValue.build();
          onChanged();
        } else {
          autoBuilder_.setMessage(builderForValue.build());
        }
        transformationDetailCase_ = 1;
        return this;
      }
      /**
       * <code>
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       * </code>
       */
      public Builder mergeAuto(
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                  .Transformation.AutoTransformation
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        if (autoBuilder_ == null) {
          if (transformationDetailCase_ == 1
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            transformationDetail_ =
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.AutoTransformation.newBuilder(
                        (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                                .AutoMlForecastingInputs.Transformation.AutoTransformation)
                            transformationDetail_)
                    .mergeFrom(value)
                    .buildPartial();
          } else {
            transformationDetail_ = value;
          }
          onChanged();
        } else {
          if (transformationDetailCase_ == 1) {
            autoBuilder_.mergeFrom(value);
          } else {
            autoBuilder_.setMessage(value);
          }
        }
        transformationDetailCase_ = 1;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
       * </code>
       */
      public Builder clearAuto() {
        if (autoBuilder_ == null) {
          if (transformationDetailCase_ == 1) {
            transformationDetailCase_ = 0;
            transformationDetail_ = null;
            onChanged();
          }
        } else {
          if (transformationDetailCase_ == 1) {
            transformationDetailCase_ = 0;
            transformationDetail_ = null;
          }
          autoBuilder_.clear();
        }
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
       * </code>
       */
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.AutoTransformation.Builder
          getAutoBuilder() {
        return getAutoFieldBuilder().getBuilder();
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
       * </code>
       */
      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.AutoTransformationOrBuilder
          getAutoOrBuilder() {
        if ((transformationDetailCase_ == 1) && (autoBuilder_ != null)) {
          return autoBuilder_.getMessageOrBuilder();
        } else {
          if (transformationDetailCase_ == 1) {
            return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.AutoTransformation)
                transformationDetail_;
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.AutoTransformation.getDefaultInstance();
        }
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.AutoTransformation auto = 1;
       * </code>
       */
      private com.google.protobuf.SingleFieldBuilderV3<
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.AutoTransformation,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.AutoTransformation.Builder,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.AutoTransformationOrBuilder>
          getAutoFieldBuilder() {
        if (autoBuilder_ == null) {
          if (!(transformationDetailCase_ == 1)) {
            transformationDetail_ =
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.AutoTransformation.getDefaultInstance();
          }
          autoBuilder_ =
              new com.google.protobuf.SingleFieldBuilderV3<
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.AutoTransformation,
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.AutoTransformation.Builder,
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.AutoTransformationOrBuilder>(
                  (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                          .AutoMlForecastingInputs.Transformation.AutoTransformation)
                      transformationDetail_,
                  getParentForChildren(),
                  isClean());
          transformationDetail_ = null;
        }
        transformationDetailCase_ = 1;
        onChanged();
        return autoBuilder_;
      }

      private com.google.protobuf.SingleFieldBuilderV3<
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.NumericTransformation,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.NumericTransformation.Builder,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.NumericTransformationOrBuilder>
          numericBuilder_;
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       *
       * @return Whether the numeric field is set.
       */
      @java.lang.Override
      public boolean hasNumeric() {
        return transformationDetailCase_ == 2;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       *
       * @return The numeric.
       */
      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.NumericTransformation
          getNumeric() {
        if (numericBuilder_ == null) {
          if (transformationDetailCase_ == 2) {
            return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.NumericTransformation)
                transformationDetail_;
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.NumericTransformation.getDefaultInstance();
        } else {
          if (transformationDetailCase_ == 2) {
            return numericBuilder_.getMessage();
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.NumericTransformation.getDefaultInstance();
        }
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       */
      public Builder setNumeric(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.NumericTransformation
              value) {
        if (numericBuilder_ == null) {
          if (value == null) {
            throw new NullPointerException();
          }
          transformationDetail_ = value;
          onChanged();
        } else {
          numericBuilder_.setMessage(value);
        }
        transformationDetailCase_ = 2;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       */
      public Builder setNumeric(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.NumericTransformation.Builder
              builderForValue) {
        if (numericBuilder_ == null) {
          transformationDetail_ = builderForValue.build();
          onChanged();
        } else {
          numericBuilder_.setMessage(builderForValue.build());
        }
        transformationDetailCase_ = 2;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       */
      public Builder mergeNumeric(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.NumericTransformation
              value) {
        if (numericBuilder_ == null) {
          if (transformationDetailCase_ == 2
              && transformationDetail_
                  != com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.NumericTransformation
                      .getDefaultInstance()) {
            transformationDetail_ =
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.NumericTransformation.newBuilder(
                        (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                                .AutoMlForecastingInputs.Transformation.NumericTransformation)
                            transformationDetail_)
                    .mergeFrom(value)
                    .buildPartial();
          } else {
            transformationDetail_ = value;
          }
          onChanged();
        } else {
          if (transformationDetailCase_ == 2) {
            numericBuilder_.mergeFrom(value);
          } else {
            numericBuilder_.setMessage(value);
          }
        }
        transformationDetailCase_ = 2;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       */
      public Builder clearNumeric() {
        if (numericBuilder_ == null) {
          if (transformationDetailCase_ == 2) {
            transformationDetailCase_ = 0;
            transformationDetail_ = null;
            onChanged();
          }
        } else {
          if (transformationDetailCase_ == 2) {
            transformationDetailCase_ = 0;
            transformationDetail_ = null;
          }
          numericBuilder_.clear();
        }
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       */
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.NumericTransformation.Builder
          getNumericBuilder() {
        return getNumericFieldBuilder().getBuilder();
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       */
      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.NumericTransformationOrBuilder
          getNumericOrBuilder() {
        if ((transformationDetailCase_ == 2) && (numericBuilder_ != null)) {
          return numericBuilder_.getMessageOrBuilder();
        } else {
          if (transformationDetailCase_ == 2) {
            return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.NumericTransformation)
                transformationDetail_;
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.NumericTransformation.getDefaultInstance();
        }
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.NumericTransformation numeric = 2;
       * </code>
       */
      private com.google.protobuf.SingleFieldBuilderV3<
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.NumericTransformation,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.NumericTransformation.Builder,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.NumericTransformationOrBuilder>
          getNumericFieldBuilder() {
        if (numericBuilder_ == null) {
          if (!(transformationDetailCase_ == 2)) {
            transformationDetail_ =
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.NumericTransformation
                    .getDefaultInstance();
          }
          numericBuilder_ =
              new com.google.protobuf.SingleFieldBuilderV3<
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.NumericTransformation,
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.NumericTransformation.Builder,
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.NumericTransformationOrBuilder>(
                  (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                          .AutoMlForecastingInputs.Transformation.NumericTransformation)
                      transformationDetail_,
                  getParentForChildren(),
                  isClean());
          transformationDetail_ = null;
        }
        transformationDetailCase_ = 2;
        onChanged();
        return numericBuilder_;
      }

      private com.google.protobuf.SingleFieldBuilderV3<
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.CategoricalTransformation,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.CategoricalTransformation.Builder,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.CategoricalTransformationOrBuilder>
          categoricalBuilder_;
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       *
       * @return Whether the categorical field is set.
       */
      @java.lang.Override
      public boolean hasCategorical() {
        return transformationDetailCase_ == 3;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       *
       * @return The categorical.
       */
      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformation
          getCategorical() {
        if (categoricalBuilder_ == null) {
          if (transformationDetailCase_ == 3) {
            return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.CategoricalTransformation)
                transformationDetail_;
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformation
              .getDefaultInstance();
        } else {
          if (transformationDetailCase_ == 3) {
            return categoricalBuilder_.getMessage();
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformation
              .getDefaultInstance();
        }
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       */
      public Builder setCategorical(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.CategoricalTransformation
              value) {
        if (categoricalBuilder_ == null) {
          if (value == null) {
            throw new NullPointerException();
          }
          transformationDetail_ = value;
          onChanged();
        } else {
          categoricalBuilder_.setMessage(value);
        }
        transformationDetailCase_ = 3;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       */
      public Builder setCategorical(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.CategoricalTransformation.Builder
              builderForValue) {
        if (categoricalBuilder_ == null) {
          transformationDetail_ = builderForValue.build();
          onChanged();
        } else {
          categoricalBuilder_.setMessage(builderForValue.build());
        }
        transformationDetailCase_ = 3;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       */
      public Builder mergeCategorical(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.CategoricalTransformation
              value) {
        if (categoricalBuilder_ == null) {
          if (transformationDetailCase_ == 3
              && transformationDetail_
                  != com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.CategoricalTransformation
                      .getDefaultInstance()) {
            transformationDetail_ =
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.CategoricalTransformation.newBuilder(
                        (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                                .AutoMlForecastingInputs.Transformation.CategoricalTransformation)
                            transformationDetail_)
                    .mergeFrom(value)
                    .buildPartial();
          } else {
            transformationDetail_ = value;
          }
          onChanged();
        } else {
          if (transformationDetailCase_ == 3) {
            categoricalBuilder_.mergeFrom(value);
          } else {
            categoricalBuilder_.setMessage(value);
          }
        }
        transformationDetailCase_ = 3;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       */
      public Builder clearCategorical() {
        if (categoricalBuilder_ == null) {
          if (transformationDetailCase_ == 3) {
            transformationDetailCase_ = 0;
            transformationDetail_ = null;
            onChanged();
          }
        } else {
          if (transformationDetailCase_ == 3) {
            transformationDetailCase_ = 0;
            transformationDetail_ = null;
          }
          categoricalBuilder_.clear();
        }
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       */
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformation.Builder
          getCategoricalBuilder() {
        return getCategoricalFieldBuilder().getBuilder();
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       */
      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformationOrBuilder
          getCategoricalOrBuilder() {
        if ((transformationDetailCase_ == 3) && (categoricalBuilder_ != null)) {
          return categoricalBuilder_.getMessageOrBuilder();
        } else {
          if (transformationDetailCase_ == 3) {
            return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.CategoricalTransformation)
                transformationDetail_;
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.CategoricalTransformation
              .getDefaultInstance();
        }
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.CategoricalTransformation categorical = 3;
       * </code>
       */
      private com.google.protobuf.SingleFieldBuilderV3<
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.CategoricalTransformation,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.CategoricalTransformation.Builder,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.CategoricalTransformationOrBuilder>
          getCategoricalFieldBuilder() {
        if (categoricalBuilder_ == null) {
          if (!(transformationDetailCase_ == 3)) {
            transformationDetail_ =
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.CategoricalTransformation
                    .getDefaultInstance();
          }
          categoricalBuilder_ =
              new com.google.protobuf.SingleFieldBuilderV3<
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.CategoricalTransformation,
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.CategoricalTransformation.Builder,
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.CategoricalTransformationOrBuilder>(
                  (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                          .AutoMlForecastingInputs.Transformation.CategoricalTransformation)
                      transformationDetail_,
                  getParentForChildren(),
                  isClean());
          transformationDetail_ = null;
        }
        transformationDetailCase_ = 3;
        onChanged();
        return categoricalBuilder_;
      }

      private com.google.protobuf.SingleFieldBuilderV3<
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.TimestampTransformation,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.TimestampTransformation.Builder,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.TimestampTransformationOrBuilder>
          timestampBuilder_;
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
       * </code>
       *
       * @return Whether the timestamp field is set.
       */
      @java.lang.Override
      public boolean hasTimestamp() {
        return transformationDetailCase_ == 4;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
       * </code>
       *
       * @return The timestamp.
       */
      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformation
          getTimestamp() {
        if (timestampBuilder_ == null) {
          if (transformationDetailCase_ == 4) {
            return (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.TimestampTransformation)
                transformationDetail_;
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformation.getDefaultInstance();
        } else {
          if (transformationDetailCase_ == 4) {
            return timestampBuilder_.getMessage();
          }
          return com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformation.getDefaultInstance();
        }
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
       * </code>
       */
      public Builder setTimestamp(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.TimestampTransformation
              value) {
        if (timestampBuilder_ == null) {
          if (value == null) {
            throw new NullPointerException();
          }
          transformationDetail_ = value;
          onChanged();
        } else {
          timestampBuilder_.setMessage(value);
        }
        transformationDetailCase_ = 4;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
       * </code>
       */
      public Builder setTimestamp(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.TimestampTransformation.Builder
              builderForValue) {
        if (timestampBuilder_ == null) {
          transformationDetail_ = builderForValue.build();
          onChanged();
        } else {
          timestampBuilder_.setMessage(builderForValue.build());
        }
        transformationDetailCase_ = 4;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
       * </code>
       */
      public Builder mergeTimestamp(
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                  .Transformation.TimestampTransformation
              value) {
        if (timestampBuilder_ == null) {
          if (transformationDetailCase_ == 4
              && transformationDetail_
                  != com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.TimestampTransformation
                      .getDefaultInstance()) {
            transformationDetail_ =
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.TimestampTransformation.newBuilder(
                        (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                                .AutoMlForecastingInputs.Transformation.TimestampTransformation)
                            transformationDetail_)
                    .mergeFrom(value)
                    .buildPartial();
          } else {
            transformationDetail_ = value;
          }
          onChanged();
        } else {
          if (transformationDetailCase_ == 4) {
            timestampBuilder_.mergeFrom(value);
          } else {
            timestampBuilder_.setMessage(value);
          }
        }
        transformationDetailCase_ = 4;
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
       * </code>
       */
      public Builder clearTimestamp() {
        if (timestampBuilder_ == null) {
          if (transformationDetailCase_ == 4) {
            transformationDetailCase_ = 0;
            transformationDetail_ = null;
            onChanged();
          }
        } else {
          if (transformationDetailCase_ == 4) {
            transformationDetailCase_ = 0;
            transformationDetail_ = null;
          }
          timestampBuilder_.clear();
        }
        return this;
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
       * </code>
       */
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformation.Builder
          getTimestampBuilder() {
        return getTimestampFieldBuilder().getBuilder();
      }
      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TimestampTransformation timestamp = 4;
       * </code>
       */
      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.Transformation.TimestampTransformationOrBuilder
          getTimestampOrBuilder() {
        if ((transformationDetailCase_ == 4) && (timestampBuilder_ != null)) {
          return timestampBuilder_.getMessageOrBuilder();
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          if (transformationDetailCase_ == 4) {
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      /**
       * <code>
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       */
      private com.google.protobuf.SingleFieldBuilderV3<
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                  .AutoMlForecastingInputs.Transformation.TimestampTransformation,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.TimestampTransformation.Builder,
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          }
          timestampBuilder_ =
              new com.google.protobuf.SingleFieldBuilderV3<
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.TimestampTransformation,
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.TimestampTransformation.Builder,
                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.TimestampTransformationOrBuilder>(
                  (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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                      transformationDetail_,
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        transformationDetailCase_ = 4;
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        return timestampBuilder_;
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      private com.google.protobuf.SingleFieldBuilderV3<
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.TextTransformation,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.TextTransformation.Builder,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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      /**
       * <code>
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       * </code>
       *
       * @return Whether the text field is set.
       */
      @java.lang.Override
      public boolean hasText() {
        return transformationDetailCase_ == 5;
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      /**
       * <code>
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       * </code>
       *
       * @return The text.
       */
      @java.lang.Override
      public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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                transformationDetail_;
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      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TextTransformation text = 5;
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      public Builder setText(
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            throw new NullPointerException();
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          transformationDetail_ = value;
          onChanged();
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          textBuilder_.setMessage(value);
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        transformationDetailCase_ = 5;
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      /**
       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TextTransformation text = 5;
       * </code>
       */
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          onChanged();
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          textBuilder_.setMessage(builderForValue.build());
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        transformationDetailCase_ = 5;
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       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TextTransformation text = 5;
       * </code>
       */
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              && transformationDetail_
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            transformationDetail_ =
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                    .AutoMlForecastingInputs.Transformation.TextTransformation.newBuilder(
                        (com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                                .AutoMlForecastingInputs.Transformation.TextTransformation)
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                    .buildPartial();
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            transformationDetail_ = value;
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          onChanged();
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       * <code>
       * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation.TextTransformation text = 5;
       * </code>
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      public Builder clearText() {
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       * </code>
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      /**
       * <code>
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       * </code>
       */
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            transformationDetail_ =
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          textBuilder_ =
              new com.google.protobuf.SingleFieldBuilderV3<
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                  com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                      .AutoMlForecastingInputs.Transformation.TextTransformation.Builder,
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                      .AutoMlForecastingInputs.Transformation.TextTransformationOrBuilder>(
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        transformationDetailCase_ = 5;
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      @java.lang.Override
      public final Builder setUnknownFields(
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      @java.lang.Override
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    }

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            .AutoMlForecastingInputs.Transformation
        DEFAULT_INSTANCE;

    static {
      DEFAULT_INSTANCE =
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    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Transformation
        getDefaultInstance() {
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    }

    private static final com.google.protobuf.Parser<Transformation> PARSER =
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          public Transformation parsePartialFrom(
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            try {
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    public static com.google.protobuf.Parser<Transformation> parser() {
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    @java.lang.Override
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  public interface GranularityOrBuilder
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      com.google.protobuf.MessageOrBuilder {

    /**
     *
     *
     * <pre>
     * The time granularity unit of this time period.
     * The supported units are:
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     *  * "minute"
     *
     *  * "hour"
     *
     *  * "day"
     *
     *  * "week"
     *
     *  * "month"
     *
     *  * "year"
     * </pre>
     *
     * <code>string unit = 1;</code>
     *
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    /**
     *
     *
     * <pre>
     * The time granularity unit of this time period.
     * The supported units are:
     *
     *  * "minute"
     *
     *  * "hour"
     *
     *  * "day"
     *
     *  * "week"
     *
     *  * "month"
     *
     *  * "year"
     * </pre>
     *
     * <code>string unit = 1;</code>
     *
     * @return The bytes for unit.
     */
    com.google.protobuf.ByteString getUnitBytes();

    /**
     *
     *
     * <pre>
     * The number of granularity_units between data points in the training
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     * must be 1.
     * </pre>
     *
     * <code>int64 quantity = 2;</code>
     *
     * @return The quantity.
     */
    long getQuantity();
  }
  /**
   *
   *
   * <pre>
   * A duration of time expressed in time granularity units.
   * </pre>
   *
   * Protobuf type {@code
   * google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity}
   */
  public static final class Granularity extends com.google.protobuf.GeneratedMessageV3
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      GranularityOrBuilder {
    private static final long serialVersionUID = 0L;
    // Use Granularity.newBuilder() to construct.
    private Granularity(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) {
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    private Granularity() {
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    @java.lang.Override
    @SuppressWarnings({"unused"})
    protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
      return new Granularity();
    }

    public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
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          .internal_static_google_cloud_aiplatform_v1beta1_schema_trainingjob_definition_AutoMlForecastingInputs_Granularity_descriptor;
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    @java.lang.Override
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    @SuppressWarnings("serial")
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    /**
     *
     *
     * <pre>
     * The time granularity unit of this time period.
     * The supported units are:
     *
     *  * "minute"
     *
     *  * "hour"
     *
     *  * "day"
     *
     *  * "week"
     *
     *  * "month"
     *
     *  * "year"
     * </pre>
     *
     * <code>string unit = 1;</code>
     *
     * @return The unit.
     */
    @java.lang.Override
    public java.lang.String getUnit() {
      java.lang.Object ref = unit_;
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        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        unit_ = s;
        return s;
      }
    }
    /**
     *
     *
     * <pre>
     * The time granularity unit of this time period.
     * The supported units are:
     *
     *  * "minute"
     *
     *  * "hour"
     *
     *  * "day"
     *
     *  * "week"
     *
     *  * "month"
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      java.lang.Object ref = unit_;
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        com.google.protobuf.ByteString b =
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        unit_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }

    public static final int QUANTITY_FIELD_NUMBER = 2;
    private long quantity_ = 0L;
    /**
     *
     *
     * <pre>
     * The number of granularity_units between data points in the training
     * data. If `granularity_unit` is `minute`,
     * can be 1, 5, 10, 15, or 30. For all other values of `granularity_unit`,
     * must be 1.
     * </pre>
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      memoizedIsInitialized = 1;
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      if (quantity_ != 0L) {
        output.writeInt64(2, quantity_);
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      getUnknownFields().writeTo(output);
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      size = 0;
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    @java.lang.Override
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      if (!getUnit().equals(other.getUnit())) return false;
      if (getQuantity() != other.getQuantity()) return false;
      if (!getUnknownFields().equals(other.getUnknownFields())) return false;
      return true;
    }

    @java.lang.Override
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      if (memoizedHashCode != 0) {
        return memoizedHashCode;
      }
      int hash = 41;
      hash = (19 * hash) + getDescriptor().hashCode();
      hash = (37 * hash) + UNIT_FIELD_NUMBER;
      hash = (53 * hash) + getUnit().hashCode();
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      hash = (53 * hash) + com.google.protobuf.Internal.hashLong(getQuantity());
      hash = (29 * hash) + getUnknownFields().hashCode();
      memoizedHashCode = hash;
      return hash;
    }

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        parseFrom(java.nio.ByteBuffer data)
            throws com.google.protobuf.InvalidProtocolBufferException {
      return PARSER.parseFrom(data);
    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseFrom(
            java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws com.google.protobuf.InvalidProtocolBufferException {
      return PARSER.parseFrom(data, extensionRegistry);
    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseFrom(com.google.protobuf.ByteString data)
            throws com.google.protobuf.InvalidProtocolBufferException {
      return PARSER.parseFrom(data);
    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseFrom(
            com.google.protobuf.ByteString data,
            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws com.google.protobuf.InvalidProtocolBufferException {
      return PARSER.parseFrom(data, extensionRegistry);
    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
      return PARSER.parseFrom(data);
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws com.google.protobuf.InvalidProtocolBufferException {
      return PARSER.parseFrom(data, extensionRegistry);
    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseFrom(java.io.InputStream input) throws java.io.IOException {
      return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseFrom(
            java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws java.io.IOException {
      return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
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    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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        parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException {
      return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseDelimitedFrom(
            java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws java.io.IOException {
      return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
          PARSER, input, extensionRegistry);
    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException {
      return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
    }

    public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
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       *
       *  * "day"
       *
       *  * "week"
       *
       *  * "month"
       *
       *  * "year"
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       *
       * <code>string unit = 1;</code>
       *
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       *
       *  * "day"
       *
       *  * "week"
       *
       *  * "month"
       *
       *  * "year"
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       * <pre>
       * The time granularity unit of this time period.
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       *
       *  * "day"
       *
       *  * "week"
       *
       *  * "month"
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       *
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       *
       *  * "day"
       *
       *  * "week"
       *
       *  * "month"
       *
       *  * "year"
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       * must be 1.
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       *
       *
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       * must be 1.
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       * can be 1, 5, 10, 15, or 30. For all other values of `granularity_unit`,
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      // @@protoc_insertion_point(builder_scope:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity)
    }

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              .AutoMlForecastingInputs.Granularity();
    }

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            .AutoMlForecastingInputs.Granularity
        getDefaultInstance() {
      return DEFAULT_INSTANCE;
    }

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      return DEFAULT_INSTANCE;
    }
  }

  public static final int TARGET_COLUMN_FIELD_NUMBER = 1;

  @SuppressWarnings("serial")
  private volatile java.lang.Object targetColumn_ = "";
  /**
   *
   *
   * <pre>
   * The name of the column that the model is to predict.
   * </pre>
   *
   * <code>string target_column = 1;</code>
   *
   * @return The targetColumn.
   */
  @java.lang.Override
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  /**
   *
   *
   * <pre>
   * The name of the column that the model is to predict.
   * </pre>
   *
   * <code>string target_column = 1;</code>
   *
   * @return The bytes for targetColumn.
   */
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      targetColumn_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int TIME_SERIES_IDENTIFIER_COLUMN_FIELD_NUMBER = 2;

  @SuppressWarnings("serial")
  private volatile java.lang.Object timeSeriesIdentifierColumn_ = "";
  /**
   *
   *
   * <pre>
   * The name of the column that identifies the time series.
   * </pre>
   *
   * <code>string time_series_identifier_column = 2;</code>
   *
   * @return The timeSeriesIdentifierColumn.
   */
  @java.lang.Override
  public java.lang.String getTimeSeriesIdentifierColumn() {
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      return (java.lang.String) ref;
    } else {
      com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
      java.lang.String s = bs.toStringUtf8();
      timeSeriesIdentifierColumn_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * The name of the column that identifies the time series.
   * </pre>
   *
   * <code>string time_series_identifier_column = 2;</code>
   *
   * @return The bytes for timeSeriesIdentifierColumn.
   */
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      timeSeriesIdentifierColumn_ = b;
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  public static final int TIME_COLUMN_FIELD_NUMBER = 3;

  @SuppressWarnings("serial")
  private volatile java.lang.Object timeColumn_ = "";
  /**
   *
   *
   * <pre>
   * The name of the column that identifies time order in the time series.
   * </pre>
   *
   * <code>string time_column = 3;</code>
   *
   * @return The timeColumn.
   */
  @java.lang.Override
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      timeColumn_ = s;
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  }
  /**
   *
   *
   * <pre>
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   *
   * <code>string time_column = 3;</code>
   *
   * @return The bytes for timeColumn.
   */
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      timeColumn_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
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  }

  public static final int TRANSFORMATIONS_FIELD_NUMBER = 4;

  @SuppressWarnings("serial")
  private java.util.List<
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
              .Transformation>
      transformations_;
  /**
   *
   *
   * <pre>
   * Each transformation will apply transform function to given input column.
   * And the result will be used for training.
   * When creating transformation for BigQuery Struct column, the column should
   * be flattened using "." as the delimiter.
   * </pre>
   *
   * <code>
   * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
   * </code>
   */
  @java.lang.Override
  public java.util.List<
          com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
              .Transformation>
      getTransformationsList() {
    return transformations_;
  }
  /**
   *
   *
   * <pre>
   * Each transformation will apply transform function to given input column.
   * And the result will be used for training.
   * When creating transformation for BigQuery Struct column, the column should
   * be flattened using "." as the delimiter.
   * </pre>
   *
   * <code>
   * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
   * </code>
   */
  @java.lang.Override
  public java.util.List<
          ? extends
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.TransformationOrBuilder>
      getTransformationsOrBuilderList() {
    return transformations_;
  }
  /**
   *
   *
   * <pre>
   * Each transformation will apply transform function to given input column.
   * And the result will be used for training.
   * When creating transformation for BigQuery Struct column, the column should
   * be flattened using "." as the delimiter.
   * </pre>
   *
   * <code>
   * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
   * </code>
   */
  @java.lang.Override
  public int getTransformationsCount() {
    return transformations_.size();
  }
  /**
   *
   *
   * <pre>
   * Each transformation will apply transform function to given input column.
   * And the result will be used for training.
   * When creating transformation for BigQuery Struct column, the column should
   * be flattened using "." as the delimiter.
   * </pre>
   *
   * <code>
   * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
   * </code>
   */
  @java.lang.Override
  public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
          .Transformation
      getTransformations(int index) {
    return transformations_.get(index);
  }
  /**
   *
   *
   * <pre>
   * Each transformation will apply transform function to given input column.
   * And the result will be used for training.
   * When creating transformation for BigQuery Struct column, the column should
   * be flattened using "." as the delimiter.
   * </pre>
   *
   * <code>
   * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
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  public static final int OPTIMIZATION_OBJECTIVE_FIELD_NUMBER = 5;

  @SuppressWarnings("serial")
  private volatile java.lang.Object optimizationObjective_ = "";
  /**
   *
   *
   * <pre>
   * Objective function the model is optimizing towards. The training process
   * creates a model that optimizes the value of the objective
   * function over the validation set.
   *
   * The supported optimization objectives:
   *
   *   * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
   *
   *   * "minimize-mae" - Minimize mean-absolute error (MAE).
   *
   *   * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
   *
   *   * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
   *
   *   * "minimize-wape-mae" - Minimize the combination of weighted absolute
   *     percentage error (WAPE) and mean-absolute-error (MAE).
   *
   *   * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
   *     defined in `quantiles`.
   * </pre>
   *
   * <code>string optimization_objective = 5;</code>
   *
   * @return The optimizationObjective.
   */
  @java.lang.Override
  public java.lang.String getOptimizationObjective() {
    java.lang.Object ref = optimizationObjective_;
    if (ref instanceof java.lang.String) {
      return (java.lang.String) ref;
    } else {
      com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
      java.lang.String s = bs.toStringUtf8();
      optimizationObjective_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * Objective function the model is optimizing towards. The training process
   * creates a model that optimizes the value of the objective
   * function over the validation set.
   *
   * The supported optimization objectives:
   *
   *   * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
   *
   *   * "minimize-mae" - Minimize mean-absolute error (MAE).
   *
   *   * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
   *
   *   * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
   *
   *   * "minimize-wape-mae" - Minimize the combination of weighted absolute
   *     percentage error (WAPE) and mean-absolute-error (MAE).
   *
   *   * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
   *     defined in `quantiles`.
   * </pre>
   *
   * <code>string optimization_objective = 5;</code>
   *
   * @return The bytes for optimizationObjective.
   */
  @java.lang.Override
  public com.google.protobuf.ByteString getOptimizationObjectiveBytes() {
    java.lang.Object ref = optimizationObjective_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      optimizationObjective_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER = 6;
  private long trainBudgetMilliNodeHours_ = 0L;
  /**
   *
   *
   * <pre>
   * Required. The train budget of creating this model, expressed in milli node
   * hours i.e. 1,000 value in this field means 1 node hour.
   *
   * The training cost of the model will not exceed this budget. The final cost
   * will be attempted to be close to the budget, though may end up being (even)
   * noticeably smaller - at the backend's discretion. This especially may
   * happen when further model training ceases to provide any improvements.
   *
   * If the budget is set to a value known to be insufficient to train a
   * model for the given dataset, the training won't be attempted and
   * will error.
   *
   * The train budget must be between 1,000 and 72,000 milli node hours,
   * inclusive.
   * </pre>
   *
   * <code>int64 train_budget_milli_node_hours = 6;</code>
   *
   * @return The trainBudgetMilliNodeHours.
   */
  @java.lang.Override
  public long getTrainBudgetMilliNodeHours() {
    return trainBudgetMilliNodeHours_;
  }

  public static final int WEIGHT_COLUMN_FIELD_NUMBER = 7;

  @SuppressWarnings("serial")
  private volatile java.lang.Object weightColumn_ = "";
  /**
   *
   *
   * <pre>
   * Column name that should be used as the weight column.
   * Higher values in this column give more importance to the row
   * during model training. The column must have numeric values between 0 and
   * 10000 inclusively; 0 means the row is ignored for training. If weight
   * column field is not set, then all rows are assumed to have equal weight
   * of 1.
   * </pre>
   *
   * <code>string weight_column = 7;</code>
   *
   * @return The weightColumn.
   */
  @java.lang.Override
  public java.lang.String getWeightColumn() {
    java.lang.Object ref = weightColumn_;
    if (ref instanceof java.lang.String) {
      return (java.lang.String) ref;
    } else {
      com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
      java.lang.String s = bs.toStringUtf8();
      weightColumn_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * Column name that should be used as the weight column.
   * Higher values in this column give more importance to the row
   * during model training. The column must have numeric values between 0 and
   * 10000 inclusively; 0 means the row is ignored for training. If weight
   * column field is not set, then all rows are assumed to have equal weight
   * of 1.
   * </pre>
   *
   * <code>string weight_column = 7;</code>
   *
   * @return The bytes for weightColumn.
   */
  @java.lang.Override
  public com.google.protobuf.ByteString getWeightColumnBytes() {
    java.lang.Object ref = weightColumn_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      weightColumn_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int TIME_SERIES_ATTRIBUTE_COLUMNS_FIELD_NUMBER = 19;

  @SuppressWarnings("serial")
  private com.google.protobuf.LazyStringArrayList timeSeriesAttributeColumns_ =
      com.google.protobuf.LazyStringArrayList.emptyList();
  /**
   *
   *
   * <pre>
   * Column names that should be used as attribute columns.
   * The value of these columns does not vary as a function of time.
   * For example, store ID or item color.
   * </pre>
   *
   * <code>repeated string time_series_attribute_columns = 19;</code>
   *
   * @return A list containing the timeSeriesAttributeColumns.
   */
  public com.google.protobuf.ProtocolStringList getTimeSeriesAttributeColumnsList() {
    return timeSeriesAttributeColumns_;
  }
  /**
   *
   *
   * <pre>
   * Column names that should be used as attribute columns.
   * The value of these columns does not vary as a function of time.
   * For example, store ID or item color.
   * </pre>
   *
   * <code>repeated string time_series_attribute_columns = 19;</code>
   *
   * @return The count of timeSeriesAttributeColumns.
   */
  public int getTimeSeriesAttributeColumnsCount() {
    return timeSeriesAttributeColumns_.size();
  }
  /**
   *
   *
   * <pre>
   * Column names that should be used as attribute columns.
   * The value of these columns does not vary as a function of time.
   * For example, store ID or item color.
   * </pre>
   *
   * <code>repeated string time_series_attribute_columns = 19;</code>
   *
   * @param index The index of the element to return.
   * @return The timeSeriesAttributeColumns at the given index.
   */
  public java.lang.String getTimeSeriesAttributeColumns(int index) {
    return timeSeriesAttributeColumns_.get(index);
  }
  /**
   *
   *
   * <pre>
   * Column names that should be used as attribute columns.
   * The value of these columns does not vary as a function of time.
   * For example, store ID or item color.
   * </pre>
   *
   * <code>repeated string time_series_attribute_columns = 19;</code>
   *
   * @param index The index of the value to return.
   * @return The bytes of the timeSeriesAttributeColumns at the given index.
   */
  public com.google.protobuf.ByteString getTimeSeriesAttributeColumnsBytes(int index) {
    return timeSeriesAttributeColumns_.getByteString(index);
  }

  public static final int UNAVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER = 20;

  @SuppressWarnings("serial")
  private com.google.protobuf.LazyStringArrayList unavailableAtForecastColumns_ =
      com.google.protobuf.LazyStringArrayList.emptyList();
  /**
   *
   *
   * <pre>
   * Names of columns that are unavailable when a forecast is requested.
   * This column contains information for the given entity (identified
   * by the time_series_identifier_column) that is unknown before the forecast
   * For example, actual weather on a given day.
   * </pre>
   *
   * <code>repeated string unavailable_at_forecast_columns = 20;</code>
   *
   * @return A list containing the unavailableAtForecastColumns.
   */
  public com.google.protobuf.ProtocolStringList getUnavailableAtForecastColumnsList() {
    return unavailableAtForecastColumns_;
  }
  /**
   *
   *
   * <pre>
   * Names of columns that are unavailable when a forecast is requested.
   * This column contains information for the given entity (identified
   * by the time_series_identifier_column) that is unknown before the forecast
   * For example, actual weather on a given day.
   * </pre>
   *
   * <code>repeated string unavailable_at_forecast_columns = 20;</code>
   *
   * @return The count of unavailableAtForecastColumns.
   */
  public int getUnavailableAtForecastColumnsCount() {
    return unavailableAtForecastColumns_.size();
  }
  /**
   *
   *
   * <pre>
   * Names of columns that are unavailable when a forecast is requested.
   * This column contains information for the given entity (identified
   * by the time_series_identifier_column) that is unknown before the forecast
   * For example, actual weather on a given day.
   * </pre>
   *
   * <code>repeated string unavailable_at_forecast_columns = 20;</code>
   *
   * @param index The index of the element to return.
   * @return The unavailableAtForecastColumns at the given index.
   */
  public java.lang.String getUnavailableAtForecastColumns(int index) {
    return unavailableAtForecastColumns_.get(index);
  }
  /**
   *
   *
   * <pre>
   * Names of columns that are unavailable when a forecast is requested.
   * This column contains information for the given entity (identified
   * by the time_series_identifier_column) that is unknown before the forecast
   * For example, actual weather on a given day.
   * </pre>
   *
   * <code>repeated string unavailable_at_forecast_columns = 20;</code>
   *
   * @param index The index of the value to return.
   * @return The bytes of the unavailableAtForecastColumns at the given index.
   */
  public com.google.protobuf.ByteString getUnavailableAtForecastColumnsBytes(int index) {
    return unavailableAtForecastColumns_.getByteString(index);
  }

  public static final int AVAILABLE_AT_FORECAST_COLUMNS_FIELD_NUMBER = 21;

  @SuppressWarnings("serial")
  private com.google.protobuf.LazyStringArrayList availableAtForecastColumns_ =
      com.google.protobuf.LazyStringArrayList.emptyList();
  /**
   *
   *
   * <pre>
   * Names of columns that are available and provided when a forecast
   * is requested. These columns
   * contain information for the given entity (identified by the
   * time_series_identifier_column column) that is known at forecast.
   * For example, predicted weather for a specific day.
   * </pre>
   *
   * <code>repeated string available_at_forecast_columns = 21;</code>
   *
   * @return A list containing the availableAtForecastColumns.
   */
  public com.google.protobuf.ProtocolStringList getAvailableAtForecastColumnsList() {
    return availableAtForecastColumns_;
  }
  /**
   *
   *
   * <pre>
   * Names of columns that are available and provided when a forecast
   * is requested. These columns
   * contain information for the given entity (identified by the
   * time_series_identifier_column column) that is known at forecast.
   * For example, predicted weather for a specific day.
   * </pre>
   *
   * <code>repeated string available_at_forecast_columns = 21;</code>
   *
   * @return The count of availableAtForecastColumns.
   */
  public int getAvailableAtForecastColumnsCount() {
    return availableAtForecastColumns_.size();
  }
  /**
   *
   *
   * <pre>
   * Names of columns that are available and provided when a forecast
   * is requested. These columns
   * contain information for the given entity (identified by the
   * time_series_identifier_column column) that is known at forecast.
   * For example, predicted weather for a specific day.
   * </pre>
   *
   * <code>repeated string available_at_forecast_columns = 21;</code>
   *
   * @param index The index of the element to return.
   * @return The availableAtForecastColumns at the given index.
   */
  public java.lang.String getAvailableAtForecastColumns(int index) {
    return availableAtForecastColumns_.get(index);
  }
  /**
   *
   *
   * <pre>
   * Names of columns that are available and provided when a forecast
   * is requested. These columns
   * contain information for the given entity (identified by the
   * time_series_identifier_column column) that is known at forecast.
   * For example, predicted weather for a specific day.
   * </pre>
   *
   * <code>repeated string available_at_forecast_columns = 21;</code>
   *
   * @param index The index of the value to return.
   * @return The bytes of the availableAtForecastColumns at the given index.
   */
  public com.google.protobuf.ByteString getAvailableAtForecastColumnsBytes(int index) {
    return availableAtForecastColumns_.getByteString(index);
  }

  public static final int DATA_GRANULARITY_FIELD_NUMBER = 22;
  private com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
          .Granularity
      dataGranularity_;
  /**
   *
   *
   * <pre>
   * Expected difference in time granularity between rows in the data.
   * </pre>
   *
   * <code>
   * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
   * </code>
   *
   * @return Whether the dataGranularity field is set.
   */
  @java.lang.Override
  public boolean hasDataGranularity() {
    return dataGranularity_ != null;
  }
  /**
   *
   *
   * <pre>
   * Expected difference in time granularity between rows in the data.
   * </pre>
   *
   * <code>
   * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
   * </code>
   *
   * @return The dataGranularity.
   */
  @java.lang.Override
  public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
          .Granularity
      getDataGranularity() {
    return dataGranularity_ == null
        ? com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Granularity.getDefaultInstance()
        : dataGranularity_;
  }
  /**
   *
   *
   * <pre>
   * Expected difference in time granularity between rows in the data.
   * </pre>
   *
   * <code>
   * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
   * </code>
   */
  @java.lang.Override
  public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
          .GranularityOrBuilder
      getDataGranularityOrBuilder() {
    return dataGranularity_ == null
        ? com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Granularity.getDefaultInstance()
        : dataGranularity_;
  }

  public static final int FORECAST_HORIZON_FIELD_NUMBER = 23;
  private long forecastHorizon_ = 0L;
  /**
   *
   *
   * <pre>
   * The amount of time into the future for which forecasted values for the
   * target are returned. Expressed in number of units defined by the
   * `data_granularity` field.
   * </pre>
   *
   * <code>int64 forecast_horizon = 23;</code>
   *
   * @return The forecastHorizon.
   */
  @java.lang.Override
  public long getForecastHorizon() {
    return forecastHorizon_;
  }

  public static final int CONTEXT_WINDOW_FIELD_NUMBER = 24;
  private long contextWindow_ = 0L;
  /**
   *
   *
   * <pre>
   * The amount of time into the past training and prediction data is used
   * for model training and prediction respectively. Expressed in number of
   * units defined by the `data_granularity` field.
   * </pre>
   *
   * <code>int64 context_window = 24;</code>
   *
   * @return The contextWindow.
   */
  @java.lang.Override
  public long getContextWindow() {
    return contextWindow_;
  }

  public static final int EXPORT_EVALUATED_DATA_ITEMS_CONFIG_FIELD_NUMBER = 15;
  private com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .ExportEvaluatedDataItemsConfig
      exportEvaluatedDataItemsConfig_;
  /**
   *
   *
   * <pre>
   * Configuration for exporting test set predictions to a BigQuery table. If
   * this configuration is absent, then the export is not performed.
   * </pre>
   *
   * <code>
   * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
   * </code>
   *
   * @return Whether the exportEvaluatedDataItemsConfig field is set.
   */
  @java.lang.Override
  public boolean hasExportEvaluatedDataItemsConfig() {
    return exportEvaluatedDataItemsConfig_ != null;
  }
  /**
   *
   *
   * <pre>
   * Configuration for exporting test set predictions to a BigQuery table. If
   * this configuration is absent, then the export is not performed.
   * </pre>
   *
   * <code>
   * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
   * </code>
   *
   * @return The exportEvaluatedDataItemsConfig.
   */
  @java.lang.Override
  public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .ExportEvaluatedDataItemsConfig
      getExportEvaluatedDataItemsConfig() {
    return exportEvaluatedDataItemsConfig_ == null
        ? com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .ExportEvaluatedDataItemsConfig.getDefaultInstance()
        : exportEvaluatedDataItemsConfig_;
  }
  /**
   *
   *
   * <pre>
   * Configuration for exporting test set predictions to a BigQuery table. If
   * this configuration is absent, then the export is not performed.
   * </pre>
   *
   * <code>
   * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
   * </code>
   */
  @java.lang.Override
  public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .ExportEvaluatedDataItemsConfigOrBuilder
      getExportEvaluatedDataItemsConfigOrBuilder() {
    return exportEvaluatedDataItemsConfig_ == null
        ? com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .ExportEvaluatedDataItemsConfig.getDefaultInstance()
        : exportEvaluatedDataItemsConfig_;
  }

  public static final int QUANTILES_FIELD_NUMBER = 16;

  @SuppressWarnings("serial")
  private com.google.protobuf.Internal.DoubleList quantiles_;
  /**
   *
   *
   * <pre>
   * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
   * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
   * the value of optimization_objective is minimize-quantile-loss. Represents
   * the percent quantiles to use for that objective. Quantiles must be unique.
   * </pre>
   *
   * <code>repeated double quantiles = 16;</code>
   *
   * @return A list containing the quantiles.
   */
  @java.lang.Override
  public java.util.List<java.lang.Double> getQuantilesList() {
    return quantiles_;
  }
  /**
   *
   *
   * <pre>
   * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
   * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
   * the value of optimization_objective is minimize-quantile-loss. Represents
   * the percent quantiles to use for that objective. Quantiles must be unique.
   * </pre>
   *
   * <code>repeated double quantiles = 16;</code>
   *
   * @return The count of quantiles.
   */
  public int getQuantilesCount() {
    return quantiles_.size();
  }
  /**
   *
   *
   * <pre>
   * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
   * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
   * the value of optimization_objective is minimize-quantile-loss. Represents
   * the percent quantiles to use for that objective. Quantiles must be unique.
   * </pre>
   *
   * <code>repeated double quantiles = 16;</code>
   *
   * @param index The index of the element to return.
   * @return The quantiles at the given index.
   */
  public double getQuantiles(int index) {
    return quantiles_.getDouble(index);
  }

  private int quantilesMemoizedSerializedSize = -1;

  public static final int VALIDATION_OPTIONS_FIELD_NUMBER = 17;

  @SuppressWarnings("serial")
  private volatile java.lang.Object validationOptions_ = "";
  /**
   *
   *
   * <pre>
   * Validation options for the data validation component. The available options
   * are:
   *
   *   * "fail-pipeline" - default, will validate against the validation and
   *      fail the pipeline if it fails.
   *
   *   * "ignore-validation" - ignore the results of the validation and continue
   * </pre>
   *
   * <code>string validation_options = 17;</code>
   *
   * @return The validationOptions.
   */
  @java.lang.Override
  public java.lang.String getValidationOptions() {
    java.lang.Object ref = validationOptions_;
    if (ref instanceof java.lang.String) {
      return (java.lang.String) ref;
    } else {
      com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
      java.lang.String s = bs.toStringUtf8();
      validationOptions_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * Validation options for the data validation component. The available options
   * are:
   *
   *   * "fail-pipeline" - default, will validate against the validation and
   *      fail the pipeline if it fails.
   *
   *   * "ignore-validation" - ignore the results of the validation and continue
   * </pre>
   *
   * <code>string validation_options = 17;</code>
   *
   * @return The bytes for validationOptions.
   */
  @java.lang.Override
  public com.google.protobuf.ByteString getValidationOptionsBytes() {
    java.lang.Object ref = validationOptions_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      validationOptions_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int ADDITIONAL_EXPERIMENTS_FIELD_NUMBER = 25;

  @SuppressWarnings("serial")
  private com.google.protobuf.LazyStringArrayList additionalExperiments_ =
      com.google.protobuf.LazyStringArrayList.emptyList();
  /**
   *
   *
   * <pre>
   * Additional experiment flags for the time series forcasting training.
   * </pre>
   *
   * <code>repeated string additional_experiments = 25;</code>
   *
   * @return A list containing the additionalExperiments.
   */
  public com.google.protobuf.ProtocolStringList getAdditionalExperimentsList() {
    return additionalExperiments_;
  }
  /**
   *
   *
   * <pre>
   * Additional experiment flags for the time series forcasting training.
   * </pre>
   *
   * <code>repeated string additional_experiments = 25;</code>
   *
   * @return The count of additionalExperiments.
   */
  public int getAdditionalExperimentsCount() {
    return additionalExperiments_.size();
  }
  /**
   *
   *
   * <pre>
   * Additional experiment flags for the time series forcasting training.
   * </pre>
   *
   * <code>repeated string additional_experiments = 25;</code>
   *
   * @param index The index of the element to return.
   * @return The additionalExperiments at the given index.
   */
  public java.lang.String getAdditionalExperiments(int index) {
    return additionalExperiments_.get(index);
  }
  /**
   *
   *
   * <pre>
   * Additional experiment flags for the time series forcasting training.
   * </pre>
   *
   * <code>repeated string additional_experiments = 25;</code>
   *
   * @param index The index of the value to return.
   * @return The bytes of the additionalExperiments at the given index.
   */
  public com.google.protobuf.ByteString getAdditionalExperimentsBytes(int index) {
    return additionalExperiments_.getByteString(index);
  }

  private byte memoizedIsInitialized = -1;

  @java.lang.Override
  public final boolean isInitialized() {
    byte isInitialized = memoizedIsInitialized;
    if (isInitialized == 1) return true;
    if (isInitialized == 0) return false;

    memoizedIsInitialized = 1;
    return true;
  }

  @java.lang.Override
  public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
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        super.mergeFrom(other);
        return this;
      }
    }

    public Builder mergeFrom(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            other) {
      if (other
          == com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
              .AutoMlForecastingInputs.getDefaultInstance()) return this;
      if (!other.getTargetColumn().isEmpty()) {
        targetColumn_ = other.targetColumn_;
        bitField0_ |= 0x00000001;
        onChanged();
      }
      if (!other.getTimeSeriesIdentifierColumn().isEmpty()) {
        timeSeriesIdentifierColumn_ = other.timeSeriesIdentifierColumn_;
        bitField0_ |= 0x00000002;
        onChanged();
      }
      if (!other.getTimeColumn().isEmpty()) {
        timeColumn_ = other.timeColumn_;
        bitField0_ |= 0x00000004;
        onChanged();
      }
      if (transformationsBuilder_ == null) {
        if (!other.transformations_.isEmpty()) {
          if (transformations_.isEmpty()) {
            transformations_ = other.transformations_;
            bitField0_ = (bitField0_ & ~0x00000008);
          } else {
            ensureTransformationsIsMutable();
            transformations_.addAll(other.transformations_);
          }
          onChanged();
        }
      } else {
        if (!other.transformations_.isEmpty()) {
          if (transformationsBuilder_.isEmpty()) {
            transformationsBuilder_.dispose();
            transformationsBuilder_ = null;
            transformations_ = other.transformations_;
            bitField0_ = (bitField0_ & ~0x00000008);
            transformationsBuilder_ =
                com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders
                    ? getTransformationsFieldBuilder()
                    : null;
          } else {
            transformationsBuilder_.addAllMessages(other.transformations_);
          }
        }
      }
      if (!other.getOptimizationObjective().isEmpty()) {
        optimizationObjective_ = other.optimizationObjective_;
        bitField0_ |= 0x00000010;
        onChanged();
      }
      if (other.getTrainBudgetMilliNodeHours() != 0L) {
        setTrainBudgetMilliNodeHours(other.getTrainBudgetMilliNodeHours());
      }
      if (!other.getWeightColumn().isEmpty()) {
        weightColumn_ = other.weightColumn_;
        bitField0_ |= 0x00000040;
        onChanged();
      }
      if (!other.timeSeriesAttributeColumns_.isEmpty()) {
        if (timeSeriesAttributeColumns_.isEmpty()) {
          timeSeriesAttributeColumns_ = other.timeSeriesAttributeColumns_;
          bitField0_ |= 0x00000080;
        } else {
          ensureTimeSeriesAttributeColumnsIsMutable();
          timeSeriesAttributeColumns_.addAll(other.timeSeriesAttributeColumns_);
        }
        onChanged();
      }
      if (!other.unavailableAtForecastColumns_.isEmpty()) {
        if (unavailableAtForecastColumns_.isEmpty()) {
          unavailableAtForecastColumns_ = other.unavailableAtForecastColumns_;
          bitField0_ |= 0x00000100;
        } else {
          ensureUnavailableAtForecastColumnsIsMutable();
          unavailableAtForecastColumns_.addAll(other.unavailableAtForecastColumns_);
        }
        onChanged();
      }
      if (!other.availableAtForecastColumns_.isEmpty()) {
        if (availableAtForecastColumns_.isEmpty()) {
          availableAtForecastColumns_ = other.availableAtForecastColumns_;
          bitField0_ |= 0x00000200;
        } else {
          ensureAvailableAtForecastColumnsIsMutable();
          availableAtForecastColumns_.addAll(other.availableAtForecastColumns_);
        }
        onChanged();
      }
      if (other.hasDataGranularity()) {
        mergeDataGranularity(other.getDataGranularity());
      }
      if (other.getForecastHorizon() != 0L) {
        setForecastHorizon(other.getForecastHorizon());
      }
      if (other.getContextWindow() != 0L) {
        setContextWindow(other.getContextWindow());
      }
      if (other.hasExportEvaluatedDataItemsConfig()) {
        mergeExportEvaluatedDataItemsConfig(other.getExportEvaluatedDataItemsConfig());
      }
      if (!other.quantiles_.isEmpty()) {
        if (quantiles_.isEmpty()) {
          quantiles_ = other.quantiles_;
          bitField0_ = (bitField0_ & ~0x00004000);
        } else {
          ensureQuantilesIsMutable();
          quantiles_.addAll(other.quantiles_);
        }
        onChanged();
      }
      if (!other.getValidationOptions().isEmpty()) {
        validationOptions_ = other.validationOptions_;
        bitField0_ |= 0x00008000;
        onChanged();
      }
      if (!other.additionalExperiments_.isEmpty()) {
        if (additionalExperiments_.isEmpty()) {
          additionalExperiments_ = other.additionalExperiments_;
          bitField0_ |= 0x00010000;
        } else {
          ensureAdditionalExperimentsIsMutable();
          additionalExperiments_.addAll(other.additionalExperiments_);
        }
        onChanged();
      }
      this.mergeUnknownFields(other.getUnknownFields());
      onChanged();
      return this;
    }

    @java.lang.Override
    public final boolean isInitialized() {
      return true;
    }

    @java.lang.Override
    public Builder mergeFrom(
        com.google.protobuf.CodedInputStream input,
        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
        throws java.io.IOException {
      if (extensionRegistry == null) {
        throw new java.lang.NullPointerException();
      }
      try {
        boolean done = false;
        while (!done) {
          int tag = input.readTag();
          switch (tag) {
            case 0:
              done = true;
              break;
            case 10:
              {
                targetColumn_ = input.readStringRequireUtf8();
                bitField0_ |= 0x00000001;
                break;
              } // case 10
            case 18:
              {
                timeSeriesIdentifierColumn_ = input.readStringRequireUtf8();
                bitField0_ |= 0x00000002;
                break;
              } // case 18
            case 26:
              {
                timeColumn_ = input.readStringRequireUtf8();
                bitField0_ |= 0x00000004;
                break;
              } // case 26
            case 34:
              {
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                        .AutoMlForecastingInputs.Transformation
                    m =
                        input.readMessage(
                            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                                .AutoMlForecastingInputs.Transformation.parser(),
                            extensionRegistry);
                if (transformationsBuilder_ == null) {
                  ensureTransformationsIsMutable();
                  transformations_.add(m);
                } else {
                  transformationsBuilder_.addMessage(m);
                }
                break;
              } // case 34
            case 42:
              {
                optimizationObjective_ = input.readStringRequireUtf8();
                bitField0_ |= 0x00000010;
                break;
              } // case 42
            case 48:
              {
                trainBudgetMilliNodeHours_ = input.readInt64();
                bitField0_ |= 0x00000020;
                break;
              } // case 48
            case 58:
              {
                weightColumn_ = input.readStringRequireUtf8();
                bitField0_ |= 0x00000040;
                break;
              } // case 58
            case 122:
              {
                input.readMessage(
                    getExportEvaluatedDataItemsConfigFieldBuilder().getBuilder(),
                    extensionRegistry);
                bitField0_ |= 0x00002000;
                break;
              } // case 122
            case 129:
              {
                double v = input.readDouble();
                ensureQuantilesIsMutable();
                quantiles_.addDouble(v);
                break;
              } // case 129
            case 130:
              {
                int length = input.readRawVarint32();
                int limit = input.pushLimit(length);
                ensureQuantilesIsMutable();
                while (input.getBytesUntilLimit() > 0) {
                  quantiles_.addDouble(input.readDouble());
                }
                input.popLimit(limit);
                break;
              } // case 130
            case 138:
              {
                validationOptions_ = input.readStringRequireUtf8();
                bitField0_ |= 0x00008000;
                break;
              } // case 138
            case 154:
              {
                java.lang.String s = input.readStringRequireUtf8();
                ensureTimeSeriesAttributeColumnsIsMutable();
                timeSeriesAttributeColumns_.add(s);
                break;
              } // case 154
            case 162:
              {
                java.lang.String s = input.readStringRequireUtf8();
                ensureUnavailableAtForecastColumnsIsMutable();
                unavailableAtForecastColumns_.add(s);
                break;
              } // case 162
            case 170:
              {
                java.lang.String s = input.readStringRequireUtf8();
                ensureAvailableAtForecastColumnsIsMutable();
                availableAtForecastColumns_.add(s);
                break;
              } // case 170
            case 178:
              {
                input.readMessage(getDataGranularityFieldBuilder().getBuilder(), extensionRegistry);
                bitField0_ |= 0x00000400;
                break;
              } // case 178
            case 184:
              {
                forecastHorizon_ = input.readInt64();
                bitField0_ |= 0x00000800;
                break;
              } // case 184
            case 192:
              {
                contextWindow_ = input.readInt64();
                bitField0_ |= 0x00001000;
                break;
              } // case 192
            case 202:
              {
                java.lang.String s = input.readStringRequireUtf8();
                ensureAdditionalExperimentsIsMutable();
                additionalExperiments_.add(s);
                break;
              } // case 202
            default:
              {
                if (!super.parseUnknownField(input, extensionRegistry, tag)) {
                  done = true; // was an endgroup tag
                }
                break;
              } // default:
          } // switch (tag)
        } // while (!done)
      } catch (com.google.protobuf.InvalidProtocolBufferException e) {
        throw e.unwrapIOException();
      } finally {
        onChanged();
      } // finally
      return this;
    }

    private int bitField0_;

    private java.lang.Object targetColumn_ = "";
    /**
     *
     *
     * <pre>
     * The name of the column that the model is to predict.
     * </pre>
     *
     * <code>string target_column = 1;</code>
     *
     * @return The targetColumn.
     */
    public java.lang.String getTargetColumn() {
      java.lang.Object ref = targetColumn_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        targetColumn_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * The name of the column that the model is to predict.
     * </pre>
     *
     * <code>string target_column = 1;</code>
     *
     * @return The bytes for targetColumn.
     */
    public com.google.protobuf.ByteString getTargetColumnBytes() {
      java.lang.Object ref = targetColumn_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        targetColumn_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * The name of the column that the model is to predict.
     * </pre>
     *
     * <code>string target_column = 1;</code>
     *
     * @param value The targetColumn to set.
     * @return This builder for chaining.
     */
    public Builder setTargetColumn(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      targetColumn_ = value;
      bitField0_ |= 0x00000001;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * The name of the column that the model is to predict.
     * </pre>
     *
     * <code>string target_column = 1;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearTargetColumn() {
      targetColumn_ = getDefaultInstance().getTargetColumn();
      bitField0_ = (bitField0_ & ~0x00000001);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * The name of the column that the model is to predict.
     * </pre>
     *
     * <code>string target_column = 1;</code>
     *
     * @param value The bytes for targetColumn to set.
     * @return This builder for chaining.
     */
    public Builder setTargetColumnBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      targetColumn_ = value;
      bitField0_ |= 0x00000001;
      onChanged();
      return this;
    }

    private java.lang.Object timeSeriesIdentifierColumn_ = "";
    /**
     *
     *
     * <pre>
     * The name of the column that identifies the time series.
     * </pre>
     *
     * <code>string time_series_identifier_column = 2;</code>
     *
     * @return The timeSeriesIdentifierColumn.
     */
    public java.lang.String getTimeSeriesIdentifierColumn() {
      java.lang.Object ref = timeSeriesIdentifierColumn_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        timeSeriesIdentifierColumn_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * The name of the column that identifies the time series.
     * </pre>
     *
     * <code>string time_series_identifier_column = 2;</code>
     *
     * @return The bytes for timeSeriesIdentifierColumn.
     */
    public com.google.protobuf.ByteString getTimeSeriesIdentifierColumnBytes() {
      java.lang.Object ref = timeSeriesIdentifierColumn_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        timeSeriesIdentifierColumn_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * The name of the column that identifies the time series.
     * </pre>
     *
     * <code>string time_series_identifier_column = 2;</code>
     *
     * @param value The timeSeriesIdentifierColumn to set.
     * @return This builder for chaining.
     */
    public Builder setTimeSeriesIdentifierColumn(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      timeSeriesIdentifierColumn_ = value;
      bitField0_ |= 0x00000002;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * The name of the column that identifies the time series.
     * </pre>
     *
     * <code>string time_series_identifier_column = 2;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearTimeSeriesIdentifierColumn() {
      timeSeriesIdentifierColumn_ = getDefaultInstance().getTimeSeriesIdentifierColumn();
      bitField0_ = (bitField0_ & ~0x00000002);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * The name of the column that identifies the time series.
     * </pre>
     *
     * <code>string time_series_identifier_column = 2;</code>
     *
     * @param value The bytes for timeSeriesIdentifierColumn to set.
     * @return This builder for chaining.
     */
    public Builder setTimeSeriesIdentifierColumnBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      timeSeriesIdentifierColumn_ = value;
      bitField0_ |= 0x00000002;
      onChanged();
      return this;
    }

    private java.lang.Object timeColumn_ = "";
    /**
     *
     *
     * <pre>
     * The name of the column that identifies time order in the time series.
     * </pre>
     *
     * <code>string time_column = 3;</code>
     *
     * @return The timeColumn.
     */
    public java.lang.String getTimeColumn() {
      java.lang.Object ref = timeColumn_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        timeColumn_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * The name of the column that identifies time order in the time series.
     * </pre>
     *
     * <code>string time_column = 3;</code>
     *
     * @return The bytes for timeColumn.
     */
    public com.google.protobuf.ByteString getTimeColumnBytes() {
      java.lang.Object ref = timeColumn_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        timeColumn_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * The name of the column that identifies time order in the time series.
     * </pre>
     *
     * <code>string time_column = 3;</code>
     *
     * @param value The timeColumn to set.
     * @return This builder for chaining.
     */
    public Builder setTimeColumn(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      timeColumn_ = value;
      bitField0_ |= 0x00000004;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * The name of the column that identifies time order in the time series.
     * </pre>
     *
     * <code>string time_column = 3;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearTimeColumn() {
      timeColumn_ = getDefaultInstance().getTimeColumn();
      bitField0_ = (bitField0_ & ~0x00000004);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * The name of the column that identifies time order in the time series.
     * </pre>
     *
     * <code>string time_column = 3;</code>
     *
     * @param value The bytes for timeColumn to set.
     * @return This builder for chaining.
     */
    public Builder setTimeColumnBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      timeColumn_ = value;
      bitField0_ |= 0x00000004;
      onChanged();
      return this;
    }

    private java.util.List<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation>
        transformations_ = java.util.Collections.emptyList();

    private void ensureTransformationsIsMutable() {
      if (!((bitField0_ & 0x00000008) != 0)) {
        transformations_ =
            new java.util.ArrayList<
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation>(transformations_);
        bitField0_ |= 0x00000008;
      }
    }

    private com.google.protobuf.RepeatedFieldBuilderV3<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.Builder,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.TransformationOrBuilder>
        transformationsBuilder_;

    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public java.util.List<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation>
        getTransformationsList() {
      if (transformationsBuilder_ == null) {
        return java.util.Collections.unmodifiableList(transformations_);
      } else {
        return transformationsBuilder_.getMessageList();
      }
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public int getTransformationsCount() {
      if (transformationsBuilder_ == null) {
        return transformations_.size();
      } else {
        return transformationsBuilder_.getCount();
      }
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation
        getTransformations(int index) {
      if (transformationsBuilder_ == null) {
        return transformations_.get(index);
      } else {
        return transformationsBuilder_.getMessage(index);
      }
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder setTransformations(
        int index,
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Transformation
            value) {
      if (transformationsBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureTransformationsIsMutable();
        transformations_.set(index, value);
        onChanged();
      } else {
        transformationsBuilder_.setMessage(index, value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder setTransformations(
        int index,
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Transformation.Builder
            builderForValue) {
      if (transformationsBuilder_ == null) {
        ensureTransformationsIsMutable();
        transformations_.set(index, builderForValue.build());
        onChanged();
      } else {
        transformationsBuilder_.setMessage(index, builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder addTransformations(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Transformation
            value) {
      if (transformationsBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureTransformationsIsMutable();
        transformations_.add(value);
        onChanged();
      } else {
        transformationsBuilder_.addMessage(value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder addTransformations(
        int index,
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Transformation
            value) {
      if (transformationsBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureTransformationsIsMutable();
        transformations_.add(index, value);
        onChanged();
      } else {
        transformationsBuilder_.addMessage(index, value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder addTransformations(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Transformation.Builder
            builderForValue) {
      if (transformationsBuilder_ == null) {
        ensureTransformationsIsMutable();
        transformations_.add(builderForValue.build());
        onChanged();
      } else {
        transformationsBuilder_.addMessage(builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder addTransformations(
        int index,
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Transformation.Builder
            builderForValue) {
      if (transformationsBuilder_ == null) {
        ensureTransformationsIsMutable();
        transformations_.add(index, builderForValue.build());
        onChanged();
      } else {
        transformationsBuilder_.addMessage(index, builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder addAllTransformations(
        java.lang.Iterable<
                ? extends
                    com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                        .AutoMlForecastingInputs.Transformation>
            values) {
      if (transformationsBuilder_ == null) {
        ensureTransformationsIsMutable();
        com.google.protobuf.AbstractMessageLite.Builder.addAll(values, transformations_);
        onChanged();
      } else {
        transformationsBuilder_.addAllMessages(values);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder clearTransformations() {
      if (transformationsBuilder_ == null) {
        transformations_ = java.util.Collections.emptyList();
        bitField0_ = (bitField0_ & ~0x00000008);
        onChanged();
      } else {
        transformationsBuilder_.clear();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public Builder removeTransformations(int index) {
      if (transformationsBuilder_ == null) {
        ensureTransformationsIsMutable();
        transformations_.remove(index);
        onChanged();
      } else {
        transformationsBuilder_.remove(index);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.Builder
        getTransformationsBuilder(int index) {
      return getTransformationsFieldBuilder().getBuilder(index);
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .TransformationOrBuilder
        getTransformationsOrBuilder(int index) {
      if (transformationsBuilder_ == null) {
        return transformations_.get(index);
      } else {
        return transformationsBuilder_.getMessageOrBuilder(index);
      }
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public java.util.List<
            ? extends
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.TransformationOrBuilder>
        getTransformationsOrBuilderList() {
      if (transformationsBuilder_ != null) {
        return transformationsBuilder_.getMessageOrBuilderList();
      } else {
        return java.util.Collections.unmodifiableList(transformations_);
      }
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.Builder
        addTransformationsBuilder() {
      return getTransformationsFieldBuilder()
          .addBuilder(
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.getDefaultInstance());
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Transformation.Builder
        addTransformationsBuilder(int index) {
      return getTransformationsFieldBuilder()
          .addBuilder(
              index,
              com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                  .AutoMlForecastingInputs.Transformation.getDefaultInstance());
    }
    /**
     *
     *
     * <pre>
     * Each transformation will apply transform function to given input column.
     * And the result will be used for training.
     * When creating transformation for BigQuery Struct column, the column should
     * be flattened using "." as the delimiter.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Transformation transformations = 4;
     * </code>
     */
    public java.util.List<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.Builder>
        getTransformationsBuilderList() {
      return getTransformationsFieldBuilder().getBuilderList();
    }

    private com.google.protobuf.RepeatedFieldBuilderV3<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Transformation.Builder,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.TransformationOrBuilder>
        getTransformationsFieldBuilder() {
      if (transformationsBuilder_ == null) {
        transformationsBuilder_ =
            new com.google.protobuf.RepeatedFieldBuilderV3<
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation,
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Transformation.Builder,
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.TransformationOrBuilder>(
                transformations_,
                ((bitField0_ & 0x00000008) != 0),
                getParentForChildren(),
                isClean());
        transformations_ = null;
      }
      return transformationsBuilder_;
    }

    private java.lang.Object optimizationObjective_ = "";
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that optimizes the value of the objective
     * function over the validation set.
     *
     * The supported optimization objectives:
     *
     *   * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
     *
     *   * "minimize-mae" - Minimize mean-absolute error (MAE).
     *
     *   * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
     *
     *   * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
     *
     *   * "minimize-wape-mae" - Minimize the combination of weighted absolute
     *     percentage error (WAPE) and mean-absolute-error (MAE).
     *
     *   * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
     *     defined in `quantiles`.
     * </pre>
     *
     * <code>string optimization_objective = 5;</code>
     *
     * @return The optimizationObjective.
     */
    public java.lang.String getOptimizationObjective() {
      java.lang.Object ref = optimizationObjective_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        optimizationObjective_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that optimizes the value of the objective
     * function over the validation set.
     *
     * The supported optimization objectives:
     *
     *   * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
     *
     *   * "minimize-mae" - Minimize mean-absolute error (MAE).
     *
     *   * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
     *
     *   * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
     *
     *   * "minimize-wape-mae" - Minimize the combination of weighted absolute
     *     percentage error (WAPE) and mean-absolute-error (MAE).
     *
     *   * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
     *     defined in `quantiles`.
     * </pre>
     *
     * <code>string optimization_objective = 5;</code>
     *
     * @return The bytes for optimizationObjective.
     */
    public com.google.protobuf.ByteString getOptimizationObjectiveBytes() {
      java.lang.Object ref = optimizationObjective_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        optimizationObjective_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that optimizes the value of the objective
     * function over the validation set.
     *
     * The supported optimization objectives:
     *
     *   * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
     *
     *   * "minimize-mae" - Minimize mean-absolute error (MAE).
     *
     *   * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
     *
     *   * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
     *
     *   * "minimize-wape-mae" - Minimize the combination of weighted absolute
     *     percentage error (WAPE) and mean-absolute-error (MAE).
     *
     *   * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
     *     defined in `quantiles`.
     * </pre>
     *
     * <code>string optimization_objective = 5;</code>
     *
     * @param value The optimizationObjective to set.
     * @return This builder for chaining.
     */
    public Builder setOptimizationObjective(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      optimizationObjective_ = value;
      bitField0_ |= 0x00000010;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that optimizes the value of the objective
     * function over the validation set.
     *
     * The supported optimization objectives:
     *
     *   * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
     *
     *   * "minimize-mae" - Minimize mean-absolute error (MAE).
     *
     *   * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
     *
     *   * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
     *
     *   * "minimize-wape-mae" - Minimize the combination of weighted absolute
     *     percentage error (WAPE) and mean-absolute-error (MAE).
     *
     *   * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
     *     defined in `quantiles`.
     * </pre>
     *
     * <code>string optimization_objective = 5;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearOptimizationObjective() {
      optimizationObjective_ = getDefaultInstance().getOptimizationObjective();
      bitField0_ = (bitField0_ & ~0x00000010);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that optimizes the value of the objective
     * function over the validation set.
     *
     * The supported optimization objectives:
     *
     *   * "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
     *
     *   * "minimize-mae" - Minimize mean-absolute error (MAE).
     *
     *   * "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
     *
     *   * "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
     *
     *   * "minimize-wape-mae" - Minimize the combination of weighted absolute
     *     percentage error (WAPE) and mean-absolute-error (MAE).
     *
     *   * "minimize-quantile-loss" - Minimize the quantile loss at the quantiles
     *     defined in `quantiles`.
     * </pre>
     *
     * <code>string optimization_objective = 5;</code>
     *
     * @param value The bytes for optimizationObjective to set.
     * @return This builder for chaining.
     */
    public Builder setOptimizationObjectiveBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      optimizationObjective_ = value;
      bitField0_ |= 0x00000010;
      onChanged();
      return this;
    }

    private long trainBudgetMilliNodeHours_;
    /**
     *
     *
     * <pre>
     * Required. The train budget of creating this model, expressed in milli node
     * hours i.e. 1,000 value in this field means 1 node hour.
     *
     * The training cost of the model will not exceed this budget. The final cost
     * will be attempted to be close to the budget, though may end up being (even)
     * noticeably smaller - at the backend's discretion. This especially may
     * happen when further model training ceases to provide any improvements.
     *
     * If the budget is set to a value known to be insufficient to train a
     * model for the given dataset, the training won't be attempted and
     * will error.
     *
     * The train budget must be between 1,000 and 72,000 milli node hours,
     * inclusive.
     * </pre>
     *
     * <code>int64 train_budget_milli_node_hours = 6;</code>
     *
     * @return The trainBudgetMilliNodeHours.
     */
    @java.lang.Override
    public long getTrainBudgetMilliNodeHours() {
      return trainBudgetMilliNodeHours_;
    }
    /**
     *
     *
     * <pre>
     * Required. The train budget of creating this model, expressed in milli node
     * hours i.e. 1,000 value in this field means 1 node hour.
     *
     * The training cost of the model will not exceed this budget. The final cost
     * will be attempted to be close to the budget, though may end up being (even)
     * noticeably smaller - at the backend's discretion. This especially may
     * happen when further model training ceases to provide any improvements.
     *
     * If the budget is set to a value known to be insufficient to train a
     * model for the given dataset, the training won't be attempted and
     * will error.
     *
     * The train budget must be between 1,000 and 72,000 milli node hours,
     * inclusive.
     * </pre>
     *
     * <code>int64 train_budget_milli_node_hours = 6;</code>
     *
     * @param value The trainBudgetMilliNodeHours to set.
     * @return This builder for chaining.
     */
    public Builder setTrainBudgetMilliNodeHours(long value) {

      trainBudgetMilliNodeHours_ = value;
      bitField0_ |= 0x00000020;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Required. The train budget of creating this model, expressed in milli node
     * hours i.e. 1,000 value in this field means 1 node hour.
     *
     * The training cost of the model will not exceed this budget. The final cost
     * will be attempted to be close to the budget, though may end up being (even)
     * noticeably smaller - at the backend's discretion. This especially may
     * happen when further model training ceases to provide any improvements.
     *
     * If the budget is set to a value known to be insufficient to train a
     * model for the given dataset, the training won't be attempted and
     * will error.
     *
     * The train budget must be between 1,000 and 72,000 milli node hours,
     * inclusive.
     * </pre>
     *
     * <code>int64 train_budget_milli_node_hours = 6;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearTrainBudgetMilliNodeHours() {
      bitField0_ = (bitField0_ & ~0x00000020);
      trainBudgetMilliNodeHours_ = 0L;
      onChanged();
      return this;
    }

    private java.lang.Object weightColumn_ = "";
    /**
     *
     *
     * <pre>
     * Column name that should be used as the weight column.
     * Higher values in this column give more importance to the row
     * during model training. The column must have numeric values between 0 and
     * 10000 inclusively; 0 means the row is ignored for training. If weight
     * column field is not set, then all rows are assumed to have equal weight
     * of 1.
     * </pre>
     *
     * <code>string weight_column = 7;</code>
     *
     * @return The weightColumn.
     */
    public java.lang.String getWeightColumn() {
      java.lang.Object ref = weightColumn_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        weightColumn_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Column name that should be used as the weight column.
     * Higher values in this column give more importance to the row
     * during model training. The column must have numeric values between 0 and
     * 10000 inclusively; 0 means the row is ignored for training. If weight
     * column field is not set, then all rows are assumed to have equal weight
     * of 1.
     * </pre>
     *
     * <code>string weight_column = 7;</code>
     *
     * @return The bytes for weightColumn.
     */
    public com.google.protobuf.ByteString getWeightColumnBytes() {
      java.lang.Object ref = weightColumn_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        weightColumn_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Column name that should be used as the weight column.
     * Higher values in this column give more importance to the row
     * during model training. The column must have numeric values between 0 and
     * 10000 inclusively; 0 means the row is ignored for training. If weight
     * column field is not set, then all rows are assumed to have equal weight
     * of 1.
     * </pre>
     *
     * <code>string weight_column = 7;</code>
     *
     * @param value The weightColumn to set.
     * @return This builder for chaining.
     */
    public Builder setWeightColumn(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      weightColumn_ = value;
      bitField0_ |= 0x00000040;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column name that should be used as the weight column.
     * Higher values in this column give more importance to the row
     * during model training. The column must have numeric values between 0 and
     * 10000 inclusively; 0 means the row is ignored for training. If weight
     * column field is not set, then all rows are assumed to have equal weight
     * of 1.
     * </pre>
     *
     * <code>string weight_column = 7;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearWeightColumn() {
      weightColumn_ = getDefaultInstance().getWeightColumn();
      bitField0_ = (bitField0_ & ~0x00000040);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column name that should be used as the weight column.
     * Higher values in this column give more importance to the row
     * during model training. The column must have numeric values between 0 and
     * 10000 inclusively; 0 means the row is ignored for training. If weight
     * column field is not set, then all rows are assumed to have equal weight
     * of 1.
     * </pre>
     *
     * <code>string weight_column = 7;</code>
     *
     * @param value The bytes for weightColumn to set.
     * @return This builder for chaining.
     */
    public Builder setWeightColumnBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      weightColumn_ = value;
      bitField0_ |= 0x00000040;
      onChanged();
      return this;
    }

    private com.google.protobuf.LazyStringArrayList timeSeriesAttributeColumns_ =
        com.google.protobuf.LazyStringArrayList.emptyList();

    private void ensureTimeSeriesAttributeColumnsIsMutable() {
      if (!timeSeriesAttributeColumns_.isModifiable()) {
        timeSeriesAttributeColumns_ =
            new com.google.protobuf.LazyStringArrayList(timeSeriesAttributeColumns_);
      }
      bitField0_ |= 0x00000080;
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @return A list containing the timeSeriesAttributeColumns.
     */
    public com.google.protobuf.ProtocolStringList getTimeSeriesAttributeColumnsList() {
      timeSeriesAttributeColumns_.makeImmutable();
      return timeSeriesAttributeColumns_;
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @return The count of timeSeriesAttributeColumns.
     */
    public int getTimeSeriesAttributeColumnsCount() {
      return timeSeriesAttributeColumns_.size();
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @param index The index of the element to return.
     * @return The timeSeriesAttributeColumns at the given index.
     */
    public java.lang.String getTimeSeriesAttributeColumns(int index) {
      return timeSeriesAttributeColumns_.get(index);
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @param index The index of the value to return.
     * @return The bytes of the timeSeriesAttributeColumns at the given index.
     */
    public com.google.protobuf.ByteString getTimeSeriesAttributeColumnsBytes(int index) {
      return timeSeriesAttributeColumns_.getByteString(index);
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @param index The index to set the value at.
     * @param value The timeSeriesAttributeColumns to set.
     * @return This builder for chaining.
     */
    public Builder setTimeSeriesAttributeColumns(int index, java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      ensureTimeSeriesAttributeColumnsIsMutable();
      timeSeriesAttributeColumns_.set(index, value);
      bitField0_ |= 0x00000080;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @param value The timeSeriesAttributeColumns to add.
     * @return This builder for chaining.
     */
    public Builder addTimeSeriesAttributeColumns(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      ensureTimeSeriesAttributeColumnsIsMutable();
      timeSeriesAttributeColumns_.add(value);
      bitField0_ |= 0x00000080;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @param values The timeSeriesAttributeColumns to add.
     * @return This builder for chaining.
     */
    public Builder addAllTimeSeriesAttributeColumns(java.lang.Iterable<java.lang.String> values) {
      ensureTimeSeriesAttributeColumnsIsMutable();
      com.google.protobuf.AbstractMessageLite.Builder.addAll(values, timeSeriesAttributeColumns_);
      bitField0_ |= 0x00000080;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearTimeSeriesAttributeColumns() {
      timeSeriesAttributeColumns_ = com.google.protobuf.LazyStringArrayList.emptyList();
      bitField0_ = (bitField0_ & ~0x00000080);
      ;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column names that should be used as attribute columns.
     * The value of these columns does not vary as a function of time.
     * For example, store ID or item color.
     * </pre>
     *
     * <code>repeated string time_series_attribute_columns = 19;</code>
     *
     * @param value The bytes of the timeSeriesAttributeColumns to add.
     * @return This builder for chaining.
     */
    public Builder addTimeSeriesAttributeColumnsBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      ensureTimeSeriesAttributeColumnsIsMutable();
      timeSeriesAttributeColumns_.add(value);
      bitField0_ |= 0x00000080;
      onChanged();
      return this;
    }

    private com.google.protobuf.LazyStringArrayList unavailableAtForecastColumns_ =
        com.google.protobuf.LazyStringArrayList.emptyList();

    private void ensureUnavailableAtForecastColumnsIsMutable() {
      if (!unavailableAtForecastColumns_.isModifiable()) {
        unavailableAtForecastColumns_ =
            new com.google.protobuf.LazyStringArrayList(unavailableAtForecastColumns_);
      }
      bitField0_ |= 0x00000100;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @return A list containing the unavailableAtForecastColumns.
     */
    public com.google.protobuf.ProtocolStringList getUnavailableAtForecastColumnsList() {
      unavailableAtForecastColumns_.makeImmutable();
      return unavailableAtForecastColumns_;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @return The count of unavailableAtForecastColumns.
     */
    public int getUnavailableAtForecastColumnsCount() {
      return unavailableAtForecastColumns_.size();
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @param index The index of the element to return.
     * @return The unavailableAtForecastColumns at the given index.
     */
    public java.lang.String getUnavailableAtForecastColumns(int index) {
      return unavailableAtForecastColumns_.get(index);
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @param index The index of the value to return.
     * @return The bytes of the unavailableAtForecastColumns at the given index.
     */
    public com.google.protobuf.ByteString getUnavailableAtForecastColumnsBytes(int index) {
      return unavailableAtForecastColumns_.getByteString(index);
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @param index The index to set the value at.
     * @param value The unavailableAtForecastColumns to set.
     * @return This builder for chaining.
     */
    public Builder setUnavailableAtForecastColumns(int index, java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      ensureUnavailableAtForecastColumnsIsMutable();
      unavailableAtForecastColumns_.set(index, value);
      bitField0_ |= 0x00000100;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @param value The unavailableAtForecastColumns to add.
     * @return This builder for chaining.
     */
    public Builder addUnavailableAtForecastColumns(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      ensureUnavailableAtForecastColumnsIsMutable();
      unavailableAtForecastColumns_.add(value);
      bitField0_ |= 0x00000100;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @param values The unavailableAtForecastColumns to add.
     * @return This builder for chaining.
     */
    public Builder addAllUnavailableAtForecastColumns(java.lang.Iterable<java.lang.String> values) {
      ensureUnavailableAtForecastColumnsIsMutable();
      com.google.protobuf.AbstractMessageLite.Builder.addAll(values, unavailableAtForecastColumns_);
      bitField0_ |= 0x00000100;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearUnavailableAtForecastColumns() {
      unavailableAtForecastColumns_ = com.google.protobuf.LazyStringArrayList.emptyList();
      bitField0_ = (bitField0_ & ~0x00000100);
      ;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are unavailable when a forecast is requested.
     * This column contains information for the given entity (identified
     * by the time_series_identifier_column) that is unknown before the forecast
     * For example, actual weather on a given day.
     * </pre>
     *
     * <code>repeated string unavailable_at_forecast_columns = 20;</code>
     *
     * @param value The bytes of the unavailableAtForecastColumns to add.
     * @return This builder for chaining.
     */
    public Builder addUnavailableAtForecastColumnsBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      ensureUnavailableAtForecastColumnsIsMutable();
      unavailableAtForecastColumns_.add(value);
      bitField0_ |= 0x00000100;
      onChanged();
      return this;
    }

    private com.google.protobuf.LazyStringArrayList availableAtForecastColumns_ =
        com.google.protobuf.LazyStringArrayList.emptyList();

    private void ensureAvailableAtForecastColumnsIsMutable() {
      if (!availableAtForecastColumns_.isModifiable()) {
        availableAtForecastColumns_ =
            new com.google.protobuf.LazyStringArrayList(availableAtForecastColumns_);
      }
      bitField0_ |= 0x00000200;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @return A list containing the availableAtForecastColumns.
     */
    public com.google.protobuf.ProtocolStringList getAvailableAtForecastColumnsList() {
      availableAtForecastColumns_.makeImmutable();
      return availableAtForecastColumns_;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @return The count of availableAtForecastColumns.
     */
    public int getAvailableAtForecastColumnsCount() {
      return availableAtForecastColumns_.size();
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @param index The index of the element to return.
     * @return The availableAtForecastColumns at the given index.
     */
    public java.lang.String getAvailableAtForecastColumns(int index) {
      return availableAtForecastColumns_.get(index);
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @param index The index of the value to return.
     * @return The bytes of the availableAtForecastColumns at the given index.
     */
    public com.google.protobuf.ByteString getAvailableAtForecastColumnsBytes(int index) {
      return availableAtForecastColumns_.getByteString(index);
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @param index The index to set the value at.
     * @param value The availableAtForecastColumns to set.
     * @return This builder for chaining.
     */
    public Builder setAvailableAtForecastColumns(int index, java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      ensureAvailableAtForecastColumnsIsMutable();
      availableAtForecastColumns_.set(index, value);
      bitField0_ |= 0x00000200;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @param value The availableAtForecastColumns to add.
     * @return This builder for chaining.
     */
    public Builder addAvailableAtForecastColumns(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      ensureAvailableAtForecastColumnsIsMutable();
      availableAtForecastColumns_.add(value);
      bitField0_ |= 0x00000200;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @param values The availableAtForecastColumns to add.
     * @return This builder for chaining.
     */
    public Builder addAllAvailableAtForecastColumns(java.lang.Iterable<java.lang.String> values) {
      ensureAvailableAtForecastColumnsIsMutable();
      com.google.protobuf.AbstractMessageLite.Builder.addAll(values, availableAtForecastColumns_);
      bitField0_ |= 0x00000200;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearAvailableAtForecastColumns() {
      availableAtForecastColumns_ = com.google.protobuf.LazyStringArrayList.emptyList();
      bitField0_ = (bitField0_ & ~0x00000200);
      ;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Names of columns that are available and provided when a forecast
     * is requested. These columns
     * contain information for the given entity (identified by the
     * time_series_identifier_column column) that is known at forecast.
     * For example, predicted weather for a specific day.
     * </pre>
     *
     * <code>repeated string available_at_forecast_columns = 21;</code>
     *
     * @param value The bytes of the availableAtForecastColumns to add.
     * @return This builder for chaining.
     */
    public Builder addAvailableAtForecastColumnsBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      ensureAvailableAtForecastColumnsIsMutable();
      availableAtForecastColumns_.add(value);
      bitField0_ |= 0x00000200;
      onChanged();
      return this;
    }

    private com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs.Granularity
        dataGranularity_;
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Granularity,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Granularity.Builder,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.GranularityOrBuilder>
        dataGranularityBuilder_;
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     *
     * @return Whether the dataGranularity field is set.
     */
    public boolean hasDataGranularity() {
      return ((bitField0_ & 0x00000400) != 0);
    }
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     *
     * @return The dataGranularity.
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Granularity
        getDataGranularity() {
      if (dataGranularityBuilder_ == null) {
        return dataGranularity_ == null
            ? com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Granularity.getDefaultInstance()
            : dataGranularity_;
      } else {
        return dataGranularityBuilder_.getMessage();
      }
    }
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     */
    public Builder setDataGranularity(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Granularity
            value) {
      if (dataGranularityBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        dataGranularity_ = value;
      } else {
        dataGranularityBuilder_.setMessage(value);
      }
      bitField0_ |= 0x00000400;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     */
    public Builder setDataGranularity(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Granularity.Builder
            builderForValue) {
      if (dataGranularityBuilder_ == null) {
        dataGranularity_ = builderForValue.build();
      } else {
        dataGranularityBuilder_.setMessage(builderForValue.build());
      }
      bitField0_ |= 0x00000400;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     */
    public Builder mergeDataGranularity(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
                .Granularity
            value) {
      if (dataGranularityBuilder_ == null) {
        if (((bitField0_ & 0x00000400) != 0)
            && dataGranularity_ != null
            && dataGranularity_
                != com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Granularity.getDefaultInstance()) {
          getDataGranularityBuilder().mergeFrom(value);
        } else {
          dataGranularity_ = value;
        }
      } else {
        dataGranularityBuilder_.mergeFrom(value);
      }
      bitField0_ |= 0x00000400;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     */
    public Builder clearDataGranularity() {
      bitField0_ = (bitField0_ & ~0x00000400);
      dataGranularity_ = null;
      if (dataGranularityBuilder_ != null) {
        dataGranularityBuilder_.dispose();
        dataGranularityBuilder_ = null;
      }
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .Granularity.Builder
        getDataGranularityBuilder() {
      bitField0_ |= 0x00000400;
      onChanged();
      return getDataGranularityFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
            .GranularityOrBuilder
        getDataGranularityOrBuilder() {
      if (dataGranularityBuilder_ != null) {
        return dataGranularityBuilder_.getMessageOrBuilder();
      } else {
        return dataGranularity_ == null
            ? com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Granularity.getDefaultInstance()
            : dataGranularity_;
      }
    }
    /**
     *
     *
     * <pre>
     * Expected difference in time granularity between rows in the data.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs.Granularity data_granularity = 22;
     * </code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Granularity,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.Granularity.Builder,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .AutoMlForecastingInputs.GranularityOrBuilder>
        getDataGranularityFieldBuilder() {
      if (dataGranularityBuilder_ == null) {
        dataGranularityBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Granularity,
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.Granularity.Builder,
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .AutoMlForecastingInputs.GranularityOrBuilder>(
                getDataGranularity(), getParentForChildren(), isClean());
        dataGranularity_ = null;
      }
      return dataGranularityBuilder_;
    }

    private long forecastHorizon_;
    /**
     *
     *
     * <pre>
     * The amount of time into the future for which forecasted values for the
     * target are returned. Expressed in number of units defined by the
     * `data_granularity` field.
     * </pre>
     *
     * <code>int64 forecast_horizon = 23;</code>
     *
     * @return The forecastHorizon.
     */
    @java.lang.Override
    public long getForecastHorizon() {
      return forecastHorizon_;
    }
    /**
     *
     *
     * <pre>
     * The amount of time into the future for which forecasted values for the
     * target are returned. Expressed in number of units defined by the
     * `data_granularity` field.
     * </pre>
     *
     * <code>int64 forecast_horizon = 23;</code>
     *
     * @param value The forecastHorizon to set.
     * @return This builder for chaining.
     */
    public Builder setForecastHorizon(long value) {

      forecastHorizon_ = value;
      bitField0_ |= 0x00000800;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * The amount of time into the future for which forecasted values for the
     * target are returned. Expressed in number of units defined by the
     * `data_granularity` field.
     * </pre>
     *
     * <code>int64 forecast_horizon = 23;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearForecastHorizon() {
      bitField0_ = (bitField0_ & ~0x00000800);
      forecastHorizon_ = 0L;
      onChanged();
      return this;
    }

    private long contextWindow_;
    /**
     *
     *
     * <pre>
     * The amount of time into the past training and prediction data is used
     * for model training and prediction respectively. Expressed in number of
     * units defined by the `data_granularity` field.
     * </pre>
     *
     * <code>int64 context_window = 24;</code>
     *
     * @return The contextWindow.
     */
    @java.lang.Override
    public long getContextWindow() {
      return contextWindow_;
    }
    /**
     *
     *
     * <pre>
     * The amount of time into the past training and prediction data is used
     * for model training and prediction respectively. Expressed in number of
     * units defined by the `data_granularity` field.
     * </pre>
     *
     * <code>int64 context_window = 24;</code>
     *
     * @param value The contextWindow to set.
     * @return This builder for chaining.
     */
    public Builder setContextWindow(long value) {

      contextWindow_ = value;
      bitField0_ |= 0x00001000;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * The amount of time into the past training and prediction data is used
     * for model training and prediction respectively. Expressed in number of
     * units defined by the `data_granularity` field.
     * </pre>
     *
     * <code>int64 context_window = 24;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearContextWindow() {
      bitField0_ = (bitField0_ & ~0x00001000);
      contextWindow_ = 0L;
      onChanged();
      return this;
    }

    private com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .ExportEvaluatedDataItemsConfig
        exportEvaluatedDataItemsConfig_;
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig.Builder,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfigOrBuilder>
        exportEvaluatedDataItemsConfigBuilder_;
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     *
     * @return Whether the exportEvaluatedDataItemsConfig field is set.
     */
    public boolean hasExportEvaluatedDataItemsConfig() {
      return ((bitField0_ & 0x00002000) != 0);
    }
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     *
     * @return The exportEvaluatedDataItemsConfig.
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .ExportEvaluatedDataItemsConfig
        getExportEvaluatedDataItemsConfig() {
      if (exportEvaluatedDataItemsConfigBuilder_ == null) {
        return exportEvaluatedDataItemsConfig_ == null
            ? com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig.getDefaultInstance()
            : exportEvaluatedDataItemsConfig_;
      } else {
        return exportEvaluatedDataItemsConfigBuilder_.getMessage();
      }
    }
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     */
    public Builder setExportEvaluatedDataItemsConfig(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig
            value) {
      if (exportEvaluatedDataItemsConfigBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        exportEvaluatedDataItemsConfig_ = value;
      } else {
        exportEvaluatedDataItemsConfigBuilder_.setMessage(value);
      }
      bitField0_ |= 0x00002000;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     */
    public Builder setExportEvaluatedDataItemsConfig(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig.Builder
            builderForValue) {
      if (exportEvaluatedDataItemsConfigBuilder_ == null) {
        exportEvaluatedDataItemsConfig_ = builderForValue.build();
      } else {
        exportEvaluatedDataItemsConfigBuilder_.setMessage(builderForValue.build());
      }
      bitField0_ |= 0x00002000;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     */
    public Builder mergeExportEvaluatedDataItemsConfig(
        com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig
            value) {
      if (exportEvaluatedDataItemsConfigBuilder_ == null) {
        if (((bitField0_ & 0x00002000) != 0)
            && exportEvaluatedDataItemsConfig_ != null
            && exportEvaluatedDataItemsConfig_
                != com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .ExportEvaluatedDataItemsConfig.getDefaultInstance()) {
          getExportEvaluatedDataItemsConfigBuilder().mergeFrom(value);
        } else {
          exportEvaluatedDataItemsConfig_ = value;
        }
      } else {
        exportEvaluatedDataItemsConfigBuilder_.mergeFrom(value);
      }
      bitField0_ |= 0x00002000;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     */
    public Builder clearExportEvaluatedDataItemsConfig() {
      bitField0_ = (bitField0_ & ~0x00002000);
      exportEvaluatedDataItemsConfig_ = null;
      if (exportEvaluatedDataItemsConfigBuilder_ != null) {
        exportEvaluatedDataItemsConfigBuilder_.dispose();
        exportEvaluatedDataItemsConfigBuilder_ = null;
      }
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .ExportEvaluatedDataItemsConfig.Builder
        getExportEvaluatedDataItemsConfigBuilder() {
      bitField0_ |= 0x00002000;
      onChanged();
      return getExportEvaluatedDataItemsConfigFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     */
    public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .ExportEvaluatedDataItemsConfigOrBuilder
        getExportEvaluatedDataItemsConfigOrBuilder() {
      if (exportEvaluatedDataItemsConfigBuilder_ != null) {
        return exportEvaluatedDataItemsConfigBuilder_.getMessageOrBuilder();
      } else {
        return exportEvaluatedDataItemsConfig_ == null
            ? com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig.getDefaultInstance()
            : exportEvaluatedDataItemsConfig_;
      }
    }
    /**
     *
     *
     * <pre>
     * Configuration for exporting test set predictions to a BigQuery table. If
     * this configuration is absent, then the export is not performed.
     * </pre>
     *
     * <code>
     * .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 15;
     * </code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfig.Builder,
            com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                .ExportEvaluatedDataItemsConfigOrBuilder>
        getExportEvaluatedDataItemsConfigFieldBuilder() {
      if (exportEvaluatedDataItemsConfigBuilder_ == null) {
        exportEvaluatedDataItemsConfigBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .ExportEvaluatedDataItemsConfig,
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .ExportEvaluatedDataItemsConfig.Builder,
                com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
                    .ExportEvaluatedDataItemsConfigOrBuilder>(
                getExportEvaluatedDataItemsConfig(), getParentForChildren(), isClean());
        exportEvaluatedDataItemsConfig_ = null;
      }
      return exportEvaluatedDataItemsConfigBuilder_;
    }

    private com.google.protobuf.Internal.DoubleList quantiles_ = emptyDoubleList();

    private void ensureQuantilesIsMutable() {
      if (!((bitField0_ & 0x00004000) != 0)) {
        quantiles_ = mutableCopy(quantiles_);
        bitField0_ |= 0x00004000;
      }
    }
    /**
     *
     *
     * <pre>
     * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
     * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
     * the value of optimization_objective is minimize-quantile-loss. Represents
     * the percent quantiles to use for that objective. Quantiles must be unique.
     * </pre>
     *
     * <code>repeated double quantiles = 16;</code>
     *
     * @return A list containing the quantiles.
     */
    public java.util.List<java.lang.Double> getQuantilesList() {
      return ((bitField0_ & 0x00004000) != 0)
          ? java.util.Collections.unmodifiableList(quantiles_)
          : quantiles_;
    }
    /**
     *
     *
     * <pre>
     * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
     * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
     * the value of optimization_objective is minimize-quantile-loss. Represents
     * the percent quantiles to use for that objective. Quantiles must be unique.
     * </pre>
     *
     * <code>repeated double quantiles = 16;</code>
     *
     * @return The count of quantiles.
     */
    public int getQuantilesCount() {
      return quantiles_.size();
    }
    /**
     *
     *
     * <pre>
     * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
     * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
     * the value of optimization_objective is minimize-quantile-loss. Represents
     * the percent quantiles to use for that objective. Quantiles must be unique.
     * </pre>
     *
     * <code>repeated double quantiles = 16;</code>
     *
     * @param index The index of the element to return.
     * @return The quantiles at the given index.
     */
    public double getQuantiles(int index) {
      return quantiles_.getDouble(index);
    }
    /**
     *
     *
     * <pre>
     * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
     * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
     * the value of optimization_objective is minimize-quantile-loss. Represents
     * the percent quantiles to use for that objective. Quantiles must be unique.
     * </pre>
     *
     * <code>repeated double quantiles = 16;</code>
     *
     * @param index The index to set the value at.
     * @param value The quantiles to set.
     * @return This builder for chaining.
     */
    public Builder setQuantiles(int index, double value) {

      ensureQuantilesIsMutable();
      quantiles_.setDouble(index, value);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
     * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
     * the value of optimization_objective is minimize-quantile-loss. Represents
     * the percent quantiles to use for that objective. Quantiles must be unique.
     * </pre>
     *
     * <code>repeated double quantiles = 16;</code>
     *
     * @param value The quantiles to add.
     * @return This builder for chaining.
     */
    public Builder addQuantiles(double value) {

      ensureQuantilesIsMutable();
      quantiles_.addDouble(value);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
     * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
     * the value of optimization_objective is minimize-quantile-loss. Represents
     * the percent quantiles to use for that objective. Quantiles must be unique.
     * </pre>
     *
     * <code>repeated double quantiles = 16;</code>
     *
     * @param values The quantiles to add.
     * @return This builder for chaining.
     */
    public Builder addAllQuantiles(java.lang.Iterable<? extends java.lang.Double> values) {
      ensureQuantilesIsMutable();
      com.google.protobuf.AbstractMessageLite.Builder.addAll(values, quantiles_);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Quantiles to use for minimize-quantile-loss `optimization_objective`. Up to
     * 5 quantiles are allowed of values between 0 and 1, exclusive. Required if
     * the value of optimization_objective is minimize-quantile-loss. Represents
     * the percent quantiles to use for that objective. Quantiles must be unique.
     * </pre>
     *
     * <code>repeated double quantiles = 16;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearQuantiles() {
      quantiles_ = emptyDoubleList();
      bitField0_ = (bitField0_ & ~0x00004000);
      onChanged();
      return this;
    }

    private java.lang.Object validationOptions_ = "";
    /**
     *
     *
     * <pre>
     * Validation options for the data validation component. The available options
     * are:
     *
     *   * "fail-pipeline" - default, will validate against the validation and
     *      fail the pipeline if it fails.
     *
     *   * "ignore-validation" - ignore the results of the validation and continue
     * </pre>
     *
     * <code>string validation_options = 17;</code>
     *
     * @return The validationOptions.
     */
    public java.lang.String getValidationOptions() {
      java.lang.Object ref = validationOptions_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        validationOptions_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Validation options for the data validation component. The available options
     * are:
     *
     *   * "fail-pipeline" - default, will validate against the validation and
     *      fail the pipeline if it fails.
     *
     *   * "ignore-validation" - ignore the results of the validation and continue
     * </pre>
     *
     * <code>string validation_options = 17;</code>
     *
     * @return The bytes for validationOptions.
     */
    public com.google.protobuf.ByteString getValidationOptionsBytes() {
      java.lang.Object ref = validationOptions_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        validationOptions_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Validation options for the data validation component. The available options
     * are:
     *
     *   * "fail-pipeline" - default, will validate against the validation and
     *      fail the pipeline if it fails.
     *
     *   * "ignore-validation" - ignore the results of the validation and continue
     * </pre>
     *
     * <code>string validation_options = 17;</code>
     *
     * @param value The validationOptions to set.
     * @return This builder for chaining.
     */
    public Builder setValidationOptions(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      validationOptions_ = value;
      bitField0_ |= 0x00008000;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Validation options for the data validation component. The available options
     * are:
     *
     *   * "fail-pipeline" - default, will validate against the validation and
     *      fail the pipeline if it fails.
     *
     *   * "ignore-validation" - ignore the results of the validation and continue
     * </pre>
     *
     * <code>string validation_options = 17;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearValidationOptions() {
      validationOptions_ = getDefaultInstance().getValidationOptions();
      bitField0_ = (bitField0_ & ~0x00008000);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Validation options for the data validation component. The available options
     * are:
     *
     *   * "fail-pipeline" - default, will validate against the validation and
     *      fail the pipeline if it fails.
     *
     *   * "ignore-validation" - ignore the results of the validation and continue
     * </pre>
     *
     * <code>string validation_options = 17;</code>
     *
     * @param value The bytes for validationOptions to set.
     * @return This builder for chaining.
     */
    public Builder setValidationOptionsBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      validationOptions_ = value;
      bitField0_ |= 0x00008000;
      onChanged();
      return this;
    }

    private com.google.protobuf.LazyStringArrayList additionalExperiments_ =
        com.google.protobuf.LazyStringArrayList.emptyList();

    private void ensureAdditionalExperimentsIsMutable() {
      if (!additionalExperiments_.isModifiable()) {
        additionalExperiments_ =
            new com.google.protobuf.LazyStringArrayList(additionalExperiments_);
      }
      bitField0_ |= 0x00010000;
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @return A list containing the additionalExperiments.
     */
    public com.google.protobuf.ProtocolStringList getAdditionalExperimentsList() {
      additionalExperiments_.makeImmutable();
      return additionalExperiments_;
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @return The count of additionalExperiments.
     */
    public int getAdditionalExperimentsCount() {
      return additionalExperiments_.size();
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @param index The index of the element to return.
     * @return The additionalExperiments at the given index.
     */
    public java.lang.String getAdditionalExperiments(int index) {
      return additionalExperiments_.get(index);
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @param index The index of the value to return.
     * @return The bytes of the additionalExperiments at the given index.
     */
    public com.google.protobuf.ByteString getAdditionalExperimentsBytes(int index) {
      return additionalExperiments_.getByteString(index);
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @param index The index to set the value at.
     * @param value The additionalExperiments to set.
     * @return This builder for chaining.
     */
    public Builder setAdditionalExperiments(int index, java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      ensureAdditionalExperimentsIsMutable();
      additionalExperiments_.set(index, value);
      bitField0_ |= 0x00010000;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @param value The additionalExperiments to add.
     * @return This builder for chaining.
     */
    public Builder addAdditionalExperiments(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      ensureAdditionalExperimentsIsMutable();
      additionalExperiments_.add(value);
      bitField0_ |= 0x00010000;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @param values The additionalExperiments to add.
     * @return This builder for chaining.
     */
    public Builder addAllAdditionalExperiments(java.lang.Iterable<java.lang.String> values) {
      ensureAdditionalExperimentsIsMutable();
      com.google.protobuf.AbstractMessageLite.Builder.addAll(values, additionalExperiments_);
      bitField0_ |= 0x00010000;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearAdditionalExperiments() {
      additionalExperiments_ = com.google.protobuf.LazyStringArrayList.emptyList();
      bitField0_ = (bitField0_ & ~0x00010000);
      ;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Additional experiment flags for the time series forcasting training.
     * </pre>
     *
     * <code>repeated string additional_experiments = 25;</code>
     *
     * @param value The bytes of the additionalExperiments to add.
     * @return This builder for chaining.
     */
    public Builder addAdditionalExperimentsBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      ensureAdditionalExperimentsIsMutable();
      additionalExperiments_.add(value);
      bitField0_ |= 0x00010000;
      onChanged();
      return this;
    }

    @java.lang.Override
    public final Builder setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields) {
      return super.setUnknownFields(unknownFields);
    }

    @java.lang.Override
    public final Builder mergeUnknownFields(
        final com.google.protobuf.UnknownFieldSet unknownFields) {
      return super.mergeUnknownFields(unknownFields);
    }

    // @@protoc_insertion_point(builder_scope:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs)
  }

  // @@protoc_insertion_point(class_scope:google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs)
  private static final com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .AutoMlForecastingInputs
      DEFAULT_INSTANCE;

  static {
    DEFAULT_INSTANCE =
        new com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
            .AutoMlForecastingInputs();
  }

  public static com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition
          .AutoMlForecastingInputs
      getDefaultInstance() {
    return DEFAULT_INSTANCE;
  }

  private static final com.google.protobuf.Parser<AutoMlForecastingInputs> PARSER =
      new com.google.protobuf.AbstractParser<AutoMlForecastingInputs>() {
        @java.lang.Override
        public AutoMlForecastingInputs parsePartialFrom(
            com.google.protobuf.CodedInputStream input,
            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws com.google.protobuf.InvalidProtocolBufferException {
          Builder builder = newBuilder();
          try {
            builder.mergeFrom(input, extensionRegistry);
          } catch (com.google.protobuf.InvalidProtocolBufferException e) {
            throw e.setUnfinishedMessage(builder.buildPartial());
          } catch (com.google.protobuf.UninitializedMessageException e) {
            throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
          } catch (java.io.IOException e) {
            throw new com.google.protobuf.InvalidProtocolBufferException(e)
                .setUnfinishedMessage(builder.buildPartial());
          }
          return builder.buildPartial();
        }
      };

  public static com.google.protobuf.Parser<AutoMlForecastingInputs> parser() {
    return PARSER;
  }

  @java.lang.Override
  public com.google.protobuf.Parser<AutoMlForecastingInputs> getParserForType() {
    return PARSER;
  }

  @java.lang.Override
  public com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlForecastingInputs
      getDefaultInstanceForType() {
    return DEFAULT_INSTANCE;
  }
}
