// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: google/cloud/automl/v1beta1/tables.proto

package com.google.cloud.automl.v1beta1;

/**
 *
 *
 * <pre>
 * Metadata for a dataset used for AutoML Tables.
 * </pre>
 *
 * Protobuf type {@code google.cloud.automl.v1beta1.TablesDatasetMetadata}
 */
public final class TablesDatasetMetadata extends com.google.protobuf.GeneratedMessageV3
    implements
    // @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.TablesDatasetMetadata)
    TablesDatasetMetadataOrBuilder {
  private static final long serialVersionUID = 0L;
  // Use TablesDatasetMetadata.newBuilder() to construct.
  private TablesDatasetMetadata(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) {
    super(builder);
  }

  private TablesDatasetMetadata() {
    primaryTableSpecId_ = "";
    targetColumnSpecId_ = "";
    weightColumnSpecId_ = "";
    mlUseColumnSpecId_ = "";
  }

  @java.lang.Override
  public final com.google.protobuf.UnknownFieldSet getUnknownFields() {
    return this.unknownFields;
  }

  private TablesDatasetMetadata(
      com.google.protobuf.CodedInputStream input,
      com.google.protobuf.ExtensionRegistryLite extensionRegistry)
      throws com.google.protobuf.InvalidProtocolBufferException {
    this();
    if (extensionRegistry == null) {
      throw new java.lang.NullPointerException();
    }
    int mutable_bitField0_ = 0;
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        switch (tag) {
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            done = true;
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          case 10:
            {
              java.lang.String s = input.readStringRequireUtf8();

              primaryTableSpecId_ = s;
              break;
            }
          case 18:
            {
              java.lang.String s = input.readStringRequireUtf8();

              targetColumnSpecId_ = s;
              break;
            }
          case 26:
            {
              java.lang.String s = input.readStringRequireUtf8();

              weightColumnSpecId_ = s;
              break;
            }
          case 34:
            {
              java.lang.String s = input.readStringRequireUtf8();

              mlUseColumnSpecId_ = s;
              break;
            }
          case 50:
            {
              if (!((mutable_bitField0_ & 0x00000010) != 0)) {
                targetColumnCorrelations_ =
                    com.google.protobuf.MapField.newMapField(
                        TargetColumnCorrelationsDefaultEntryHolder.defaultEntry);
                mutable_bitField0_ |= 0x00000010;
              }
              com.google.protobuf.MapEntry<
                      java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
                  targetColumnCorrelations__ =
                      input.readMessage(
                          TargetColumnCorrelationsDefaultEntryHolder.defaultEntry
                              .getParserForType(),
                          extensionRegistry);
              targetColumnCorrelations_
                  .getMutableMap()
                  .put(targetColumnCorrelations__.getKey(), targetColumnCorrelations__.getValue());
              break;
            }
          case 58:
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              com.google.protobuf.Timestamp.Builder subBuilder = null;
              if (statsUpdateTime_ != null) {
                subBuilder = statsUpdateTime_.toBuilder();
              }
              statsUpdateTime_ =
                  input.readMessage(com.google.protobuf.Timestamp.parser(), extensionRegistry);
              if (subBuilder != null) {
                subBuilder.mergeFrom(statsUpdateTime_);
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      makeExtensionsImmutable();
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  public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
    return com.google.cloud.automl.v1beta1.Tables
        .internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_descriptor;
  }

  @SuppressWarnings({"rawtypes"})
  @java.lang.Override
  protected com.google.protobuf.MapField internalGetMapField(int number) {
    switch (number) {
      case 6:
        return internalGetTargetColumnCorrelations();
      default:
        throw new RuntimeException("Invalid map field number: " + number);
    }
  }

  @java.lang.Override
  protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
      internalGetFieldAccessorTable() {
    return com.google.cloud.automl.v1beta1.Tables
        .internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_fieldAccessorTable
        .ensureFieldAccessorsInitialized(
            com.google.cloud.automl.v1beta1.TablesDatasetMetadata.class,
            com.google.cloud.automl.v1beta1.TablesDatasetMetadata.Builder.class);
  }

  private int bitField0_;
  public static final int PRIMARY_TABLE_SPEC_ID_FIELD_NUMBER = 1;
  private volatile java.lang.Object primaryTableSpecId_;
  /**
   *
   *
   * <pre>
   * Output only. The table_spec_id of the primary table of this dataset.
   * </pre>
   *
   * <code>string primary_table_spec_id = 1;</code>
   */
  public java.lang.String getPrimaryTableSpecId() {
    java.lang.Object ref = primaryTableSpecId_;
    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();
      primaryTableSpecId_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * Output only. The table_spec_id of the primary table of this dataset.
   * </pre>
   *
   * <code>string primary_table_spec_id = 1;</code>
   */
  public com.google.protobuf.ByteString getPrimaryTableSpecIdBytes() {
    java.lang.Object ref = primaryTableSpecId_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      primaryTableSpecId_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int TARGET_COLUMN_SPEC_ID_FIELD_NUMBER = 2;
  private volatile java.lang.Object targetColumnSpecId_;
  /**
   *
   *
   * <pre>
   * column_spec_id of the primary table's column that should be used as the
   * training &amp; prediction target.
   * This column must be non-nullable and have one of following data types
   * (otherwise model creation will error):
   * * CATEGORY
   * * ARRAY(CATEGORY)
   * * FLOAT64
   * Furthermore, if the type is CATEGORY or ARRAY(CATEGORY), then only up to
   * 40 unique values may exist in that column across all rows, but for
   * ARRAY(CATEGORY) unique values are counted as elements of the ARRAY (i.e.
   * following 3 ARRAY-s: [A, B], [A], [B] are counted as having 2 unique
   * values).
   * NOTE: Updates of this field will instantly affect any other users
   * concurrently working with the dataset.
   * </pre>
   *
   * <code>string target_column_spec_id = 2;</code>
   */
  public java.lang.String getTargetColumnSpecId() {
    java.lang.Object ref = targetColumnSpecId_;
    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();
      targetColumnSpecId_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * column_spec_id of the primary table's column that should be used as the
   * training &amp; prediction target.
   * This column must be non-nullable and have one of following data types
   * (otherwise model creation will error):
   * * CATEGORY
   * * ARRAY(CATEGORY)
   * * FLOAT64
   * Furthermore, if the type is CATEGORY or ARRAY(CATEGORY), then only up to
   * 40 unique values may exist in that column across all rows, but for
   * ARRAY(CATEGORY) unique values are counted as elements of the ARRAY (i.e.
   * following 3 ARRAY-s: [A, B], [A], [B] are counted as having 2 unique
   * values).
   * NOTE: Updates of this field will instantly affect any other users
   * concurrently working with the dataset.
   * </pre>
   *
   * <code>string target_column_spec_id = 2;</code>
   */
  public com.google.protobuf.ByteString getTargetColumnSpecIdBytes() {
    java.lang.Object ref = targetColumnSpecId_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      targetColumnSpecId_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int WEIGHT_COLUMN_SPEC_ID_FIELD_NUMBER = 3;
  private volatile java.lang.Object weightColumnSpecId_;
  /**
   *
   *
   * <pre>
   * column_spec_id of the primary table's column that should be used as the
   * weight column, i.e. the higher the value the more important the row will be
   * during model training.
   * Required type: FLOAT64.
   * Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
   *                 ignored for training.
   * If not set all rows are assumed to have equal weight of 1.
   * NOTE: Updates of this field will instantly affect any other users
   * concurrently working with the dataset.
   * </pre>
   *
   * <code>string weight_column_spec_id = 3;</code>
   */
  public java.lang.String getWeightColumnSpecId() {
    java.lang.Object ref = weightColumnSpecId_;
    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();
      weightColumnSpecId_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * column_spec_id of the primary table's column that should be used as the
   * weight column, i.e. the higher the value the more important the row will be
   * during model training.
   * Required type: FLOAT64.
   * Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
   *                 ignored for training.
   * If not set all rows are assumed to have equal weight of 1.
   * NOTE: Updates of this field will instantly affect any other users
   * concurrently working with the dataset.
   * </pre>
   *
   * <code>string weight_column_spec_id = 3;</code>
   */
  public com.google.protobuf.ByteString getWeightColumnSpecIdBytes() {
    java.lang.Object ref = weightColumnSpecId_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      weightColumnSpecId_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int ML_USE_COLUMN_SPEC_ID_FIELD_NUMBER = 4;
  private volatile java.lang.Object mlUseColumnSpecId_;
  /**
   *
   *
   * <pre>
   * column_spec_id of the primary table column which specifies a possible ML
   * use of the row, i.e. the column will be used to split the rows into TRAIN,
   * VALIDATE and TEST sets.
   * Required type: STRING.
   * This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
   * among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
   * case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
   * that if a given ml use distribution makes it impossible to create a "good"
   * model, that call will error describing the issue.
   * If both this column_spec_id and primary table's time_column_spec_id are not
   * set, then all rows are treated as `UNASSIGNED`.
   * NOTE: Updates of this field will instantly affect any other users
   * concurrently working with the dataset.
   * </pre>
   *
   * <code>string ml_use_column_spec_id = 4;</code>
   */
  public java.lang.String getMlUseColumnSpecId() {
    java.lang.Object ref = mlUseColumnSpecId_;
    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();
      mlUseColumnSpecId_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * column_spec_id of the primary table column which specifies a possible ML
   * use of the row, i.e. the column will be used to split the rows into TRAIN,
   * VALIDATE and TEST sets.
   * Required type: STRING.
   * This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
   * among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
   * case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
   * that if a given ml use distribution makes it impossible to create a "good"
   * model, that call will error describing the issue.
   * If both this column_spec_id and primary table's time_column_spec_id are not
   * set, then all rows are treated as `UNASSIGNED`.
   * NOTE: Updates of this field will instantly affect any other users
   * concurrently working with the dataset.
   * </pre>
   *
   * <code>string ml_use_column_spec_id = 4;</code>
   */
  public com.google.protobuf.ByteString getMlUseColumnSpecIdBytes() {
    java.lang.Object ref = mlUseColumnSpecId_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      mlUseColumnSpecId_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int TARGET_COLUMN_CORRELATIONS_FIELD_NUMBER = 6;

  private static final class TargetColumnCorrelationsDefaultEntryHolder {
    static final com.google.protobuf.MapEntry<
            java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
        defaultEntry =
            com.google.protobuf.MapEntry
                .<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
                    newDefaultInstance(
                        com.google.cloud.automl.v1beta1.Tables
                            .internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_TargetColumnCorrelationsEntry_descriptor,
                        com.google.protobuf.WireFormat.FieldType.STRING,
                        "",
                        com.google.protobuf.WireFormat.FieldType.MESSAGE,
                        com.google.cloud.automl.v1beta1.CorrelationStats.getDefaultInstance());
  }

  private com.google.protobuf.MapField<
          java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
      targetColumnCorrelations_;

  private com.google.protobuf.MapField<
          java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
      internalGetTargetColumnCorrelations() {
    if (targetColumnCorrelations_ == null) {
      return com.google.protobuf.MapField.emptyMapField(
          TargetColumnCorrelationsDefaultEntryHolder.defaultEntry);
    }
    return targetColumnCorrelations_;
  }

  public int getTargetColumnCorrelationsCount() {
    return internalGetTargetColumnCorrelations().getMap().size();
  }
  /**
   *
   *
   * <pre>
   * Output only. Correlations between
   * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
   * and other columns of the
   * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
   * Only set if the target column is set. Mapping from other column spec id to
   * its CorrelationStats with the target column.
   * This field may be stale, see the stats_update_time field for
   * for the timestamp at which these stats were last updated.
   * </pre>
   *
   * <code>
   * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
   * </code>
   */
  public boolean containsTargetColumnCorrelations(java.lang.String key) {
    if (key == null) {
      throw new java.lang.NullPointerException();
    }
    return internalGetTargetColumnCorrelations().getMap().containsKey(key);
  }
  /** Use {@link #getTargetColumnCorrelationsMap()} instead. */
  @java.lang.Deprecated
  public java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
      getTargetColumnCorrelations() {
    return getTargetColumnCorrelationsMap();
  }
  /**
   *
   *
   * <pre>
   * Output only. Correlations between
   * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
   * and other columns of the
   * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
   * Only set if the target column is set. Mapping from other column spec id to
   * its CorrelationStats with the target column.
   * This field may be stale, see the stats_update_time field for
   * for the timestamp at which these stats were last updated.
   * </pre>
   *
   * <code>
   * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
   * </code>
   */
  public java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
      getTargetColumnCorrelationsMap() {
    return internalGetTargetColumnCorrelations().getMap();
  }
  /**
   *
   *
   * <pre>
   * Output only. Correlations between
   * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
   * and other columns of the
   * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
   * Only set if the target column is set. Mapping from other column spec id to
   * its CorrelationStats with the target column.
   * This field may be stale, see the stats_update_time field for
   * for the timestamp at which these stats were last updated.
   * </pre>
   *
   * <code>
   * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
   * </code>
   */
  public com.google.cloud.automl.v1beta1.CorrelationStats getTargetColumnCorrelationsOrDefault(
      java.lang.String key, com.google.cloud.automl.v1beta1.CorrelationStats defaultValue) {
    if (key == null) {
      throw new java.lang.NullPointerException();
    }
    java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats> map =
        internalGetTargetColumnCorrelations().getMap();
    return map.containsKey(key) ? map.get(key) : defaultValue;
  }
  /**
   *
   *
   * <pre>
   * Output only. Correlations between
   * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
   * and other columns of the
   * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
   * Only set if the target column is set. Mapping from other column spec id to
   * its CorrelationStats with the target column.
   * This field may be stale, see the stats_update_time field for
   * for the timestamp at which these stats were last updated.
   * </pre>
   *
   * <code>
   * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
   * </code>
   */
  public com.google.cloud.automl.v1beta1.CorrelationStats getTargetColumnCorrelationsOrThrow(
      java.lang.String key) {
    if (key == null) {
      throw new java.lang.NullPointerException();
    }
    java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats> map =
        internalGetTargetColumnCorrelations().getMap();
    if (!map.containsKey(key)) {
      throw new java.lang.IllegalArgumentException();
    }
    return map.get(key);
  }

  public static final int STATS_UPDATE_TIME_FIELD_NUMBER = 7;
  private com.google.protobuf.Timestamp statsUpdateTime_;
  /**
   *
   *
   * <pre>
   * The most recent timestamp when target_column_correlations field and all
   * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
   * fields were last (re-)generated. Any changes that happened to the dataset
   * afterwards are not reflected in these fields values. The regeneration
   * happens in the background on a best effort basis.
   * </pre>
   *
   * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
   */
  public boolean hasStatsUpdateTime() {
    return statsUpdateTime_ != null;
  }
  /**
   *
   *
   * <pre>
   * The most recent timestamp when target_column_correlations field and all
   * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
   * fields were last (re-)generated. Any changes that happened to the dataset
   * afterwards are not reflected in these fields values. The regeneration
   * happens in the background on a best effort basis.
   * </pre>
   *
   * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
   */
  public com.google.protobuf.Timestamp getStatsUpdateTime() {
    return statsUpdateTime_ == null
        ? com.google.protobuf.Timestamp.getDefaultInstance()
        : statsUpdateTime_;
  }
  /**
   *
   *
   * <pre>
   * The most recent timestamp when target_column_correlations field and all
   * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
   * fields were last (re-)generated. Any changes that happened to the dataset
   * afterwards are not reflected in these fields values. The regeneration
   * happens in the background on a best effort basis.
   * </pre>
   *
   * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
   */
  public com.google.protobuf.TimestampOrBuilder getStatsUpdateTimeOrBuilder() {
    return getStatsUpdateTime();
  }

  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 {
    if (!getPrimaryTableSpecIdBytes().isEmpty()) {
      com.google.protobuf.GeneratedMessageV3.writeString(output, 1, primaryTableSpecId_);
    }
    if (!getTargetColumnSpecIdBytes().isEmpty()) {
      com.google.protobuf.GeneratedMessageV3.writeString(output, 2, targetColumnSpecId_);
    }
    if (!getWeightColumnSpecIdBytes().isEmpty()) {
      com.google.protobuf.GeneratedMessageV3.writeString(output, 3, weightColumnSpecId_);
    }
    if (!getMlUseColumnSpecIdBytes().isEmpty()) {
      com.google.protobuf.GeneratedMessageV3.writeString(output, 4, mlUseColumnSpecId_);
    }
    com.google.protobuf.GeneratedMessageV3.serializeStringMapTo(
        output,
        internalGetTargetColumnCorrelations(),
        TargetColumnCorrelationsDefaultEntryHolder.defaultEntry,
        6);
    if (statsUpdateTime_ != null) {
      output.writeMessage(7, getStatsUpdateTime());
    }
    unknownFields.writeTo(output);
  }

  @java.lang.Override
  public int getSerializedSize() {
    int size = memoizedSize;
    if (size != -1) return size;

    size = 0;
    if (!getPrimaryTableSpecIdBytes().isEmpty()) {
      size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, primaryTableSpecId_);
    }
    if (!getTargetColumnSpecIdBytes().isEmpty()) {
      size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, targetColumnSpecId_);
    }
    if (!getWeightColumnSpecIdBytes().isEmpty()) {
      size += com.google.protobuf.GeneratedMessageV3.computeStringSize(3, weightColumnSpecId_);
    }
    if (!getMlUseColumnSpecIdBytes().isEmpty()) {
      size += com.google.protobuf.GeneratedMessageV3.computeStringSize(4, mlUseColumnSpecId_);
    }
    for (java.util.Map.Entry<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
        entry : internalGetTargetColumnCorrelations().getMap().entrySet()) {
      com.google.protobuf.MapEntry<
              java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
          targetColumnCorrelations__ =
              TargetColumnCorrelationsDefaultEntryHolder.defaultEntry
                  .newBuilderForType()
                  .setKey(entry.getKey())
                  .setValue(entry.getValue())
                  .build();
      size +=
          com.google.protobuf.CodedOutputStream.computeMessageSize(6, targetColumnCorrelations__);
    }
    if (statsUpdateTime_ != null) {
      size += com.google.protobuf.CodedOutputStream.computeMessageSize(7, getStatsUpdateTime());
    }
    size += unknownFields.getSerializedSize();
    memoizedSize = size;
    return size;
  }

  @java.lang.Override
  public boolean equals(final java.lang.Object obj) {
    if (obj == this) {
      return true;
    }
    if (!(obj instanceof com.google.cloud.automl.v1beta1.TablesDatasetMetadata)) {
      return super.equals(obj);
    }
    com.google.cloud.automl.v1beta1.TablesDatasetMetadata other =
        (com.google.cloud.automl.v1beta1.TablesDatasetMetadata) obj;

    if (!getPrimaryTableSpecId().equals(other.getPrimaryTableSpecId())) return false;
    if (!getTargetColumnSpecId().equals(other.getTargetColumnSpecId())) return false;
    if (!getWeightColumnSpecId().equals(other.getWeightColumnSpecId())) return false;
    if (!getMlUseColumnSpecId().equals(other.getMlUseColumnSpecId())) return false;
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    hash = (37 * hash) + PRIMARY_TABLE_SPEC_ID_FIELD_NUMBER;
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  public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata parseFrom(
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  public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata parseFrom(
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  public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata parseFrom(
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  public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata parseFrom(
      com.google.protobuf.CodedInputStream input,
      com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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    return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
        PARSER, input, extensionRegistry);
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  @java.lang.Override
  public Builder newBuilderForType() {
    return newBuilder();
  }

  public static Builder newBuilder() {
    return DEFAULT_INSTANCE.toBuilder();
  }

  public static Builder newBuilder(
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    return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
  }

  @java.lang.Override
  public Builder toBuilder() {
    return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
  }

  @java.lang.Override
  protected Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
    Builder builder = new Builder(parent);
    return builder;
  }
  /**
   *
   *
   * <pre>
   * Metadata for a dataset used for AutoML Tables.
   * </pre>
   *
   * Protobuf type {@code google.cloud.automl.v1beta1.TablesDatasetMetadata}
   */
  public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder<Builder>
      implements
      // @@protoc_insertion_point(builder_implements:google.cloud.automl.v1beta1.TablesDatasetMetadata)
      com.google.cloud.automl.v1beta1.TablesDatasetMetadataOrBuilder {
    public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
      return com.google.cloud.automl.v1beta1.Tables
          .internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_descriptor;
    }

    @SuppressWarnings({"rawtypes"})
    protected com.google.protobuf.MapField internalGetMapField(int number) {
      switch (number) {
        case 6:
          return internalGetTargetColumnCorrelations();
        default:
          throw new RuntimeException("Invalid map field number: " + number);
      }
    }

    @SuppressWarnings({"rawtypes"})
    protected com.google.protobuf.MapField internalGetMutableMapField(int number) {
      switch (number) {
        case 6:
          return internalGetMutableTargetColumnCorrelations();
        default:
          throw new RuntimeException("Invalid map field number: " + number);
      }
    }

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    // Construct using com.google.cloud.automl.v1beta1.TablesDatasetMetadata.newBuilder()
    private Builder() {
      maybeForceBuilderInitialization();
    }

    private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
      super(parent);
      maybeForceBuilderInitialization();
    }

    private void maybeForceBuilderInitialization() {
      if (com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders) {}
    }

    @java.lang.Override
    public Builder clear() {
      super.clear();
      primaryTableSpecId_ = "";

      targetColumnSpecId_ = "";

      weightColumnSpecId_ = "";

      mlUseColumnSpecId_ = "";

      internalGetMutableTargetColumnCorrelations().clear();
      if (statsUpdateTimeBuilder_ == null) {
        statsUpdateTime_ = null;
      } else {
        statsUpdateTime_ = null;
        statsUpdateTimeBuilder_ = null;
      }
      return this;
    }

    @java.lang.Override
    public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
      return com.google.cloud.automl.v1beta1.Tables
          .internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_descriptor;
    }

    @java.lang.Override
    public com.google.cloud.automl.v1beta1.TablesDatasetMetadata getDefaultInstanceForType() {
      return com.google.cloud.automl.v1beta1.TablesDatasetMetadata.getDefaultInstance();
    }

    @java.lang.Override
    public com.google.cloud.automl.v1beta1.TablesDatasetMetadata build() {
      com.google.cloud.automl.v1beta1.TablesDatasetMetadata result = buildPartial();
      if (!result.isInitialized()) {
        throw newUninitializedMessageException(result);
      }
      return result;
    }

    @java.lang.Override
    public com.google.cloud.automl.v1beta1.TablesDatasetMetadata buildPartial() {
      com.google.cloud.automl.v1beta1.TablesDatasetMetadata result =
          new com.google.cloud.automl.v1beta1.TablesDatasetMetadata(this);
      int from_bitField0_ = bitField0_;
      int to_bitField0_ = 0;
      result.primaryTableSpecId_ = primaryTableSpecId_;
      result.targetColumnSpecId_ = targetColumnSpecId_;
      result.weightColumnSpecId_ = weightColumnSpecId_;
      result.mlUseColumnSpecId_ = mlUseColumnSpecId_;
      result.targetColumnCorrelations_ = internalGetTargetColumnCorrelations();
      result.targetColumnCorrelations_.makeImmutable();
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        result.statsUpdateTime_ = statsUpdateTime_;
      } else {
        result.statsUpdateTime_ = statsUpdateTimeBuilder_.build();
      }
      result.bitField0_ = to_bitField0_;
      onBuilt();
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    public Builder mergeFrom(com.google.protobuf.Message other) {
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      if (!other.getPrimaryTableSpecId().isEmpty()) {
        primaryTableSpecId_ = other.primaryTableSpecId_;
        onChanged();
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        onChanged();
      }
      if (!other.getWeightColumnSpecId().isEmpty()) {
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        onChanged();
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    @java.lang.Override
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        throws java.io.IOException {
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      } catch (com.google.protobuf.InvalidProtocolBufferException e) {
        parsedMessage =
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    private int bitField0_;

    private java.lang.Object primaryTableSpecId_ = "";
    /**
     *
     *
     * <pre>
     * Output only. The table_spec_id of the primary table of this dataset.
     * </pre>
     *
     * <code>string primary_table_spec_id = 1;</code>
     */
    public java.lang.String getPrimaryTableSpecId() {
      java.lang.Object ref = primaryTableSpecId_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        primaryTableSpecId_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. The table_spec_id of the primary table of this dataset.
     * </pre>
     *
     * <code>string primary_table_spec_id = 1;</code>
     */
    public com.google.protobuf.ByteString getPrimaryTableSpecIdBytes() {
      java.lang.Object ref = primaryTableSpecId_;
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        primaryTableSpecId_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. The table_spec_id of the primary table of this dataset.
     * </pre>
     *
     * <code>string primary_table_spec_id = 1;</code>
     */
    public Builder setPrimaryTableSpecId(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }

      primaryTableSpecId_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. The table_spec_id of the primary table of this dataset.
     * </pre>
     *
     * <code>string primary_table_spec_id = 1;</code>
     */
    public Builder clearPrimaryTableSpecId() {

      primaryTableSpecId_ = getDefaultInstance().getPrimaryTableSpecId();
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. The table_spec_id of the primary table of this dataset.
     * </pre>
     *
     * <code>string primary_table_spec_id = 1;</code>
     */
    public Builder setPrimaryTableSpecIdBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);

      primaryTableSpecId_ = value;
      onChanged();
      return this;
    }

    private java.lang.Object targetColumnSpecId_ = "";
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * training &amp; prediction target.
     * This column must be non-nullable and have one of following data types
     * (otherwise model creation will error):
     * * CATEGORY
     * * ARRAY(CATEGORY)
     * * FLOAT64
     * Furthermore, if the type is CATEGORY or ARRAY(CATEGORY), then only up to
     * 40 unique values may exist in that column across all rows, but for
     * ARRAY(CATEGORY) unique values are counted as elements of the ARRAY (i.e.
     * following 3 ARRAY-s: [A, B], [A], [B] are counted as having 2 unique
     * values).
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string target_column_spec_id = 2;</code>
     */
    public java.lang.String getTargetColumnSpecId() {
      java.lang.Object ref = targetColumnSpecId_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        targetColumnSpecId_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * training &amp; prediction target.
     * This column must be non-nullable and have one of following data types
     * (otherwise model creation will error):
     * * CATEGORY
     * * ARRAY(CATEGORY)
     * * FLOAT64
     * Furthermore, if the type is CATEGORY or ARRAY(CATEGORY), then only up to
     * 40 unique values may exist in that column across all rows, but for
     * ARRAY(CATEGORY) unique values are counted as elements of the ARRAY (i.e.
     * following 3 ARRAY-s: [A, B], [A], [B] are counted as having 2 unique
     * values).
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string target_column_spec_id = 2;</code>
     */
    public com.google.protobuf.ByteString getTargetColumnSpecIdBytes() {
      java.lang.Object ref = targetColumnSpecId_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        targetColumnSpecId_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * training &amp; prediction target.
     * This column must be non-nullable and have one of following data types
     * (otherwise model creation will error):
     * * CATEGORY
     * * ARRAY(CATEGORY)
     * * FLOAT64
     * Furthermore, if the type is CATEGORY or ARRAY(CATEGORY), then only up to
     * 40 unique values may exist in that column across all rows, but for
     * ARRAY(CATEGORY) unique values are counted as elements of the ARRAY (i.e.
     * following 3 ARRAY-s: [A, B], [A], [B] are counted as having 2 unique
     * values).
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string target_column_spec_id = 2;</code>
     */
    public Builder setTargetColumnSpecId(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }

      targetColumnSpecId_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * training &amp; prediction target.
     * This column must be non-nullable and have one of following data types
     * (otherwise model creation will error):
     * * CATEGORY
     * * ARRAY(CATEGORY)
     * * FLOAT64
     * Furthermore, if the type is CATEGORY or ARRAY(CATEGORY), then only up to
     * 40 unique values may exist in that column across all rows, but for
     * ARRAY(CATEGORY) unique values are counted as elements of the ARRAY (i.e.
     * following 3 ARRAY-s: [A, B], [A], [B] are counted as having 2 unique
     * values).
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string target_column_spec_id = 2;</code>
     */
    public Builder clearTargetColumnSpecId() {

      targetColumnSpecId_ = getDefaultInstance().getTargetColumnSpecId();
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * training &amp; prediction target.
     * This column must be non-nullable and have one of following data types
     * (otherwise model creation will error):
     * * CATEGORY
     * * ARRAY(CATEGORY)
     * * FLOAT64
     * Furthermore, if the type is CATEGORY or ARRAY(CATEGORY), then only up to
     * 40 unique values may exist in that column across all rows, but for
     * ARRAY(CATEGORY) unique values are counted as elements of the ARRAY (i.e.
     * following 3 ARRAY-s: [A, B], [A], [B] are counted as having 2 unique
     * values).
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string target_column_spec_id = 2;</code>
     */
    public Builder setTargetColumnSpecIdBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);

      targetColumnSpecId_ = value;
      onChanged();
      return this;
    }

    private java.lang.Object weightColumnSpecId_ = "";
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * weight column, i.e. the higher the value the more important the row will be
     * during model training.
     * Required type: FLOAT64.
     * Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
     *                 ignored for training.
     * If not set all rows are assumed to have equal weight of 1.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string weight_column_spec_id = 3;</code>
     */
    public java.lang.String getWeightColumnSpecId() {
      java.lang.Object ref = weightColumnSpecId_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        weightColumnSpecId_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * weight column, i.e. the higher the value the more important the row will be
     * during model training.
     * Required type: FLOAT64.
     * Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
     *                 ignored for training.
     * If not set all rows are assumed to have equal weight of 1.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string weight_column_spec_id = 3;</code>
     */
    public com.google.protobuf.ByteString getWeightColumnSpecIdBytes() {
      java.lang.Object ref = weightColumnSpecId_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        weightColumnSpecId_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * weight column, i.e. the higher the value the more important the row will be
     * during model training.
     * Required type: FLOAT64.
     * Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
     *                 ignored for training.
     * If not set all rows are assumed to have equal weight of 1.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string weight_column_spec_id = 3;</code>
     */
    public Builder setWeightColumnSpecId(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }

      weightColumnSpecId_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * weight column, i.e. the higher the value the more important the row will be
     * during model training.
     * Required type: FLOAT64.
     * Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
     *                 ignored for training.
     * If not set all rows are assumed to have equal weight of 1.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string weight_column_spec_id = 3;</code>
     */
    public Builder clearWeightColumnSpecId() {

      weightColumnSpecId_ = getDefaultInstance().getWeightColumnSpecId();
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table's column that should be used as the
     * weight column, i.e. the higher the value the more important the row will be
     * during model training.
     * Required type: FLOAT64.
     * Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
     *                 ignored for training.
     * If not set all rows are assumed to have equal weight of 1.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string weight_column_spec_id = 3;</code>
     */
    public Builder setWeightColumnSpecIdBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);

      weightColumnSpecId_ = value;
      onChanged();
      return this;
    }

    private java.lang.Object mlUseColumnSpecId_ = "";
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table column which specifies a possible ML
     * use of the row, i.e. the column will be used to split the rows into TRAIN,
     * VALIDATE and TEST sets.
     * Required type: STRING.
     * This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
     * among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
     * case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
     * that if a given ml use distribution makes it impossible to create a "good"
     * model, that call will error describing the issue.
     * If both this column_spec_id and primary table's time_column_spec_id are not
     * set, then all rows are treated as `UNASSIGNED`.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string ml_use_column_spec_id = 4;</code>
     */
    public java.lang.String getMlUseColumnSpecId() {
      java.lang.Object ref = mlUseColumnSpecId_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        mlUseColumnSpecId_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table column which specifies a possible ML
     * use of the row, i.e. the column will be used to split the rows into TRAIN,
     * VALIDATE and TEST sets.
     * Required type: STRING.
     * This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
     * among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
     * case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
     * that if a given ml use distribution makes it impossible to create a "good"
     * model, that call will error describing the issue.
     * If both this column_spec_id and primary table's time_column_spec_id are not
     * set, then all rows are treated as `UNASSIGNED`.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string ml_use_column_spec_id = 4;</code>
     */
    public com.google.protobuf.ByteString getMlUseColumnSpecIdBytes() {
      java.lang.Object ref = mlUseColumnSpecId_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        mlUseColumnSpecId_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table column which specifies a possible ML
     * use of the row, i.e. the column will be used to split the rows into TRAIN,
     * VALIDATE and TEST sets.
     * Required type: STRING.
     * This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
     * among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
     * case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
     * that if a given ml use distribution makes it impossible to create a "good"
     * model, that call will error describing the issue.
     * If both this column_spec_id and primary table's time_column_spec_id are not
     * set, then all rows are treated as `UNASSIGNED`.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string ml_use_column_spec_id = 4;</code>
     */
    public Builder setMlUseColumnSpecId(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }

      mlUseColumnSpecId_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table column which specifies a possible ML
     * use of the row, i.e. the column will be used to split the rows into TRAIN,
     * VALIDATE and TEST sets.
     * Required type: STRING.
     * This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
     * among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
     * case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
     * that if a given ml use distribution makes it impossible to create a "good"
     * model, that call will error describing the issue.
     * If both this column_spec_id and primary table's time_column_spec_id are not
     * set, then all rows are treated as `UNASSIGNED`.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string ml_use_column_spec_id = 4;</code>
     */
    public Builder clearMlUseColumnSpecId() {

      mlUseColumnSpecId_ = getDefaultInstance().getMlUseColumnSpecId();
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * column_spec_id of the primary table column which specifies a possible ML
     * use of the row, i.e. the column will be used to split the rows into TRAIN,
     * VALIDATE and TEST sets.
     * Required type: STRING.
     * This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
     * among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
     * case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
     * that if a given ml use distribution makes it impossible to create a "good"
     * model, that call will error describing the issue.
     * If both this column_spec_id and primary table's time_column_spec_id are not
     * set, then all rows are treated as `UNASSIGNED`.
     * NOTE: Updates of this field will instantly affect any other users
     * concurrently working with the dataset.
     * </pre>
     *
     * <code>string ml_use_column_spec_id = 4;</code>
     */
    public Builder setMlUseColumnSpecIdBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);

      mlUseColumnSpecId_ = value;
      onChanged();
      return this;
    }

    private com.google.protobuf.MapField<
            java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
        targetColumnCorrelations_;

    private com.google.protobuf.MapField<
            java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
        internalGetTargetColumnCorrelations() {
      if (targetColumnCorrelations_ == null) {
        return com.google.protobuf.MapField.emptyMapField(
            TargetColumnCorrelationsDefaultEntryHolder.defaultEntry);
      }
      return targetColumnCorrelations_;
    }

    private com.google.protobuf.MapField<
            java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
        internalGetMutableTargetColumnCorrelations() {
      onChanged();
      ;
      if (targetColumnCorrelations_ == null) {
        targetColumnCorrelations_ =
            com.google.protobuf.MapField.newMapField(
                TargetColumnCorrelationsDefaultEntryHolder.defaultEntry);
      }
      if (!targetColumnCorrelations_.isMutable()) {
        targetColumnCorrelations_ = targetColumnCorrelations_.copy();
      }
      return targetColumnCorrelations_;
    }

    public int getTargetColumnCorrelationsCount() {
      return internalGetTargetColumnCorrelations().getMap().size();
    }
    /**
     *
     *
     * <pre>
     * Output only. Correlations between
     * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
     * and other columns of the
     * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
     * Only set if the target column is set. Mapping from other column spec id to
     * its CorrelationStats with the target column.
     * This field may be stale, see the stats_update_time field for
     * for the timestamp at which these stats were last updated.
     * </pre>
     *
     * <code>
     * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
     * </code>
     */
    public boolean containsTargetColumnCorrelations(java.lang.String key) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      return internalGetTargetColumnCorrelations().getMap().containsKey(key);
    }
    /** Use {@link #getTargetColumnCorrelationsMap()} instead. */
    @java.lang.Deprecated
    public java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
        getTargetColumnCorrelations() {
      return getTargetColumnCorrelationsMap();
    }
    /**
     *
     *
     * <pre>
     * Output only. Correlations between
     * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
     * and other columns of the
     * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
     * Only set if the target column is set. Mapping from other column spec id to
     * its CorrelationStats with the target column.
     * This field may be stale, see the stats_update_time field for
     * for the timestamp at which these stats were last updated.
     * </pre>
     *
     * <code>
     * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
     * </code>
     */
    public java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
        getTargetColumnCorrelationsMap() {
      return internalGetTargetColumnCorrelations().getMap();
    }
    /**
     *
     *
     * <pre>
     * Output only. Correlations between
     * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
     * and other columns of the
     * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
     * Only set if the target column is set. Mapping from other column spec id to
     * its CorrelationStats with the target column.
     * This field may be stale, see the stats_update_time field for
     * for the timestamp at which these stats were last updated.
     * </pre>
     *
     * <code>
     * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
     * </code>
     */
    public com.google.cloud.automl.v1beta1.CorrelationStats getTargetColumnCorrelationsOrDefault(
        java.lang.String key, com.google.cloud.automl.v1beta1.CorrelationStats defaultValue) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats> map =
          internalGetTargetColumnCorrelations().getMap();
      return map.containsKey(key) ? map.get(key) : defaultValue;
    }
    /**
     *
     *
     * <pre>
     * Output only. Correlations between
     * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
     * and other columns of the
     * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
     * Only set if the target column is set. Mapping from other column spec id to
     * its CorrelationStats with the target column.
     * This field may be stale, see the stats_update_time field for
     * for the timestamp at which these stats were last updated.
     * </pre>
     *
     * <code>
     * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
     * </code>
     */
    public com.google.cloud.automl.v1beta1.CorrelationStats getTargetColumnCorrelationsOrThrow(
        java.lang.String key) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats> map =
          internalGetTargetColumnCorrelations().getMap();
      if (!map.containsKey(key)) {
        throw new java.lang.IllegalArgumentException();
      }
      return map.get(key);
    }

    public Builder clearTargetColumnCorrelations() {
      internalGetMutableTargetColumnCorrelations().getMutableMap().clear();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Correlations between
     * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
     * and other columns of the
     * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
     * Only set if the target column is set. Mapping from other column spec id to
     * its CorrelationStats with the target column.
     * This field may be stale, see the stats_update_time field for
     * for the timestamp at which these stats were last updated.
     * </pre>
     *
     * <code>
     * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
     * </code>
     */
    public Builder removeTargetColumnCorrelations(java.lang.String key) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      internalGetMutableTargetColumnCorrelations().getMutableMap().remove(key);
      return this;
    }
    /** Use alternate mutation accessors instead. */
    @java.lang.Deprecated
    public java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
        getMutableTargetColumnCorrelations() {
      return internalGetMutableTargetColumnCorrelations().getMutableMap();
    }
    /**
     *
     *
     * <pre>
     * Output only. Correlations between
     * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
     * and other columns of the
     * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
     * Only set if the target column is set. Mapping from other column spec id to
     * its CorrelationStats with the target column.
     * This field may be stale, see the stats_update_time field for
     * for the timestamp at which these stats were last updated.
     * </pre>
     *
     * <code>
     * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
     * </code>
     */
    public Builder putTargetColumnCorrelations(
        java.lang.String key, com.google.cloud.automl.v1beta1.CorrelationStats value) {
      if (key == null) {
        throw new java.lang.NullPointerException();
      }
      if (value == null) {
        throw new java.lang.NullPointerException();
      }
      internalGetMutableTargetColumnCorrelations().getMutableMap().put(key, value);
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Correlations between
     * [target_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column],
     * and other columns of the
     * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
     * Only set if the target column is set. Mapping from other column spec id to
     * its CorrelationStats with the target column.
     * This field may be stale, see the stats_update_time field for
     * for the timestamp at which these stats were last updated.
     * </pre>
     *
     * <code>
     * map&lt;string, .google.cloud.automl.v1beta1.CorrelationStats&gt; target_column_correlations = 6;
     * </code>
     */
    public Builder putAllTargetColumnCorrelations(
        java.util.Map<java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats> values) {
      internalGetMutableTargetColumnCorrelations().getMutableMap().putAll(values);
      return this;
    }

    private com.google.protobuf.Timestamp statsUpdateTime_;
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.protobuf.Timestamp,
            com.google.protobuf.Timestamp.Builder,
            com.google.protobuf.TimestampOrBuilder>
        statsUpdateTimeBuilder_;
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    public boolean hasStatsUpdateTime() {
      return statsUpdateTimeBuilder_ != null || statsUpdateTime_ != null;
    }
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    public com.google.protobuf.Timestamp getStatsUpdateTime() {
      if (statsUpdateTimeBuilder_ == null) {
        return statsUpdateTime_ == null
            ? com.google.protobuf.Timestamp.getDefaultInstance()
            : statsUpdateTime_;
      } else {
        return statsUpdateTimeBuilder_.getMessage();
      }
    }
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    public Builder setStatsUpdateTime(com.google.protobuf.Timestamp value) {
      if (statsUpdateTimeBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        statsUpdateTime_ = value;
        onChanged();
      } else {
        statsUpdateTimeBuilder_.setMessage(value);
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    public Builder setStatsUpdateTime(com.google.protobuf.Timestamp.Builder builderForValue) {
      if (statsUpdateTimeBuilder_ == null) {
        statsUpdateTime_ = builderForValue.build();
        onChanged();
      } else {
        statsUpdateTimeBuilder_.setMessage(builderForValue.build());
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    public Builder mergeStatsUpdateTime(com.google.protobuf.Timestamp value) {
      if (statsUpdateTimeBuilder_ == null) {
        if (statsUpdateTime_ != null) {
          statsUpdateTime_ =
              com.google.protobuf.Timestamp.newBuilder(statsUpdateTime_)
                  .mergeFrom(value)
                  .buildPartial();
        } else {
          statsUpdateTime_ = value;
        }
        onChanged();
      } else {
        statsUpdateTimeBuilder_.mergeFrom(value);
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    public Builder clearStatsUpdateTime() {
      if (statsUpdateTimeBuilder_ == null) {
        statsUpdateTime_ = null;
        onChanged();
      } else {
        statsUpdateTime_ = null;
        statsUpdateTimeBuilder_ = null;
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    public com.google.protobuf.Timestamp.Builder getStatsUpdateTimeBuilder() {

      onChanged();
      return getStatsUpdateTimeFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    public com.google.protobuf.TimestampOrBuilder getStatsUpdateTimeOrBuilder() {
      if (statsUpdateTimeBuilder_ != null) {
        return statsUpdateTimeBuilder_.getMessageOrBuilder();
      } else {
        return statsUpdateTime_ == null
            ? com.google.protobuf.Timestamp.getDefaultInstance()
            : statsUpdateTime_;
      }
    }
    /**
     *
     *
     * <pre>
     * The most recent timestamp when target_column_correlations field and all
     * descendant ColumnSpec.data_stats and ColumnSpec.top_correlated_columns
     * fields were last (re-)generated. Any changes that happened to the dataset
     * afterwards are not reflected in these fields values. The regeneration
     * happens in the background on a best effort basis.
     * </pre>
     *
     * <code>.google.protobuf.Timestamp stats_update_time = 7;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.protobuf.Timestamp,
            com.google.protobuf.Timestamp.Builder,
            com.google.protobuf.TimestampOrBuilder>
        getStatsUpdateTimeFieldBuilder() {
      if (statsUpdateTimeBuilder_ == null) {
        statsUpdateTimeBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.protobuf.Timestamp,
                com.google.protobuf.Timestamp.Builder,
                com.google.protobuf.TimestampOrBuilder>(
                getStatsUpdateTime(), getParentForChildren(), isClean());
        statsUpdateTime_ = null;
      }
      return statsUpdateTimeBuilder_;
    }

    @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.automl.v1beta1.TablesDatasetMetadata)
  }

  // @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.TablesDatasetMetadata)
  private static final com.google.cloud.automl.v1beta1.TablesDatasetMetadata DEFAULT_INSTANCE;

  static {
    DEFAULT_INSTANCE = new com.google.cloud.automl.v1beta1.TablesDatasetMetadata();
  }

  public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata getDefaultInstance() {
    return DEFAULT_INSTANCE;
  }

  private static final com.google.protobuf.Parser<TablesDatasetMetadata> PARSER =
      new com.google.protobuf.AbstractParser<TablesDatasetMetadata>() {
        @java.lang.Override
        public TablesDatasetMetadata parsePartialFrom(
            com.google.protobuf.CodedInputStream input,
            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws com.google.protobuf.InvalidProtocolBufferException {
          return new TablesDatasetMetadata(input, extensionRegistry);
        }
      };

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

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

  @java.lang.Override
  public com.google.cloud.automl.v1beta1.TablesDatasetMetadata getDefaultInstanceForType() {
    return DEFAULT_INSTANCE;
  }
}
