/*
 * Copyright 2020 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/privacy/dlp/v2/dlp.proto

package com.google.privacy.dlp.v2;

/**
 *
 *
 * <pre>
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 *
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  /**
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   *
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   *
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  /**
   *
   *
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   *
   * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
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   *
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   *
   *
   * <pre>
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   *
   * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
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   *
   * <pre>
   * A column can be tagged with a InfoType to use the relevant public
   * dataset as a statistical model of population, if available. We
   * currently support US ZIP codes, region codes, ages and genders.
   * To programmatically obtain the list of supported InfoTypes, use
   * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
   * </pre>
   *
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   *
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   * To programmatically obtain the list of supported InfoTypes, use
   * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
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   * To programmatically obtain the list of supported InfoTypes, use
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                input.readMessage(getInferredFieldBuilder().getBuilder(), extensionRegistry);
                tagCase_ = 4;
                break;
              } // case 34
            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 tagCase_ = 0;
    private java.lang.Object tag_;

    public TagCase getTagCase() {
      return TagCase.forNumber(tagCase_);
    }

    public Builder clearTag() {
      tagCase_ = 0;
      tag_ = null;
      onChanged();
      return this;
    }

    private int bitField0_;

    private com.google.privacy.dlp.v2.FieldId field_;
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.privacy.dlp.v2.FieldId,
            com.google.privacy.dlp.v2.FieldId.Builder,
            com.google.privacy.dlp.v2.FieldIdOrBuilder>
        fieldBuilder_;
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     *
     * @return Whether the field field is set.
     */
    public boolean hasField() {
      return ((bitField0_ & 0x00000001) != 0);
    }
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     *
     * @return The field.
     */
    public com.google.privacy.dlp.v2.FieldId getField() {
      if (fieldBuilder_ == null) {
        return field_ == null ? com.google.privacy.dlp.v2.FieldId.getDefaultInstance() : field_;
      } else {
        return fieldBuilder_.getMessage();
      }
    }
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     */
    public Builder setField(com.google.privacy.dlp.v2.FieldId value) {
      if (fieldBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        field_ = value;
      } else {
        fieldBuilder_.setMessage(value);
      }
      bitField0_ |= 0x00000001;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     */
    public Builder setField(com.google.privacy.dlp.v2.FieldId.Builder builderForValue) {
      if (fieldBuilder_ == null) {
        field_ = builderForValue.build();
      } else {
        fieldBuilder_.setMessage(builderForValue.build());
      }
      bitField0_ |= 0x00000001;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     */
    public Builder mergeField(com.google.privacy.dlp.v2.FieldId value) {
      if (fieldBuilder_ == null) {
        if (((bitField0_ & 0x00000001) != 0)
            && field_ != null
            && field_ != com.google.privacy.dlp.v2.FieldId.getDefaultInstance()) {
          getFieldBuilder().mergeFrom(value);
        } else {
          field_ = value;
        }
      } else {
        fieldBuilder_.mergeFrom(value);
      }
      bitField0_ |= 0x00000001;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     */
    public Builder clearField() {
      bitField0_ = (bitField0_ & ~0x00000001);
      field_ = null;
      if (fieldBuilder_ != null) {
        fieldBuilder_.dispose();
        fieldBuilder_ = null;
      }
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     */
    public com.google.privacy.dlp.v2.FieldId.Builder getFieldBuilder() {
      bitField0_ |= 0x00000001;
      onChanged();
      return getFieldFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     */
    public com.google.privacy.dlp.v2.FieldIdOrBuilder getFieldOrBuilder() {
      if (fieldBuilder_ != null) {
        return fieldBuilder_.getMessageOrBuilder();
      } else {
        return field_ == null ? com.google.privacy.dlp.v2.FieldId.getDefaultInstance() : field_;
      }
    }
    /**
     *
     *
     * <pre>
     * Required. Identifies the column.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.FieldId field = 1 [(.google.api.field_behavior) = REQUIRED];
     * </code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.privacy.dlp.v2.FieldId,
            com.google.privacy.dlp.v2.FieldId.Builder,
            com.google.privacy.dlp.v2.FieldIdOrBuilder>
        getFieldFieldBuilder() {
      if (fieldBuilder_ == null) {
        fieldBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.privacy.dlp.v2.FieldId,
                com.google.privacy.dlp.v2.FieldId.Builder,
                com.google.privacy.dlp.v2.FieldIdOrBuilder>(
                getField(), getParentForChildren(), isClean());
        field_ = null;
      }
      return fieldBuilder_;
    }

    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.privacy.dlp.v2.InfoType,
            com.google.privacy.dlp.v2.InfoType.Builder,
            com.google.privacy.dlp.v2.InfoTypeOrBuilder>
        infoTypeBuilder_;
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     *
     * @return Whether the infoType field is set.
     */
    @java.lang.Override
    public boolean hasInfoType() {
      return tagCase_ == 2;
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     *
     * @return The infoType.
     */
    @java.lang.Override
    public com.google.privacy.dlp.v2.InfoType getInfoType() {
      if (infoTypeBuilder_ == null) {
        if (tagCase_ == 2) {
          return (com.google.privacy.dlp.v2.InfoType) tag_;
        }
        return com.google.privacy.dlp.v2.InfoType.getDefaultInstance();
      } else {
        if (tagCase_ == 2) {
          return infoTypeBuilder_.getMessage();
        }
        return com.google.privacy.dlp.v2.InfoType.getDefaultInstance();
      }
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     */
    public Builder setInfoType(com.google.privacy.dlp.v2.InfoType value) {
      if (infoTypeBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        tag_ = value;
        onChanged();
      } else {
        infoTypeBuilder_.setMessage(value);
      }
      tagCase_ = 2;
      return this;
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     */
    public Builder setInfoType(com.google.privacy.dlp.v2.InfoType.Builder builderForValue) {
      if (infoTypeBuilder_ == null) {
        tag_ = builderForValue.build();
        onChanged();
      } else {
        infoTypeBuilder_.setMessage(builderForValue.build());
      }
      tagCase_ = 2;
      return this;
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     */
    public Builder mergeInfoType(com.google.privacy.dlp.v2.InfoType value) {
      if (infoTypeBuilder_ == null) {
        if (tagCase_ == 2 && tag_ != com.google.privacy.dlp.v2.InfoType.getDefaultInstance()) {
          tag_ =
              com.google.privacy.dlp.v2.InfoType.newBuilder(
                      (com.google.privacy.dlp.v2.InfoType) tag_)
                  .mergeFrom(value)
                  .buildPartial();
        } else {
          tag_ = value;
        }
        onChanged();
      } else {
        if (tagCase_ == 2) {
          infoTypeBuilder_.mergeFrom(value);
        } else {
          infoTypeBuilder_.setMessage(value);
        }
      }
      tagCase_ = 2;
      return this;
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     */
    public Builder clearInfoType() {
      if (infoTypeBuilder_ == null) {
        if (tagCase_ == 2) {
          tagCase_ = 0;
          tag_ = null;
          onChanged();
        }
      } else {
        if (tagCase_ == 2) {
          tagCase_ = 0;
          tag_ = null;
        }
        infoTypeBuilder_.clear();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     */
    public com.google.privacy.dlp.v2.InfoType.Builder getInfoTypeBuilder() {
      return getInfoTypeFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     */
    @java.lang.Override
    public com.google.privacy.dlp.v2.InfoTypeOrBuilder getInfoTypeOrBuilder() {
      if ((tagCase_ == 2) && (infoTypeBuilder_ != null)) {
        return infoTypeBuilder_.getMessageOrBuilder();
      } else {
        if (tagCase_ == 2) {
          return (com.google.privacy.dlp.v2.InfoType) tag_;
        }
        return com.google.privacy.dlp.v2.InfoType.getDefaultInstance();
      }
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a InfoType to use the relevant public
     * dataset as a statistical model of population, if available. We
     * currently support US ZIP codes, region codes, ages and genders.
     * To programmatically obtain the list of supported InfoTypes, use
     * ListInfoTypes with the supported_by=RISK_ANALYSIS filter.
     * </pre>
     *
     * <code>.google.privacy.dlp.v2.InfoType info_type = 2;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.privacy.dlp.v2.InfoType,
            com.google.privacy.dlp.v2.InfoType.Builder,
            com.google.privacy.dlp.v2.InfoTypeOrBuilder>
        getInfoTypeFieldBuilder() {
      if (infoTypeBuilder_ == null) {
        if (!(tagCase_ == 2)) {
          tag_ = com.google.privacy.dlp.v2.InfoType.getDefaultInstance();
        }
        infoTypeBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.privacy.dlp.v2.InfoType,
                com.google.privacy.dlp.v2.InfoType.Builder,
                com.google.privacy.dlp.v2.InfoTypeOrBuilder>(
                (com.google.privacy.dlp.v2.InfoType) tag_, getParentForChildren(), isClean());
        tag_ = null;
      }
      tagCase_ = 2;
      onChanged();
      return infoTypeBuilder_;
    }

    /**
     *
     *
     * <pre>
     * A column can be tagged with a custom tag. In this case, the user must
     * indicate an auxiliary table that contains statistical information on
     * the possible values of this column (below).
     * </pre>
     *
     * <code>string custom_tag = 3;</code>
     *
     * @return Whether the customTag field is set.
     */
    @java.lang.Override
    public boolean hasCustomTag() {
      return tagCase_ == 3;
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a custom tag. In this case, the user must
     * indicate an auxiliary table that contains statistical information on
     * the possible values of this column (below).
     * </pre>
     *
     * <code>string custom_tag = 3;</code>
     *
     * @return The customTag.
     */
    @java.lang.Override
    public java.lang.String getCustomTag() {
      java.lang.Object ref = "";
      if (tagCase_ == 3) {
        ref = tag_;
      }
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        if (tagCase_ == 3) {
          tag_ = s;
        }
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a custom tag. In this case, the user must
     * indicate an auxiliary table that contains statistical information on
     * the possible values of this column (below).
     * </pre>
     *
     * <code>string custom_tag = 3;</code>
     *
     * @return The bytes for customTag.
     */
    @java.lang.Override
    public com.google.protobuf.ByteString getCustomTagBytes() {
      java.lang.Object ref = "";
      if (tagCase_ == 3) {
        ref = tag_;
      }
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        if (tagCase_ == 3) {
          tag_ = b;
        }
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a custom tag. In this case, the user must
     * indicate an auxiliary table that contains statistical information on
     * the possible values of this column (below).
     * </pre>
     *
     * <code>string custom_tag = 3;</code>
     *
     * @param value The customTag to set.
     * @return This builder for chaining.
     */
    public Builder setCustomTag(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      tagCase_ = 3;
      tag_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a custom tag. In this case, the user must
     * indicate an auxiliary table that contains statistical information on
     * the possible values of this column (below).
     * </pre>
     *
     * <code>string custom_tag = 3;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearCustomTag() {
      if (tagCase_ == 3) {
        tagCase_ = 0;
        tag_ = null;
        onChanged();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * A column can be tagged with a custom tag. In this case, the user must
     * indicate an auxiliary table that contains statistical information on
     * the possible values of this column (below).
     * </pre>
     *
     * <code>string custom_tag = 3;</code>
     *
     * @param value The bytes for customTag to set.
     * @return This builder for chaining.
     */
    public Builder setCustomTagBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      tagCase_ = 3;
      tag_ = value;
      onChanged();
      return this;
    }

    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.protobuf.Empty,
            com.google.protobuf.Empty.Builder,
            com.google.protobuf.EmptyOrBuilder>
        inferredBuilder_;
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     *
     * @return Whether the inferred field is set.
     */
    @java.lang.Override
    public boolean hasInferred() {
      return tagCase_ == 4;
    }
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     *
     * @return The inferred.
     */
    @java.lang.Override
    public com.google.protobuf.Empty getInferred() {
      if (inferredBuilder_ == null) {
        if (tagCase_ == 4) {
          return (com.google.protobuf.Empty) tag_;
        }
        return com.google.protobuf.Empty.getDefaultInstance();
      } else {
        if (tagCase_ == 4) {
          return inferredBuilder_.getMessage();
        }
        return com.google.protobuf.Empty.getDefaultInstance();
      }
    }
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     */
    public Builder setInferred(com.google.protobuf.Empty value) {
      if (inferredBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        tag_ = value;
        onChanged();
      } else {
        inferredBuilder_.setMessage(value);
      }
      tagCase_ = 4;
      return this;
    }
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     */
    public Builder setInferred(com.google.protobuf.Empty.Builder builderForValue) {
      if (inferredBuilder_ == null) {
        tag_ = builderForValue.build();
        onChanged();
      } else {
        inferredBuilder_.setMessage(builderForValue.build());
      }
      tagCase_ = 4;
      return this;
    }
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     */
    public Builder mergeInferred(com.google.protobuf.Empty value) {
      if (inferredBuilder_ == null) {
        if (tagCase_ == 4 && tag_ != com.google.protobuf.Empty.getDefaultInstance()) {
          tag_ =
              com.google.protobuf.Empty.newBuilder((com.google.protobuf.Empty) tag_)
                  .mergeFrom(value)
                  .buildPartial();
        } else {
          tag_ = value;
        }
        onChanged();
      } else {
        if (tagCase_ == 4) {
          inferredBuilder_.mergeFrom(value);
        } else {
          inferredBuilder_.setMessage(value);
        }
      }
      tagCase_ = 4;
      return this;
    }
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     */
    public Builder clearInferred() {
      if (inferredBuilder_ == null) {
        if (tagCase_ == 4) {
          tagCase_ = 0;
          tag_ = null;
          onChanged();
        }
      } else {
        if (tagCase_ == 4) {
          tagCase_ = 0;
          tag_ = null;
        }
        inferredBuilder_.clear();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     */
    public com.google.protobuf.Empty.Builder getInferredBuilder() {
      return getInferredFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     */
    @java.lang.Override
    public com.google.protobuf.EmptyOrBuilder getInferredOrBuilder() {
      if ((tagCase_ == 4) && (inferredBuilder_ != null)) {
        return inferredBuilder_.getMessageOrBuilder();
      } else {
        if (tagCase_ == 4) {
          return (com.google.protobuf.Empty) tag_;
        }
        return com.google.protobuf.Empty.getDefaultInstance();
      }
    }
    /**
     *
     *
     * <pre>
     * If no semantic tag is indicated, we infer the statistical model from
     * the distribution of values in the input data
     * </pre>
     *
     * <code>.google.protobuf.Empty inferred = 4;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.protobuf.Empty,
            com.google.protobuf.Empty.Builder,
            com.google.protobuf.EmptyOrBuilder>
        getInferredFieldBuilder() {
      if (inferredBuilder_ == null) {
        if (!(tagCase_ == 4)) {
          tag_ = com.google.protobuf.Empty.getDefaultInstance();
        }
        inferredBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.protobuf.Empty,
                com.google.protobuf.Empty.Builder,
                com.google.protobuf.EmptyOrBuilder>(
                (com.google.protobuf.Empty) tag_, getParentForChildren(), isClean());
        tag_ = null;
      }
      tagCase_ = 4;
      onChanged();
      return inferredBuilder_;
    }

    @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.privacy.dlp.v2.QuasiId)
  }

  // @@protoc_insertion_point(class_scope:google.privacy.dlp.v2.QuasiId)
  private static final com.google.privacy.dlp.v2.QuasiId DEFAULT_INSTANCE;

  static {
    DEFAULT_INSTANCE = new com.google.privacy.dlp.v2.QuasiId();
  }

  public static com.google.privacy.dlp.v2.QuasiId getDefaultInstance() {
    return DEFAULT_INSTANCE;
  }

  private static final com.google.protobuf.Parser<QuasiId> PARSER =
      new com.google.protobuf.AbstractParser<QuasiId>() {
        @java.lang.Override
        public QuasiId 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<QuasiId> parser() {
    return PARSER;
  }

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

  @java.lang.Override
  public com.google.privacy.dlp.v2.QuasiId getDefaultInstanceForType() {
    return DEFAULT_INSTANCE;
  }
}
