/*
 * Copyright 2023 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: google/cloud/automl/v1beta1/io.proto

package com.google.cloud.automl.v1beta1;

/**
 *
 *
 * <pre>
 * Input configuration for ImportData Action.
 *
 * The format of input depends on dataset_metadata the Dataset into which
 * the import is happening has. As input source the
 * [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source]
 * is expected, unless specified otherwise. Additionally any input .CSV file
 * by itself must be 100MB or smaller, unless specified otherwise.
 * If an "example" file (that is, image, video etc.) with identical content
 * (even if it had different GCS_FILE_PATH) is mentioned multiple times, then
 * its label, bounding boxes etc. are appended. The same file should be always
 * provided with the same ML_USE and GCS_FILE_PATH, if it is not, then
 * these values are nondeterministically selected from the given ones.
 *
 * The formats are represented in EBNF with commas being literal and with
 * non-terminal symbols defined near the end of this comment. The formats are:
 *
 *  *  For Image Classification:
 *         CSV file(s) with each line in format:
 *           ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
 *           GCS_FILE_PATH leads to image of up to 30MB in size. Supported
 *           extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO
 *           For MULTICLASS classification type, at most one LABEL is allowed
 *           per image. If an image has not yet been labeled, then it should be
 *           mentioned just once with no LABEL.
 *         Some sample rows:
 *           TRAIN,gs://folder/image1.jpg,daisy
 *           TEST,gs://folder/image2.jpg,dandelion,tulip,rose
 *           UNASSIGNED,gs://folder/image3.jpg,daisy
 *           UNASSIGNED,gs://folder/image4.jpg
 *
 *  *  For Image Object Detection:
 *         CSV file(s) with each line in format:
 *           ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,)
 *           GCS_FILE_PATH leads to image of up to 30MB in size. Supported
 *           extensions: .JPEG, .GIF, .PNG.
 *           Each image is assumed to be exhaustively labeled. The minimum
 *           allowed BOUNDING_BOX edge length is 0.01, and no more than 500
 *           BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined
 *           per line). If an image has not yet been labeled, then it should be
 *           mentioned just once with no LABEL and the ",,,,,,," in place of the
 *           BOUNDING_BOX. For images which are known to not contain any
 *           bounding boxes, they should be labelled explictly as
 *           "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the
 *           BOUNDING_BOX.
 *         Sample rows:
 *           TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,,
 *           TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,,
 *           UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3
 *           TEST,gs://folder/im3.png,,,,,,,,,
 *           TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,
 *
 *  *  For Video Classification:
 *         CSV file(s) with each line in format:
 *           ML_USE,GCS_FILE_PATH
 *           where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH
 *           should lead to another .csv file which describes examples that have
 *           given ML_USE, using the following row format:
 *           GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,)
 *           Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
 *           to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
 *           TIME_SEGMENT_START and TIME_SEGMENT_END must be within the
 *           length of the video, and end has to be after the start. Any segment
 *           of a video which has one or more labels on it, is considered a
 *           hard negative for all other labels. Any segment with no labels on
 *           it is considered to be unknown. If a whole video is unknown, then
 *           it shuold be mentioned just once with ",," in place of LABEL,
 *           TIME_SEGMENT_START,TIME_SEGMENT_END.
 *         Sample top level CSV file:
 *           TRAIN,gs://folder/train_videos.csv
 *           TEST,gs://folder/test_videos.csv
 *           UNASSIGNED,gs://folder/other_videos.csv
 *         Sample rows of a CSV file for a particular ML_USE:
 *           gs://folder/video1.avi,car,120,180.000021
 *           gs://folder/video1.avi,bike,150,180.000021
 *           gs://folder/vid2.avi,car,0,60.5
 *           gs://folder/vid3.avi,,,
 *
 *  *  For Video Object Tracking:
 *         CSV file(s) with each line in format:
 *           ML_USE,GCS_FILE_PATH
 *           where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH
 *           should lead to another .csv file which describes examples that have
 *           given ML_USE, using one of the following row format:
 *           GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX
 *           or
 *           GCS_FILE_PATH,,,,,,,,,,
 *           Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
 *           to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
 *           Providing INSTANCE_IDs can help to obtain a better model. When
 *           a specific labeled entity leaves the video frame, and shows up
 *           afterwards it is not required, albeit preferable, that the same
 *           INSTANCE_ID is given to it.
 *           TIMESTAMP must be within the length of the video, the
 *           BOUNDING_BOX is assumed to be drawn on the closest video's frame
 *           to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected
 *           to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per
 *           frame are allowed. If a whole video is unknown, then it should be
 *           mentioned just once with ",,,,,,,,,," in place of LABEL,
 *           [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX.
 *         Sample top level CSV file:
 *           TRAIN,gs://folder/train_videos.csv
 *           TEST,gs://folder/test_videos.csv
 *           UNASSIGNED,gs://folder/other_videos.csv
 *         Seven sample rows of a CSV file for a particular ML_USE:
 *           gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
 *           gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
 *           gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
 *           gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
 *           gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,,
 *           gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,,
 *           gs://folder/video2.avi,,,,,,,,,,,
 *  *  For Text Extraction:
 *         CSV file(s) with each line in format:
 *           ML_USE,GCS_FILE_PATH
 *           GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which
 *           either imports text in-line or as documents. Any given
 *           .JSONL file must be 100MB or smaller.
 *           The in-line .JSONL file contains, per line, a proto that wraps a
 *           TextSnippet proto (in json representation) followed by one or more
 *           AnnotationPayload protos (called annotations), which have
 *           display_name and text_extraction detail populated. The given text
 *           is expected to be annotated exhaustively, for example, if you look
 *           for animals and text contains "dolphin" that is not labeled, then
 *           "dolphin" is assumed to not be an animal. Any given text snippet
 *           content must be 10KB or smaller, and also be UTF-8 NFC encoded
 *           (ASCII already is).
 *           The document .JSONL file contains, per line, a proto that wraps a
 *           Document proto. The Document proto must have either document_text
 *           or input_config set. In document_text case, the Document proto may
 *           also contain the spatial information of the document, including
 *           layout, document dimension and page number. In input_config case,
 *           only PDF documents are supported now, and each document may be up
 *           to 2MB large. Currently, annotations on documents cannot be
 *           specified at import.
 *         Three sample CSV rows:
 *           TRAIN,gs://folder/file1.jsonl
 *           VALIDATE,gs://folder/file2.jsonl
 *           TEST,gs://folder/file3.jsonl
 *         Sample in-line JSON Lines file for entity extraction (presented here
 *         with artificial line breaks, but the only actual line break is
 *         denoted by &#92;n).:
 *           {
 *             "document": {
 *               "document_text": {"content": "dog cat"}
 *               "layout": [
 *                 {
 *                   "text_segment": {
 *                     "start_offset": 0,
 *                     "end_offset": 3,
 *                   },
 *                   "page_number": 1,
 *                   "bounding_poly": {
 *                     "normalized_vertices": [
 *                       {"x": 0.1, "y": 0.1},
 *                       {"x": 0.1, "y": 0.3},
 *                       {"x": 0.3, "y": 0.3},
 *                       {"x": 0.3, "y": 0.1},
 *                     ],
 *                   },
 *                   "text_segment_type": TOKEN,
 *                 },
 *                 {
 *                   "text_segment": {
 *                     "start_offset": 4,
 *                     "end_offset": 7,
 *                   },
 *                   "page_number": 1,
 *                   "bounding_poly": {
 *                     "normalized_vertices": [
 *                       {"x": 0.4, "y": 0.1},
 *                       {"x": 0.4, "y": 0.3},
 *                       {"x": 0.8, "y": 0.3},
 *                       {"x": 0.8, "y": 0.1},
 *                     ],
 *                   },
 *                   "text_segment_type": TOKEN,
 *                 }
 *
 *               ],
 *               "document_dimensions": {
 *                 "width": 8.27,
 *                 "height": 11.69,
 *                 "unit": INCH,
 *               }
 *               "page_count": 1,
 *             },
 *             "annotations": [
 *               {
 *                 "display_name": "animal",
 *                 "text_extraction": {"text_segment": {"start_offset": 0,
 *                 "end_offset": 3}}
 *               },
 *               {
 *                 "display_name": "animal",
 *                 "text_extraction": {"text_segment": {"start_offset": 4,
 *                 "end_offset": 7}}
 *               }
 *             ],
 *           }&#92;n
 *           {
 *              "text_snippet": {
 *                "content": "This dog is good."
 *              },
 *              "annotations": [
 *                {
 *                  "display_name": "animal",
 *                  "text_extraction": {
 *                    "text_segment": {"start_offset": 5, "end_offset": 8}
 *                  }
 *                }
 *              ]
 *           }
 *         Sample document JSON Lines file (presented here with artificial line
 *         breaks, but the only actual line break is denoted by &#92;n).:
 *           {
 *             "document": {
 *               "input_config": {
 *                 "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
 *                 }
 *               }
 *             }
 *           }&#92;n
 *           {
 *             "document": {
 *               "input_config": {
 *                 "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
 *                 }
 *               }
 *             }
 *           }
 *
 *  *  For Text Classification:
 *         CSV file(s) with each line in format:
 *           ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...
 *           TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
 *           the column content is a valid gcs file path, i.e. prefixed by
 *           "gs://", it will be treated as a GCS_FILE_PATH, else if the content
 *           is enclosed within double quotes (""), it is
 *           treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
 *           must lead to a .txt file with UTF-8 encoding, for example,
 *           "gs://folder/content.txt", and the content in it is extracted
 *           as a text snippet. In TEXT_SNIPPET case, the column content
 *           excluding quotes is treated as to be imported text snippet. In
 *           both cases, the text snippet/file size must be within 128kB.
 *           Maximum 100 unique labels are allowed per CSV row.
 *         Sample rows:
 *           TRAIN,"They have bad food and very rude",RudeService,BadFood
 *           TRAIN,gs://folder/content.txt,SlowService
 *           TEST,"Typically always bad service there.",RudeService
 *           VALIDATE,"Stomach ache to go.",BadFood
 *
 *  *  For Text Sentiment:
 *         CSV file(s) with each line in format:
 *           ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT
 *           TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
 *           the column content is a valid gcs file path, that is, prefixed by
 *           "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated
 *           as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
 *           must lead to a .txt file with UTF-8 encoding, for example,
 *           "gs://folder/content.txt", and the content in it is extracted
 *           as a text snippet. In TEXT_SNIPPET case, the column content itself
 *           is treated as to be imported text snippet. In both cases, the
 *           text snippet must be up to 500 characters long.
 *         Sample rows:
 *           TRAIN,"&#64;freewrytin this is way too good for your product",2
 *           TRAIN,"I need this product so bad",3
 *           TEST,"Thank you for this product.",4
 *           VALIDATE,gs://folder/content.txt,2
 *
 *   *  For Tables:
 *         Either
 *         [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or
 *
 * [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]
 *         can be used. All inputs is concatenated into a single
 *
 * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name]
 *         For gcs_source:
 *           CSV file(s), where the first row of the first file is the header,
 *           containing unique column names. If the first row of a subsequent
 *           file is the same as the header, then it is also treated as a
 *           header. All other rows contain values for the corresponding
 *           columns.
 *           Each .CSV file by itself must be 10GB or smaller, and their total
 *           size must be 100GB or smaller.
 *           First three sample rows of a CSV file:
 *           "Id","First Name","Last Name","Dob","Addresses"
 *
 * "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
 *
 * "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
 *         For bigquery_source:
 *           An URI of a BigQuery table. The user data size of the BigQuery
 *           table must be 100GB or smaller.
 *         An imported table must have between 2 and 1,000 columns, inclusive,
 *         and between 1000 and 100,000,000 rows, inclusive. There are at most 5
 *         import data running in parallel.
 *  Definitions:
 *  ML_USE = "TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED"
 *           Describes how the given example (file) should be used for model
 *           training. "UNASSIGNED" can be used when user has no preference.
 *  GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/image1.png".
 *  LABEL = A display name of an object on an image, video etc., e.g. "dog".
 *          Must be up to 32 characters long and can consist only of ASCII
 *          Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
 *          For each label an AnnotationSpec is created which display_name
 *          becomes the label; AnnotationSpecs are given back in predictions.
 *  INSTANCE_ID = A positive integer that identifies a specific instance of a
 *                labeled entity on an example. Used e.g. to track two cars on
 *                a video while being able to tell apart which one is which.
 *  BOUNDING_BOX = VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,
 *                 A rectangle parallel to the frame of the example (image,
 *                 video). If 4 vertices are given they are connected by edges
 *                 in the order provided, if 2 are given they are recognized
 *                 as diagonally opposite vertices of the rectangle.
 *  VERTEX = COORDINATE,COORDINATE
 *           First coordinate is horizontal (x), the second is vertical (y).
 *  COORDINATE = A float in 0 to 1 range, relative to total length of
 *               image or video in given dimension. For fractions the
 *               leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
 *               Point 0,0 is in top left.
 *  TIME_SEGMENT_START = TIME_OFFSET
 *                       Expresses a beginning, inclusive, of a time segment
 *                       within an example that has a time dimension
 *                       (e.g. video).
 *  TIME_SEGMENT_END = TIME_OFFSET
 *                     Expresses an end, exclusive, of a time segment within
 *                     an example that has a time dimension (e.g. video).
 *  TIME_OFFSET = A number of seconds as measured from the start of an
 *                example (e.g. video). Fractions are allowed, up to a
 *                microsecond precision. "inf" is allowed, and it means the end
 *                of the example.
 *  TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
 *                 double quotes ("").
 *  SENTIMENT = An integer between 0 and
 *              Dataset.text_sentiment_dataset_metadata.sentiment_max
 *              (inclusive). Describes the ordinal of the sentiment - higher
 *              value means a more positive sentiment. All the values are
 *              completely relative, i.e. neither 0 needs to mean a negative or
 *              neutral sentiment nor sentiment_max needs to mean a positive one
 *              - it is just required that 0 is the least positive sentiment
 *              in the data, and sentiment_max is the  most positive one.
 *              The SENTIMENT shouldn't be confused with "score" or "magnitude"
 *              from the previous Natural Language Sentiment Analysis API.
 *              All SENTIMENT values between 0 and sentiment_max must be
 *              represented in the imported data. On prediction the same 0 to
 *              sentiment_max range will be used. The difference between
 *              neighboring sentiment values needs not to be uniform, e.g. 1 and
 *              2 may be similar whereas the difference between 2 and 3 may be
 *              huge.
 *
 *  Errors:
 *  If any of the provided CSV files can't be parsed or if more than certain
 *  percent of CSV rows cannot be processed then the operation fails and
 *  nothing is imported. Regardless of overall success or failure the per-row
 *  failures, up to a certain count cap, is listed in
 *  Operation.metadata.partial_failures.
 * </pre>
 *
 * Protobuf type {@code google.cloud.automl.v1beta1.InputConfig}
 */
public final class InputConfig extends com.google.protobuf.GeneratedMessageV3
    implements
    // @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.InputConfig)
    InputConfigOrBuilder {
  private static final long serialVersionUID = 0L;
  // Use InputConfig.newBuilder() to construct.
  private InputConfig(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) {
    super(builder);
  }

  private InputConfig() {}

  @java.lang.Override
  @SuppressWarnings({"unused"})
  protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
    return new InputConfig();
  }

  public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
    return com.google.cloud.automl.v1beta1.Io
        .internal_static_google_cloud_automl_v1beta1_InputConfig_descriptor;
  }

  @SuppressWarnings({"rawtypes"})
  @java.lang.Override
  protected com.google.protobuf.MapField internalGetMapField(int number) {
    switch (number) {
      case 2:
        return internalGetParams();
      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.Io
        .internal_static_google_cloud_automl_v1beta1_InputConfig_fieldAccessorTable
        .ensureFieldAccessorsInitialized(
            com.google.cloud.automl.v1beta1.InputConfig.class,
            com.google.cloud.automl.v1beta1.InputConfig.Builder.class);
  }

  private int sourceCase_ = 0;

  @SuppressWarnings("serial")
  private java.lang.Object source_;

  public enum SourceCase
      implements
          com.google.protobuf.Internal.EnumLite,
          com.google.protobuf.AbstractMessage.InternalOneOfEnum {
    GCS_SOURCE(1),
    BIGQUERY_SOURCE(3),
    SOURCE_NOT_SET(0);
    private final int value;

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

    public static SourceCase forNumber(int value) {
      switch (value) {
        case 1:
          return GCS_SOURCE;
        case 3:
          return BIGQUERY_SOURCE;
        case 0:
          return SOURCE_NOT_SET;
        default:
          return null;
      }
    }

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

  public SourceCase getSourceCase() {
    return SourceCase.forNumber(sourceCase_);
  }

  public static final int GCS_SOURCE_FIELD_NUMBER = 1;
  /**
   *
   *
   * <pre>
   * The Google Cloud Storage location for the input content.
   * In ImportData, the gcs_source points to a csv with structure described in
   * the comment.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
   *
   * @return Whether the gcsSource field is set.
   */
  @java.lang.Override
  public boolean hasGcsSource() {
    return sourceCase_ == 1;
  }
  /**
   *
   *
   * <pre>
   * The Google Cloud Storage location for the input content.
   * In ImportData, the gcs_source points to a csv with structure described in
   * the comment.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
   *
   * @return The gcsSource.
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.GcsSource getGcsSource() {
    if (sourceCase_ == 1) {
      return (com.google.cloud.automl.v1beta1.GcsSource) source_;
    }
    return com.google.cloud.automl.v1beta1.GcsSource.getDefaultInstance();
  }
  /**
   *
   *
   * <pre>
   * The Google Cloud Storage location for the input content.
   * In ImportData, the gcs_source points to a csv with structure described in
   * the comment.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.GcsSourceOrBuilder getGcsSourceOrBuilder() {
    if (sourceCase_ == 1) {
      return (com.google.cloud.automl.v1beta1.GcsSource) source_;
    }
    return com.google.cloud.automl.v1beta1.GcsSource.getDefaultInstance();
  }

  public static final int BIGQUERY_SOURCE_FIELD_NUMBER = 3;
  /**
   *
   *
   * <pre>
   * The BigQuery location for the input content.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
   *
   * @return Whether the bigquerySource field is set.
   */
  @java.lang.Override
  public boolean hasBigquerySource() {
    return sourceCase_ == 3;
  }
  /**
   *
   *
   * <pre>
   * The BigQuery location for the input content.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
   *
   * @return The bigquerySource.
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.BigQuerySource getBigquerySource() {
    if (sourceCase_ == 3) {
      return (com.google.cloud.automl.v1beta1.BigQuerySource) source_;
    }
    return com.google.cloud.automl.v1beta1.BigQuerySource.getDefaultInstance();
  }
  /**
   *
   *
   * <pre>
   * The BigQuery location for the input content.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.BigQuerySourceOrBuilder getBigquerySourceOrBuilder() {
    if (sourceCase_ == 3) {
      return (com.google.cloud.automl.v1beta1.BigQuerySource) source_;
    }
    return com.google.cloud.automl.v1beta1.BigQuerySource.getDefaultInstance();
  }

  public static final int PARAMS_FIELD_NUMBER = 2;

  private static final class ParamsDefaultEntryHolder {
    static final com.google.protobuf.MapEntry<java.lang.String, java.lang.String> defaultEntry =
        com.google.protobuf.MapEntry.<java.lang.String, java.lang.String>newDefaultInstance(
            com.google.cloud.automl.v1beta1.Io
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            "",
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  private com.google.protobuf.MapField<java.lang.String, java.lang.String> internalGetParams() {
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    }
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  }

  public int getParamsCount() {
    return internalGetParams().getMap().size();
  }
  /**
   *
   *
   * <pre>
   * Additional domain-specific parameters describing the semantic of the
   * imported data, any string must be up to 25000
   * characters long.
   *
   * *  For Tables:
   *    `schema_inference_version` - (integer) Required. The version of the
   *        algorithm that should be used for the initial inference of the
   *        schema (columns' DataTypes) of the table the data is being imported
   *        into. Allowed values: "1".
   * </pre>
   *
   * <code>map&lt;string, string&gt; params = 2;</code>
   */
  @java.lang.Override
  public boolean containsParams(java.lang.String key) {
    if (key == null) {
      throw new NullPointerException("map key");
    }
    return internalGetParams().getMap().containsKey(key);
  }
  /** Use {@link #getParamsMap()} instead. */
  @java.lang.Override
  @java.lang.Deprecated
  public java.util.Map<java.lang.String, java.lang.String> getParams() {
    return getParamsMap();
  }
  /**
   *
   *
   * <pre>
   * Additional domain-specific parameters describing the semantic of the
   * imported data, any string must be up to 25000
   * characters long.
   *
   * *  For Tables:
   *    `schema_inference_version` - (integer) Required. The version of the
   *        algorithm that should be used for the initial inference of the
   *        schema (columns' DataTypes) of the table the data is being imported
   *        into. Allowed values: "1".
   * </pre>
   *
   * <code>map&lt;string, string&gt; params = 2;</code>
   */
  @java.lang.Override
  public java.util.Map<java.lang.String, java.lang.String> getParamsMap() {
    return internalGetParams().getMap();
  }
  /**
   *
   *
   * <pre>
   * Additional domain-specific parameters describing the semantic of the
   * imported data, any string must be up to 25000
   * characters long.
   *
   * *  For Tables:
   *    `schema_inference_version` - (integer) Required. The version of the
   *        algorithm that should be used for the initial inference of the
   *        schema (columns' DataTypes) of the table the data is being imported
   *        into. Allowed values: "1".
   * </pre>
   *
   * <code>map&lt;string, string&gt; params = 2;</code>
   */
  @java.lang.Override
  public /* nullable */ java.lang.String getParamsOrDefault(
      java.lang.String key,
      /* nullable */
      java.lang.String defaultValue) {
    if (key == null) {
      throw new NullPointerException("map key");
    }
    java.util.Map<java.lang.String, java.lang.String> map = internalGetParams().getMap();
    return map.containsKey(key) ? map.get(key) : defaultValue;
  }
  /**
   *
   *
   * <pre>
   * Additional domain-specific parameters describing the semantic of the
   * imported data, any string must be up to 25000
   * characters long.
   *
   * *  For Tables:
   *    `schema_inference_version` - (integer) Required. The version of the
   *        algorithm that should be used for the initial inference of the
   *        schema (columns' DataTypes) of the table the data is being imported
   *        into. Allowed values: "1".
   * </pre>
   *
   * <code>map&lt;string, string&gt; params = 2;</code>
   */
  @java.lang.Override
  public java.lang.String getParamsOrThrow(java.lang.String key) {
    if (key == null) {
      throw new NullPointerException("map key");
    }
    java.util.Map<java.lang.String, java.lang.String> map = internalGetParams().getMap();
    if (!map.containsKey(key)) {
      throw new java.lang.IllegalArgumentException();
    }
    return map.get(key);
  }

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      output.writeMessage(1, (com.google.cloud.automl.v1beta1.GcsSource) source_);
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        output, internalGetParams(), ParamsDefaultEntryHolder.defaultEntry, 2);
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    if (!internalGetParams().equals(other.internalGetParams())) return false;
    if (!getSourceCase().equals(other.getSourceCase())) return false;
    switch (sourceCase_) {
      case 1:
        if (!getGcsSource().equals(other.getGcsSource())) return false;
        break;
      case 3:
        if (!getBigquerySource().equals(other.getBigquerySource())) return false;
        break;
      case 0:
      default:
    }
    if (!getUnknownFields().equals(other.getUnknownFields())) return false;
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  @java.lang.Override
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      hash = (53 * hash) + internalGetParams().hashCode();
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      case 1:
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        hash = (53 * hash) + getGcsSource().hashCode();
        break;
      case 3:
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        break;
      case 0:
      default:
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  public static com.google.cloud.automl.v1beta1.InputConfig parseFrom(java.nio.ByteBuffer data)
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  public static com.google.cloud.automl.v1beta1.InputConfig parseFrom(
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  public static com.google.cloud.automl.v1beta1.InputConfig parseFrom(
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  public static com.google.cloud.automl.v1beta1.InputConfig parseFrom(
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  /**
   *
   *
   * <pre>
   * Input configuration for ImportData Action.
   *
   * The format of input depends on dataset_metadata the Dataset into which
   * the import is happening has. As input source the
   * [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source]
   * is expected, unless specified otherwise. Additionally any input .CSV file
   * by itself must be 100MB or smaller, unless specified otherwise.
   * If an "example" file (that is, image, video etc.) with identical content
   * (even if it had different GCS_FILE_PATH) is mentioned multiple times, then
   * its label, bounding boxes etc. are appended. The same file should be always
   * provided with the same ML_USE and GCS_FILE_PATH, if it is not, then
   * these values are nondeterministically selected from the given ones.
   *
   * The formats are represented in EBNF with commas being literal and with
   * non-terminal symbols defined near the end of this comment. The formats are:
   *
   *  *  For Image Classification:
   *         CSV file(s) with each line in format:
   *           ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
   *           GCS_FILE_PATH leads to image of up to 30MB in size. Supported
   *           extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO
   *           For MULTICLASS classification type, at most one LABEL is allowed
   *           per image. If an image has not yet been labeled, then it should be
   *           mentioned just once with no LABEL.
   *         Some sample rows:
   *           TRAIN,gs://folder/image1.jpg,daisy
   *           TEST,gs://folder/image2.jpg,dandelion,tulip,rose
   *           UNASSIGNED,gs://folder/image3.jpg,daisy
   *           UNASSIGNED,gs://folder/image4.jpg
   *
   *  *  For Image Object Detection:
   *         CSV file(s) with each line in format:
   *           ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,)
   *           GCS_FILE_PATH leads to image of up to 30MB in size. Supported
   *           extensions: .JPEG, .GIF, .PNG.
   *           Each image is assumed to be exhaustively labeled. The minimum
   *           allowed BOUNDING_BOX edge length is 0.01, and no more than 500
   *           BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined
   *           per line). If an image has not yet been labeled, then it should be
   *           mentioned just once with no LABEL and the ",,,,,,," in place of the
   *           BOUNDING_BOX. For images which are known to not contain any
   *           bounding boxes, they should be labelled explictly as
   *           "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the
   *           BOUNDING_BOX.
   *         Sample rows:
   *           TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,,
   *           TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,,
   *           UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3
   *           TEST,gs://folder/im3.png,,,,,,,,,
   *           TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,
   *
   *  *  For Video Classification:
   *         CSV file(s) with each line in format:
   *           ML_USE,GCS_FILE_PATH
   *           where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH
   *           should lead to another .csv file which describes examples that have
   *           given ML_USE, using the following row format:
   *           GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,)
   *           Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
   *           to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
   *           TIME_SEGMENT_START and TIME_SEGMENT_END must be within the
   *           length of the video, and end has to be after the start. Any segment
   *           of a video which has one or more labels on it, is considered a
   *           hard negative for all other labels. Any segment with no labels on
   *           it is considered to be unknown. If a whole video is unknown, then
   *           it shuold be mentioned just once with ",," in place of LABEL,
   *           TIME_SEGMENT_START,TIME_SEGMENT_END.
   *         Sample top level CSV file:
   *           TRAIN,gs://folder/train_videos.csv
   *           TEST,gs://folder/test_videos.csv
   *           UNASSIGNED,gs://folder/other_videos.csv
   *         Sample rows of a CSV file for a particular ML_USE:
   *           gs://folder/video1.avi,car,120,180.000021
   *           gs://folder/video1.avi,bike,150,180.000021
   *           gs://folder/vid2.avi,car,0,60.5
   *           gs://folder/vid3.avi,,,
   *
   *  *  For Video Object Tracking:
   *         CSV file(s) with each line in format:
   *           ML_USE,GCS_FILE_PATH
   *           where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH
   *           should lead to another .csv file which describes examples that have
   *           given ML_USE, using one of the following row format:
   *           GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX
   *           or
   *           GCS_FILE_PATH,,,,,,,,,,
   *           Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
   *           to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
   *           Providing INSTANCE_IDs can help to obtain a better model. When
   *           a specific labeled entity leaves the video frame, and shows up
   *           afterwards it is not required, albeit preferable, that the same
   *           INSTANCE_ID is given to it.
   *           TIMESTAMP must be within the length of the video, the
   *           BOUNDING_BOX is assumed to be drawn on the closest video's frame
   *           to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected
   *           to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per
   *           frame are allowed. If a whole video is unknown, then it should be
   *           mentioned just once with ",,,,,,,,,," in place of LABEL,
   *           [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX.
   *         Sample top level CSV file:
   *           TRAIN,gs://folder/train_videos.csv
   *           TEST,gs://folder/test_videos.csv
   *           UNASSIGNED,gs://folder/other_videos.csv
   *         Seven sample rows of a CSV file for a particular ML_USE:
   *           gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
   *           gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
   *           gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
   *           gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
   *           gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,,
   *           gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,,
   *           gs://folder/video2.avi,,,,,,,,,,,
   *  *  For Text Extraction:
   *         CSV file(s) with each line in format:
   *           ML_USE,GCS_FILE_PATH
   *           GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which
   *           either imports text in-line or as documents. Any given
   *           .JSONL file must be 100MB or smaller.
   *           The in-line .JSONL file contains, per line, a proto that wraps a
   *           TextSnippet proto (in json representation) followed by one or more
   *           AnnotationPayload protos (called annotations), which have
   *           display_name and text_extraction detail populated. The given text
   *           is expected to be annotated exhaustively, for example, if you look
   *           for animals and text contains "dolphin" that is not labeled, then
   *           "dolphin" is assumed to not be an animal. Any given text snippet
   *           content must be 10KB or smaller, and also be UTF-8 NFC encoded
   *           (ASCII already is).
   *           The document .JSONL file contains, per line, a proto that wraps a
   *           Document proto. The Document proto must have either document_text
   *           or input_config set. In document_text case, the Document proto may
   *           also contain the spatial information of the document, including
   *           layout, document dimension and page number. In input_config case,
   *           only PDF documents are supported now, and each document may be up
   *           to 2MB large. Currently, annotations on documents cannot be
   *           specified at import.
   *         Three sample CSV rows:
   *           TRAIN,gs://folder/file1.jsonl
   *           VALIDATE,gs://folder/file2.jsonl
   *           TEST,gs://folder/file3.jsonl
   *         Sample in-line JSON Lines file for entity extraction (presented here
   *         with artificial line breaks, but the only actual line break is
   *         denoted by &#92;n).:
   *           {
   *             "document": {
   *               "document_text": {"content": "dog cat"}
   *               "layout": [
   *                 {
   *                   "text_segment": {
   *                     "start_offset": 0,
   *                     "end_offset": 3,
   *                   },
   *                   "page_number": 1,
   *                   "bounding_poly": {
   *                     "normalized_vertices": [
   *                       {"x": 0.1, "y": 0.1},
   *                       {"x": 0.1, "y": 0.3},
   *                       {"x": 0.3, "y": 0.3},
   *                       {"x": 0.3, "y": 0.1},
   *                     ],
   *                   },
   *                   "text_segment_type": TOKEN,
   *                 },
   *                 {
   *                   "text_segment": {
   *                     "start_offset": 4,
   *                     "end_offset": 7,
   *                   },
   *                   "page_number": 1,
   *                   "bounding_poly": {
   *                     "normalized_vertices": [
   *                       {"x": 0.4, "y": 0.1},
   *                       {"x": 0.4, "y": 0.3},
   *                       {"x": 0.8, "y": 0.3},
   *                       {"x": 0.8, "y": 0.1},
   *                     ],
   *                   },
   *                   "text_segment_type": TOKEN,
   *                 }
   *
   *               ],
   *               "document_dimensions": {
   *                 "width": 8.27,
   *                 "height": 11.69,
   *                 "unit": INCH,
   *               }
   *               "page_count": 1,
   *             },
   *             "annotations": [
   *               {
   *                 "display_name": "animal",
   *                 "text_extraction": {"text_segment": {"start_offset": 0,
   *                 "end_offset": 3}}
   *               },
   *               {
   *                 "display_name": "animal",
   *                 "text_extraction": {"text_segment": {"start_offset": 4,
   *                 "end_offset": 7}}
   *               }
   *             ],
   *           }&#92;n
   *           {
   *              "text_snippet": {
   *                "content": "This dog is good."
   *              },
   *              "annotations": [
   *                {
   *                  "display_name": "animal",
   *                  "text_extraction": {
   *                    "text_segment": {"start_offset": 5, "end_offset": 8}
   *                  }
   *                }
   *              ]
   *           }
   *         Sample document JSON Lines file (presented here with artificial line
   *         breaks, but the only actual line break is denoted by &#92;n).:
   *           {
   *             "document": {
   *               "input_config": {
   *                 "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
   *                 }
   *               }
   *             }
   *           }&#92;n
   *           {
   *             "document": {
   *               "input_config": {
   *                 "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
   *                 }
   *               }
   *             }
   *           }
   *
   *  *  For Text Classification:
   *         CSV file(s) with each line in format:
   *           ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...
   *           TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
   *           the column content is a valid gcs file path, i.e. prefixed by
   *           "gs://", it will be treated as a GCS_FILE_PATH, else if the content
   *           is enclosed within double quotes (""), it is
   *           treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
   *           must lead to a .txt file with UTF-8 encoding, for example,
   *           "gs://folder/content.txt", and the content in it is extracted
   *           as a text snippet. In TEXT_SNIPPET case, the column content
   *           excluding quotes is treated as to be imported text snippet. In
   *           both cases, the text snippet/file size must be within 128kB.
   *           Maximum 100 unique labels are allowed per CSV row.
   *         Sample rows:
   *           TRAIN,"They have bad food and very rude",RudeService,BadFood
   *           TRAIN,gs://folder/content.txt,SlowService
   *           TEST,"Typically always bad service there.",RudeService
   *           VALIDATE,"Stomach ache to go.",BadFood
   *
   *  *  For Text Sentiment:
   *         CSV file(s) with each line in format:
   *           ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT
   *           TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
   *           the column content is a valid gcs file path, that is, prefixed by
   *           "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated
   *           as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
   *           must lead to a .txt file with UTF-8 encoding, for example,
   *           "gs://folder/content.txt", and the content in it is extracted
   *           as a text snippet. In TEXT_SNIPPET case, the column content itself
   *           is treated as to be imported text snippet. In both cases, the
   *           text snippet must be up to 500 characters long.
   *         Sample rows:
   *           TRAIN,"&#64;freewrytin this is way too good for your product",2
   *           TRAIN,"I need this product so bad",3
   *           TEST,"Thank you for this product.",4
   *           VALIDATE,gs://folder/content.txt,2
   *
   *   *  For Tables:
   *         Either
   *         [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or
   *
   * [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]
   *         can be used. All inputs is concatenated into a single
   *
   * [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name]
   *         For gcs_source:
   *           CSV file(s), where the first row of the first file is the header,
   *           containing unique column names. If the first row of a subsequent
   *           file is the same as the header, then it is also treated as a
   *           header. All other rows contain values for the corresponding
   *           columns.
   *           Each .CSV file by itself must be 10GB or smaller, and their total
   *           size must be 100GB or smaller.
   *           First three sample rows of a CSV file:
   *           "Id","First Name","Last Name","Dob","Addresses"
   *
   * "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
   *
   * "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
   *         For bigquery_source:
   *           An URI of a BigQuery table. The user data size of the BigQuery
   *           table must be 100GB or smaller.
   *         An imported table must have between 2 and 1,000 columns, inclusive,
   *         and between 1000 and 100,000,000 rows, inclusive. There are at most 5
   *         import data running in parallel.
   *  Definitions:
   *  ML_USE = "TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED"
   *           Describes how the given example (file) should be used for model
   *           training. "UNASSIGNED" can be used when user has no preference.
   *  GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/image1.png".
   *  LABEL = A display name of an object on an image, video etc., e.g. "dog".
   *          Must be up to 32 characters long and can consist only of ASCII
   *          Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
   *          For each label an AnnotationSpec is created which display_name
   *          becomes the label; AnnotationSpecs are given back in predictions.
   *  INSTANCE_ID = A positive integer that identifies a specific instance of a
   *                labeled entity on an example. Used e.g. to track two cars on
   *                a video while being able to tell apart which one is which.
   *  BOUNDING_BOX = VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,
   *                 A rectangle parallel to the frame of the example (image,
   *                 video). If 4 vertices are given they are connected by edges
   *                 in the order provided, if 2 are given they are recognized
   *                 as diagonally opposite vertices of the rectangle.
   *  VERTEX = COORDINATE,COORDINATE
   *           First coordinate is horizontal (x), the second is vertical (y).
   *  COORDINATE = A float in 0 to 1 range, relative to total length of
   *               image or video in given dimension. For fractions the
   *               leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
   *               Point 0,0 is in top left.
   *  TIME_SEGMENT_START = TIME_OFFSET
   *                       Expresses a beginning, inclusive, of a time segment
   *                       within an example that has a time dimension
   *                       (e.g. video).
   *  TIME_SEGMENT_END = TIME_OFFSET
   *                     Expresses an end, exclusive, of a time segment within
   *                     an example that has a time dimension (e.g. video).
   *  TIME_OFFSET = A number of seconds as measured from the start of an
   *                example (e.g. video). Fractions are allowed, up to a
   *                microsecond precision. "inf" is allowed, and it means the end
   *                of the example.
   *  TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
   *                 double quotes ("").
   *  SENTIMENT = An integer between 0 and
   *              Dataset.text_sentiment_dataset_metadata.sentiment_max
   *              (inclusive). Describes the ordinal of the sentiment - higher
   *              value means a more positive sentiment. All the values are
   *              completely relative, i.e. neither 0 needs to mean a negative or
   *              neutral sentiment nor sentiment_max needs to mean a positive one
   *              - it is just required that 0 is the least positive sentiment
   *              in the data, and sentiment_max is the  most positive one.
   *              The SENTIMENT shouldn't be confused with "score" or "magnitude"
   *              from the previous Natural Language Sentiment Analysis API.
   *              All SENTIMENT values between 0 and sentiment_max must be
   *              represented in the imported data. On prediction the same 0 to
   *              sentiment_max range will be used. The difference between
   *              neighboring sentiment values needs not to be uniform, e.g. 1 and
   *              2 may be similar whereas the difference between 2 and 3 may be
   *              huge.
   *
   *  Errors:
   *  If any of the provided CSV files can't be parsed or if more than certain
   *  percent of CSV rows cannot be processed then the operation fails and
   *  nothing is imported. Regardless of overall success or failure the per-row
   *  failures, up to a certain count cap, is listed in
   *  Operation.metadata.partial_failures.
   * </pre>
   *
   * Protobuf type {@code google.cloud.automl.v1beta1.InputConfig}
   */
  public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder<Builder>
      implements
      // @@protoc_insertion_point(builder_implements:google.cloud.automl.v1beta1.InputConfig)
      com.google.cloud.automl.v1beta1.InputConfigOrBuilder {
    public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
      return com.google.cloud.automl.v1beta1.Io
          .internal_static_google_cloud_automl_v1beta1_InputConfig_descriptor;
    }

    @SuppressWarnings({"rawtypes"})
    protected com.google.protobuf.MapField internalGetMapField(int number) {
      switch (number) {
        case 2:
          return internalGetParams();
        default:
          throw new RuntimeException("Invalid map field number: " + number);
      }
    }

    @SuppressWarnings({"rawtypes"})
    protected com.google.protobuf.MapField internalGetMutableMapField(int number) {
      switch (number) {
        case 2:
          return internalGetMutableParams();
        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.Io
          .internal_static_google_cloud_automl_v1beta1_InputConfig_fieldAccessorTable
          .ensureFieldAccessorsInitialized(
              com.google.cloud.automl.v1beta1.InputConfig.class,
              com.google.cloud.automl.v1beta1.InputConfig.Builder.class);
    }

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

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

    @java.lang.Override
    public Builder clear() {
      super.clear();
      bitField0_ = 0;
      if (gcsSourceBuilder_ != null) {
        gcsSourceBuilder_.clear();
      }
      if (bigquerySourceBuilder_ != null) {
        bigquerySourceBuilder_.clear();
      }
      internalGetMutableParams().clear();
      sourceCase_ = 0;
      source_ = null;
      return this;
    }

    @java.lang.Override
    public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
      return com.google.cloud.automl.v1beta1.Io
          .internal_static_google_cloud_automl_v1beta1_InputConfig_descriptor;
    }

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

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

    @java.lang.Override
    public com.google.cloud.automl.v1beta1.InputConfig buildPartial() {
      com.google.cloud.automl.v1beta1.InputConfig result =
          new com.google.cloud.automl.v1beta1.InputConfig(this);
      if (bitField0_ != 0) {
        buildPartial0(result);
      }
      buildPartialOneofs(result);
      onBuilt();
      return result;
    }

    private void buildPartial0(com.google.cloud.automl.v1beta1.InputConfig result) {
      int from_bitField0_ = bitField0_;
      if (((from_bitField0_ & 0x00000004) != 0)) {
        result.params_ = internalGetParams();
        result.params_.makeImmutable();
      }
    }

    private void buildPartialOneofs(com.google.cloud.automl.v1beta1.InputConfig result) {
      result.sourceCase_ = sourceCase_;
      result.source_ = this.source_;
      if (sourceCase_ == 1 && gcsSourceBuilder_ != null) {
        result.source_ = gcsSourceBuilder_.build();
      }
      if (sourceCase_ == 3 && bigquerySourceBuilder_ != null) {
        result.source_ = bigquerySourceBuilder_.build();
      }
    }

    @java.lang.Override
    public Builder clone() {
      return super.clone();
    }

    @java.lang.Override
    public Builder setField(
        com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
      return super.setField(field, value);
    }

    @java.lang.Override
    public Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field) {
      return super.clearField(field);
    }

    @java.lang.Override
    public Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) {
      return super.clearOneof(oneof);
    }

    @java.lang.Override
    public Builder setRepeatedField(
        com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) {
      return super.setRepeatedField(field, index, value);
    }

    @java.lang.Override
    public Builder addRepeatedField(
        com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
      return super.addRepeatedField(field, value);
    }

    @java.lang.Override
    public Builder mergeFrom(com.google.protobuf.Message other) {
      if (other instanceof com.google.cloud.automl.v1beta1.InputConfig) {
        return mergeFrom((com.google.cloud.automl.v1beta1.InputConfig) other);
      } else {
        super.mergeFrom(other);
        return this;
      }
    }

    public Builder mergeFrom(com.google.cloud.automl.v1beta1.InputConfig other) {
      if (other == com.google.cloud.automl.v1beta1.InputConfig.getDefaultInstance()) return this;
      internalGetMutableParams().mergeFrom(other.internalGetParams());
      bitField0_ |= 0x00000004;
      switch (other.getSourceCase()) {
        case GCS_SOURCE:
          {
            mergeGcsSource(other.getGcsSource());
            break;
          }
        case BIGQUERY_SOURCE:
          {
            mergeBigquerySource(other.getBigquerySource());
            break;
          }
        case SOURCE_NOT_SET:
          {
            break;
          }
      }
      this.mergeUnknownFields(other.getUnknownFields());
      onChanged();
      return this;
    }

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

    @java.lang.Override
    public Builder mergeFrom(
        com.google.protobuf.CodedInputStream input,
        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
        throws java.io.IOException {
      if (extensionRegistry == null) {
        throw new java.lang.NullPointerException();
      }
      try {
        boolean done = false;
        while (!done) {
          int tag = input.readTag();
          switch (tag) {
            case 0:
              done = true;
              break;
            case 10:
              {
                input.readMessage(getGcsSourceFieldBuilder().getBuilder(), extensionRegistry);
                sourceCase_ = 1;
                break;
              } // case 10
            case 18:
              {
                com.google.protobuf.MapEntry<java.lang.String, java.lang.String> params__ =
                    input.readMessage(
                        ParamsDefaultEntryHolder.defaultEntry.getParserForType(),
                        extensionRegistry);
                internalGetMutableParams()
                    .getMutableMap()
                    .put(params__.getKey(), params__.getValue());
                bitField0_ |= 0x00000004;
                break;
              } // case 18
            case 26:
              {
                input.readMessage(getBigquerySourceFieldBuilder().getBuilder(), extensionRegistry);
                sourceCase_ = 3;
                break;
              } // case 26
            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 sourceCase_ = 0;
    private java.lang.Object source_;

    public SourceCase getSourceCase() {
      return SourceCase.forNumber(sourceCase_);
    }

    public Builder clearSource() {
      sourceCase_ = 0;
      source_ = null;
      onChanged();
      return this;
    }

    private int bitField0_;

    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.GcsSource,
            com.google.cloud.automl.v1beta1.GcsSource.Builder,
            com.google.cloud.automl.v1beta1.GcsSourceOrBuilder>
        gcsSourceBuilder_;
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     *
     * @return Whether the gcsSource field is set.
     */
    @java.lang.Override
    public boolean hasGcsSource() {
      return sourceCase_ == 1;
    }
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     *
     * @return The gcsSource.
     */
    @java.lang.Override
    public com.google.cloud.automl.v1beta1.GcsSource getGcsSource() {
      if (gcsSourceBuilder_ == null) {
        if (sourceCase_ == 1) {
          return (com.google.cloud.automl.v1beta1.GcsSource) source_;
        }
        return com.google.cloud.automl.v1beta1.GcsSource.getDefaultInstance();
      } else {
        if (sourceCase_ == 1) {
          return gcsSourceBuilder_.getMessage();
        }
        return com.google.cloud.automl.v1beta1.GcsSource.getDefaultInstance();
      }
    }
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     */
    public Builder setGcsSource(com.google.cloud.automl.v1beta1.GcsSource value) {
      if (gcsSourceBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        source_ = value;
        onChanged();
      } else {
        gcsSourceBuilder_.setMessage(value);
      }
      sourceCase_ = 1;
      return this;
    }
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     */
    public Builder setGcsSource(com.google.cloud.automl.v1beta1.GcsSource.Builder builderForValue) {
      if (gcsSourceBuilder_ == null) {
        source_ = builderForValue.build();
        onChanged();
      } else {
        gcsSourceBuilder_.setMessage(builderForValue.build());
      }
      sourceCase_ = 1;
      return this;
    }
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     */
    public Builder mergeGcsSource(com.google.cloud.automl.v1beta1.GcsSource value) {
      if (gcsSourceBuilder_ == null) {
        if (sourceCase_ == 1
            && source_ != com.google.cloud.automl.v1beta1.GcsSource.getDefaultInstance()) {
          source_ =
              com.google.cloud.automl.v1beta1.GcsSource.newBuilder(
                      (com.google.cloud.automl.v1beta1.GcsSource) source_)
                  .mergeFrom(value)
                  .buildPartial();
        } else {
          source_ = value;
        }
        onChanged();
      } else {
        if (sourceCase_ == 1) {
          gcsSourceBuilder_.mergeFrom(value);
        } else {
          gcsSourceBuilder_.setMessage(value);
        }
      }
      sourceCase_ = 1;
      return this;
    }
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     */
    public Builder clearGcsSource() {
      if (gcsSourceBuilder_ == null) {
        if (sourceCase_ == 1) {
          sourceCase_ = 0;
          source_ = null;
          onChanged();
        }
      } else {
        if (sourceCase_ == 1) {
          sourceCase_ = 0;
          source_ = null;
        }
        gcsSourceBuilder_.clear();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     */
    public com.google.cloud.automl.v1beta1.GcsSource.Builder getGcsSourceBuilder() {
      return getGcsSourceFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     */
    @java.lang.Override
    public com.google.cloud.automl.v1beta1.GcsSourceOrBuilder getGcsSourceOrBuilder() {
      if ((sourceCase_ == 1) && (gcsSourceBuilder_ != null)) {
        return gcsSourceBuilder_.getMessageOrBuilder();
      } else {
        if (sourceCase_ == 1) {
          return (com.google.cloud.automl.v1beta1.GcsSource) source_;
        }
        return com.google.cloud.automl.v1beta1.GcsSource.getDefaultInstance();
      }
    }
    /**
     *
     *
     * <pre>
     * The Google Cloud Storage location for the input content.
     * In ImportData, the gcs_source points to a csv with structure described in
     * the comment.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.GcsSource gcs_source = 1;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.GcsSource,
            com.google.cloud.automl.v1beta1.GcsSource.Builder,
            com.google.cloud.automl.v1beta1.GcsSourceOrBuilder>
        getGcsSourceFieldBuilder() {
      if (gcsSourceBuilder_ == null) {
        if (!(sourceCase_ == 1)) {
          source_ = com.google.cloud.automl.v1beta1.GcsSource.getDefaultInstance();
        }
        gcsSourceBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.cloud.automl.v1beta1.GcsSource,
                com.google.cloud.automl.v1beta1.GcsSource.Builder,
                com.google.cloud.automl.v1beta1.GcsSourceOrBuilder>(
                (com.google.cloud.automl.v1beta1.GcsSource) source_,
                getParentForChildren(),
                isClean());
        source_ = null;
      }
      sourceCase_ = 1;
      onChanged();
      return gcsSourceBuilder_;
    }

    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.BigQuerySource,
            com.google.cloud.automl.v1beta1.BigQuerySource.Builder,
            com.google.cloud.automl.v1beta1.BigQuerySourceOrBuilder>
        bigquerySourceBuilder_;
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     *
     * @return Whether the bigquerySource field is set.
     */
    @java.lang.Override
    public boolean hasBigquerySource() {
      return sourceCase_ == 3;
    }
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     *
     * @return The bigquerySource.
     */
    @java.lang.Override
    public com.google.cloud.automl.v1beta1.BigQuerySource getBigquerySource() {
      if (bigquerySourceBuilder_ == null) {
        if (sourceCase_ == 3) {
          return (com.google.cloud.automl.v1beta1.BigQuerySource) source_;
        }
        return com.google.cloud.automl.v1beta1.BigQuerySource.getDefaultInstance();
      } else {
        if (sourceCase_ == 3) {
          return bigquerySourceBuilder_.getMessage();
        }
        return com.google.cloud.automl.v1beta1.BigQuerySource.getDefaultInstance();
      }
    }
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     */
    public Builder setBigquerySource(com.google.cloud.automl.v1beta1.BigQuerySource value) {
      if (bigquerySourceBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        source_ = value;
        onChanged();
      } else {
        bigquerySourceBuilder_.setMessage(value);
      }
      sourceCase_ = 3;
      return this;
    }
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     */
    public Builder setBigquerySource(
        com.google.cloud.automl.v1beta1.BigQuerySource.Builder builderForValue) {
      if (bigquerySourceBuilder_ == null) {
        source_ = builderForValue.build();
        onChanged();
      } else {
        bigquerySourceBuilder_.setMessage(builderForValue.build());
      }
      sourceCase_ = 3;
      return this;
    }
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     */
    public Builder mergeBigquerySource(com.google.cloud.automl.v1beta1.BigQuerySource value) {
      if (bigquerySourceBuilder_ == null) {
        if (sourceCase_ == 3
            && source_ != com.google.cloud.automl.v1beta1.BigQuerySource.getDefaultInstance()) {
          source_ =
              com.google.cloud.automl.v1beta1.BigQuerySource.newBuilder(
                      (com.google.cloud.automl.v1beta1.BigQuerySource) source_)
                  .mergeFrom(value)
                  .buildPartial();
        } else {
          source_ = value;
        }
        onChanged();
      } else {
        if (sourceCase_ == 3) {
          bigquerySourceBuilder_.mergeFrom(value);
        } else {
          bigquerySourceBuilder_.setMessage(value);
        }
      }
      sourceCase_ = 3;
      return this;
    }
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     */
    public Builder clearBigquerySource() {
      if (bigquerySourceBuilder_ == null) {
        if (sourceCase_ == 3) {
          sourceCase_ = 0;
          source_ = null;
          onChanged();
        }
      } else {
        if (sourceCase_ == 3) {
          sourceCase_ = 0;
          source_ = null;
        }
        bigquerySourceBuilder_.clear();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     */
    public com.google.cloud.automl.v1beta1.BigQuerySource.Builder getBigquerySourceBuilder() {
      return getBigquerySourceFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     */
    @java.lang.Override
    public com.google.cloud.automl.v1beta1.BigQuerySourceOrBuilder getBigquerySourceOrBuilder() {
      if ((sourceCase_ == 3) && (bigquerySourceBuilder_ != null)) {
        return bigquerySourceBuilder_.getMessageOrBuilder();
      } else {
        if (sourceCase_ == 3) {
          return (com.google.cloud.automl.v1beta1.BigQuerySource) source_;
        }
        return com.google.cloud.automl.v1beta1.BigQuerySource.getDefaultInstance();
      }
    }
    /**
     *
     *
     * <pre>
     * The BigQuery location for the input content.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.BigQuerySource,
            com.google.cloud.automl.v1beta1.BigQuerySource.Builder,
            com.google.cloud.automl.v1beta1.BigQuerySourceOrBuilder>
        getBigquerySourceFieldBuilder() {
      if (bigquerySourceBuilder_ == null) {
        if (!(sourceCase_ == 3)) {
          source_ = com.google.cloud.automl.v1beta1.BigQuerySource.getDefaultInstance();
        }
        bigquerySourceBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.cloud.automl.v1beta1.BigQuerySource,
                com.google.cloud.automl.v1beta1.BigQuerySource.Builder,
                com.google.cloud.automl.v1beta1.BigQuerySourceOrBuilder>(
                (com.google.cloud.automl.v1beta1.BigQuerySource) source_,
                getParentForChildren(),
                isClean());
        source_ = null;
      }
      sourceCase_ = 3;
      onChanged();
      return bigquerySourceBuilder_;
    }

    private com.google.protobuf.MapField<java.lang.String, java.lang.String> params_;

    private com.google.protobuf.MapField<java.lang.String, java.lang.String> internalGetParams() {
      if (params_ == null) {
        return com.google.protobuf.MapField.emptyMapField(ParamsDefaultEntryHolder.defaultEntry);
      }
      return params_;
    }

    private com.google.protobuf.MapField<java.lang.String, java.lang.String>
        internalGetMutableParams() {
      if (params_ == null) {
        params_ = com.google.protobuf.MapField.newMapField(ParamsDefaultEntryHolder.defaultEntry);
      }
      if (!params_.isMutable()) {
        params_ = params_.copy();
      }
      bitField0_ |= 0x00000004;
      onChanged();
      return params_;
    }

    public int getParamsCount() {
      return internalGetParams().getMap().size();
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters describing the semantic of the
     * imported data, any string must be up to 25000
     * characters long.
     *
     * *  For Tables:
     *    `schema_inference_version` - (integer) Required. The version of the
     *        algorithm that should be used for the initial inference of the
     *        schema (columns' DataTypes) of the table the data is being imported
     *        into. Allowed values: "1".
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 2;</code>
     */
    @java.lang.Override
    public boolean containsParams(java.lang.String key) {
      if (key == null) {
        throw new NullPointerException("map key");
      }
      return internalGetParams().getMap().containsKey(key);
    }
    /** Use {@link #getParamsMap()} instead. */
    @java.lang.Override
    @java.lang.Deprecated
    public java.util.Map<java.lang.String, java.lang.String> getParams() {
      return getParamsMap();
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters describing the semantic of the
     * imported data, any string must be up to 25000
     * characters long.
     *
     * *  For Tables:
     *    `schema_inference_version` - (integer) Required. The version of the
     *        algorithm that should be used for the initial inference of the
     *        schema (columns' DataTypes) of the table the data is being imported
     *        into. Allowed values: "1".
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 2;</code>
     */
    @java.lang.Override
    public java.util.Map<java.lang.String, java.lang.String> getParamsMap() {
      return internalGetParams().getMap();
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters describing the semantic of the
     * imported data, any string must be up to 25000
     * characters long.
     *
     * *  For Tables:
     *    `schema_inference_version` - (integer) Required. The version of the
     *        algorithm that should be used for the initial inference of the
     *        schema (columns' DataTypes) of the table the data is being imported
     *        into. Allowed values: "1".
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 2;</code>
     */
    @java.lang.Override
    public /* nullable */ java.lang.String getParamsOrDefault(
        java.lang.String key,
        /* nullable */
        java.lang.String defaultValue) {
      if (key == null) {
        throw new NullPointerException("map key");
      }
      java.util.Map<java.lang.String, java.lang.String> map = internalGetParams().getMap();
      return map.containsKey(key) ? map.get(key) : defaultValue;
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters describing the semantic of the
     * imported data, any string must be up to 25000
     * characters long.
     *
     * *  For Tables:
     *    `schema_inference_version` - (integer) Required. The version of the
     *        algorithm that should be used for the initial inference of the
     *        schema (columns' DataTypes) of the table the data is being imported
     *        into. Allowed values: "1".
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 2;</code>
     */
    @java.lang.Override
    public java.lang.String getParamsOrThrow(java.lang.String key) {
      if (key == null) {
        throw new NullPointerException("map key");
      }
      java.util.Map<java.lang.String, java.lang.String> map = internalGetParams().getMap();
      if (!map.containsKey(key)) {
        throw new java.lang.IllegalArgumentException();
      }
      return map.get(key);
    }

    public Builder clearParams() {
      bitField0_ = (bitField0_ & ~0x00000004);
      internalGetMutableParams().getMutableMap().clear();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters describing the semantic of the
     * imported data, any string must be up to 25000
     * characters long.
     *
     * *  For Tables:
     *    `schema_inference_version` - (integer) Required. The version of the
     *        algorithm that should be used for the initial inference of the
     *        schema (columns' DataTypes) of the table the data is being imported
     *        into. Allowed values: "1".
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 2;</code>
     */
    public Builder removeParams(java.lang.String key) {
      if (key == null) {
        throw new NullPointerException("map key");
      }
      internalGetMutableParams().getMutableMap().remove(key);
      return this;
    }
    /** Use alternate mutation accessors instead. */
    @java.lang.Deprecated
    public java.util.Map<java.lang.String, java.lang.String> getMutableParams() {
      bitField0_ |= 0x00000004;
      return internalGetMutableParams().getMutableMap();
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters describing the semantic of the
     * imported data, any string must be up to 25000
     * characters long.
     *
     * *  For Tables:
     *    `schema_inference_version` - (integer) Required. The version of the
     *        algorithm that should be used for the initial inference of the
     *        schema (columns' DataTypes) of the table the data is being imported
     *        into. Allowed values: "1".
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 2;</code>
     */
    public Builder putParams(java.lang.String key, java.lang.String value) {
      if (key == null) {
        throw new NullPointerException("map key");
      }
      if (value == null) {
        throw new NullPointerException("map value");
      }
      internalGetMutableParams().getMutableMap().put(key, value);
      bitField0_ |= 0x00000004;
      return this;
    }
    /**
     *
     *
     * <pre>
     * Additional domain-specific parameters describing the semantic of the
     * imported data, any string must be up to 25000
     * characters long.
     *
     * *  For Tables:
     *    `schema_inference_version` - (integer) Required. The version of the
     *        algorithm that should be used for the initial inference of the
     *        schema (columns' DataTypes) of the table the data is being imported
     *        into. Allowed values: "1".
     * </pre>
     *
     * <code>map&lt;string, string&gt; params = 2;</code>
     */
    public Builder putAllParams(java.util.Map<java.lang.String, java.lang.String> values) {
      internalGetMutableParams().getMutableMap().putAll(values);
      bitField0_ |= 0x00000004;
      return this;
    }

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

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

    // @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.InputConfig)
  }

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

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

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

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

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

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