Package com.google.cloud.automl.v1

Class InputConfig.Builder

  • All Implemented Interfaces:
    InputConfigOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
    Enclosing class:
    InputConfig
    public static final class InputConfig.Builder
extends com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>
implements InputConfigOrBuilder
     Input configuration for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.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.v1.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:

 <h4>AutoML Vision</h4>


 <div class="ds-selector-tabs"><section><h5>Classification</h5>

 See [Preparing your training
 data](https://cloud.google.com/vision/automl/docs/prepare) for more
 information.

 CSV file(s) with each line in format:

     ML_USE,GCS_FILE_PATH,LABEL,LABEL,...

 *   `ML_USE` - Identifies the data set that the current row (file) applies
 to.
     This value can be one of the following:
     * `TRAIN` - Rows in this file are used to train the model.
     * `TEST` - Rows in this file are used to test the model during training.
     * `UNASSIGNED` - Rows in this file are not categorized. They are
        Automatically divided into train and test data. 80% for training and
        20% for testing.

 *   `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to
      30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP,
      .TIFF, .ICO.

 *   `LABEL` - A label that identifies the object in the image.

 For the `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


 </section><section><h5>Object Detection</h5>
 See [Preparing your training
 data](https://cloud.google.com/vision/automl/object-detection/docs/prepare)
 for more information.

 A CSV file(s) with each line in format:

     ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)

 *   `ML_USE` - Identifies the data set that the current row (file) applies
 to.
     This value can be one of the following:
     * `TRAIN` - Rows in this file are used to train the model.
     * `TEST` - Rows in this file are used to test the model during training.
     * `UNASSIGNED` - Rows in this file are not categorized. They are
        Automatically divided into train and test data. 80% for training and
        20% for testing.

 *  `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to
     30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image
     is assumed to be exhaustively labeled.

 *  `LABEL` - A label that identifies the object in the image specified by the
    `BOUNDING_BOX`.

 *  `BOUNDING BOX` - The vertices of an object in the example image.
    The minimum allowed `BOUNDING_BOX` edge length is 0.01, and no more than
    500 `BOUNDING_BOX` instances per image are allowed (one `BOUNDING_BOX`
    per line). If an image has no looked for objects then it should be
    mentioned just once with no LABEL and the ",,,,,,," in place of the
   `BOUNDING_BOX`.

 **Four 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,,,,,,,,,
   </section>
 </div>


 <h4>AutoML Video Intelligence</h4>


 <div class="ds-selector-tabs"><section><h5>Classification</h5>

 See [Preparing your training
 data](https://cloud.google.com/video-intelligence/automl/docs/prepare) for
 more information.

 CSV file(s) with each line in format:

     ML_USE,GCS_FILE_PATH

 For `ML_USE`, do not use `VALIDATE`.

 `GCS_FILE_PATH` is the path to another .csv file that describes training
 example for a 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 the end time must be after the start time. 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 should 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,,,



 </section><section><h5>Object Tracking</h5>

 See [Preparing your training
 data](/video-intelligence/automl/object-tracking/docs/prepare) for more
 information.

 CSV file(s) with each line in format:

     ML_USE,GCS_FILE_PATH

 For `ML_USE`, do not use `VALIDATE`.

 `GCS_FILE_PATH` is the path to another .csv file that describes training
 example for a given `ML_USE`, using 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_ID`s 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,,,,,,,,,,,
   </section>
 </div>


 <h4>AutoML Natural Language</h4>


 <div class="ds-selector-tabs"><section><h5>Entity Extraction</h5>

 See [Preparing your training
 data](/natural-language/automl/entity-analysis/docs/prepare) for more
 information.

 One or more CSV file(s) with each line in the following format:

     ML_USE,GCS_FILE_PATH

 *   `ML_USE` - Identifies the data set that the current row (file) applies
 to.
     This value can be one of the following:
     * `TRAIN` - Rows in this file are used to train the model.
     * `TEST` - Rows in this file are used to test the model during training.
     * `UNASSIGNED` - Rows in this file are not categorized. They are
        Automatically divided into train and test data. 80% for training and
        20% for testing..

 *   `GCS_FILE_PATH` - a Identifies JSON Lines (.JSONL) file stored in
      Google Cloud Storage that contains in-line text in-line as documents
      for model training.

 After the training data set has been determined from the `TRAIN` and
 `UNASSIGNED` CSV files, the training data is divided into train and
 validation data sets. 70% for training and 30% for validation.

 For example:

     TRAIN,gs://folder/file1.jsonl
     VALIDATE,gs://folder/file2.jsonl
     TEST,gs://folder/file3.jsonl

 **In-line JSONL files**

 In-line .JSONL files contain, per line, a JSON document that wraps a
 [`text_snippet`][google.cloud.automl.v1.TextSnippet] field followed by
 one or more [`annotations`][google.cloud.automl.v1.AnnotationPayload]
 fields, which have `display_name` and `text_extraction` fields to describe
 the entity from the text snippet. Multiple JSON documents can be separated
 using line breaks (\n).

 The supplied text must be annotated exhaustively. For example, if you
 include the text "horse", but do not label it as "animal",
 then "horse" is assumed to not be an "animal".

 Any given text snippet content must have 30,000 characters or
 less, and also be UTF-8 NFC encoded. ASCII is accepted as it is
 UTF-8 NFC encoded.

 For example:

     {
       "text_snippet": {
         "content": "dog car cat"
       },
       "annotations": [
          {
            "display_name": "animal",
            "text_extraction": {
              "text_segment": {"start_offset": 0, "end_offset": 2}
           }
          },
          {
           "display_name": "vehicle",
            "text_extraction": {
              "text_segment": {"start_offset": 4, "end_offset": 6}
            }
          },
          {
            "display_name": "animal",
            "text_extraction": {
              "text_segment": {"start_offset": 8, "end_offset": 10}
            }
          }
      ]
     }\n
     {
        "text_snippet": {
          "content": "This dog is good."
        },
        "annotations": [
           {
             "display_name": "animal",
             "text_extraction": {
               "text_segment": {"start_offset": 5, "end_offset": 7}
             }
           }
        ]
     }

 **JSONL files that reference documents**

 .JSONL files contain, per line, a JSON document that wraps a
 `input_config` that contains the path to a source document.
 Multiple JSON documents can be separated using line breaks (\n).

 Supported document extensions: .PDF, .TIF, .TIFF

 For example:

     {
       "document": {
         "input_config": {
           "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
           }
         }
       }
     }\n
     {
       "document": {
         "input_config": {
           "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ]
           }
         }
       }
     }

 **In-line JSONL files with document layout information**

 **Note:** You can only annotate documents using the UI. The format described
 below applies to annotated documents exported using the UI or `exportData`.

 In-line .JSONL files for documents contain, per line, a JSON document
 that wraps a `document` field that provides the textual content of the
 document and the layout information.

 For example:

     {
       "document": {
               "document_text": {
                 "content": "dog car cat"
               }
               "layout": [
                 {
                   "text_segment": {
                     "start_offset": 0,
                     "end_offset": 11,
                    },
                    "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,
                 }
               ],
               "document_dimensions": {
                 "width": 8.27,
                 "height": 11.69,
                 "unit": INCH,
               }
               "page_count": 3,
             },
             "annotations": [
               {
                 "display_name": "animal",
                 "text_extraction": {
                   "text_segment": {"start_offset": 0, "end_offset": 3}
                 }
               },
               {
                 "display_name": "vehicle",
                 "text_extraction": {
                   "text_segment": {"start_offset": 4, "end_offset": 7}
                 }
               },
               {
                 "display_name": "animal",
                 "text_extraction": {
                   "text_segment": {"start_offset": 8, "end_offset": 11}
                 }
               },
             ],




 </section><section><h5>Classification</h5>

 See [Preparing your training
 data](https://cloud.google.com/natural-language/automl/docs/prepare) for more
 information.

 One or more CSV file(s) with each line in the following format:

     ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...

 *   `ML_USE` - Identifies the data set that the current row (file) applies
 to.
     This value can be one of the following:
     * `TRAIN` - Rows in this file are used to train the model.
     * `TEST` - Rows in this file are used to test the model during training.
     * `UNASSIGNED` - Rows in this file are not categorized. They are
        Automatically divided into train and test data. 80% for training and
        20% for testing.

 *   `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a pattern. If
     the column content is a valid Google Cloud Storage file path, that is,
     prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, if
     the content is enclosed in double quotes (""), it is treated as a
     `TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a
     file with supported extension and UTF-8 encoding, for example,
     "gs://folder/content.txt" AutoML imports the file content
     as a text snippet. For `TEXT_SNIPPET`, AutoML imports the column content
     excluding quotes. In both cases, size of the content must be 10MB or
     less in size. For zip files, the size of each file inside the zip must be
     10MB or less in size.

     For the `MULTICLASS` classification type, at most one `LABEL` is allowed.

     The `ML_USE` and `LABEL` columns are optional.
     Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP

 A maximum of 100 unique labels are allowed per CSV row.

 Sample rows:

     TRAIN,"They have bad food and very rude",RudeService,BadFood
     gs://folder/content.txt,SlowService
     TEST,gs://folder/document.pdf
     VALIDATE,gs://folder/text_files.zip,BadFood



 </section><section><h5>Sentiment Analysis</h5>

 See [Preparing your training
 data](https://cloud.google.com/natural-language/automl/docs/prepare) for more
 information.

 CSV file(s) with each line in format:

     ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT

 *   `ML_USE` - Identifies the data set that the current row (file) applies
 to.
     This value can be one of the following:
     * `TRAIN` - Rows in this file are used to train the model.
     * `TEST` - Rows in this file are used to test the model during training.
     * `UNASSIGNED` - Rows in this file are not categorized. They are
        Automatically divided into train and test data. 80% for training and
        20% for testing.

 *   `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a pattern. If
     the column content is a valid  Google Cloud Storage file path, that is,
     prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, if
     the content is enclosed in double quotes (""), it is treated as a
     `TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a
     file with supported extension and UTF-8 encoding, for example,
     "gs://folder/content.txt" AutoML imports the file content
     as a text snippet. For `TEXT_SNIPPET`, AutoML imports the column content
     excluding quotes. In both cases, size of the content must be 128kB or
     less in size. For zip files, the size of each file inside the zip must be
     128kB or less in size.

     The `ML_USE` and `SENTIMENT` columns are optional.
     Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP

 *  `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
     large.

 Sample rows:

     TRAIN,"@freewrytin this is way too good for your product",2
     gs://folder/content.txt,3
     TEST,gs://folder/document.pdf
     VALIDATE,gs://folder/text_files.zip,2
   </section>
 </div>



 <h4>AutoML Tables</h4><div class="ui-datasection-main"><section
 class="selected">

 See [Preparing your training
 data](https://cloud.google.com/automl-tables/docs/prepare) for more
 information.

 You can use either
 [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or
 [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source].
 All input is concatenated into a
 single
 [primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id]

 **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:
 <pre>
 "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"}]}
 </pre>
 **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.

   </section>
 </div>


 **Input field 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`
 : The path to a file on Google Cloud Storage. For example,
   "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
   n 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`
 : The content of a text snippet, UTF-8 encoded, enclosed within
   double quotes ("").

 `DOCUMENT`
 : A field that provides the textual content with document and the layout
   information.


  **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.
    Protobuf type google.cloud.automl.v1.InputConfig

    • Method Detail

      • getDescriptor

        public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

      • internalGetMapField

        protected com.google.protobuf.MapField internalGetMapField​(int number)
        Overrides:
        internalGetMapField in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • internalGetMutableMapField

        protected com.google.protobuf.MapField internalGetMutableMapField​(int number)
        Overrides:
        internalGetMutableMapField in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • clear

        public InputConfig.Builder clear()
        Specified by:
        clear in interface com.google.protobuf.Message.Builder
        Specified by:
        clear in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        clear in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • getDescriptorForType

        public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
        Specified by:
        getDescriptorForType in interface com.google.protobuf.Message.Builder
        Specified by:
        getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
        Overrides:
        getDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • getDefaultInstanceForType

        public InputConfig getDefaultInstanceForType()
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

      • build

        public InputConfig build()
        Specified by:
        build in interface com.google.protobuf.Message.Builder
        Specified by:
        build in interface com.google.protobuf.MessageLite.Builder

      • buildPartial

        public InputConfig buildPartial()
        Specified by:
        buildPartial in interface com.google.protobuf.Message.Builder
        Specified by:
        buildPartial in interface com.google.protobuf.MessageLite.Builder

      • clone

        public InputConfig.Builder clone()
        Specified by:
        clone in interface com.google.protobuf.Message.Builder
        Specified by:
        clone in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        clone in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • setField

        public InputConfig.Builder setField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                    Object value)
        Specified by:
        setField in interface com.google.protobuf.Message.Builder
        Overrides:
        setField in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • clearField

        public InputConfig.Builder clearField​(com.google.protobuf.Descriptors.FieldDescriptor field)
        Specified by:
        clearField in interface com.google.protobuf.Message.Builder
        Overrides:
        clearField in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • clearOneof

        public InputConfig.Builder clearOneof​(com.google.protobuf.Descriptors.OneofDescriptor oneof)
        Specified by:
        clearOneof in interface com.google.protobuf.Message.Builder
        Overrides:
        clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • setRepeatedField

        public InputConfig.Builder setRepeatedField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                            int index,
                                            Object value)
        Specified by:
        setRepeatedField in interface com.google.protobuf.Message.Builder
        Overrides:
        setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • addRepeatedField

        public InputConfig.Builder addRepeatedField​(com.google.protobuf.Descriptors.FieldDescriptor field,
                                            Object value)
        Specified by:
        addRepeatedField in interface com.google.protobuf.Message.Builder
        Overrides:
        addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • mergeFrom

        public InputConfig.Builder mergeFrom​(com.google.protobuf.Message other)
        Specified by:
        mergeFrom in interface com.google.protobuf.Message.Builder
        Overrides:
        mergeFrom in class com.google.protobuf.AbstractMessage.Builder<InputConfig.Builder>

      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • mergeFrom

        public InputConfig.Builder mergeFrom​(com.google.protobuf.CodedInputStream input,
                                     com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                              throws IOException
        Specified by:
        mergeFrom in interface com.google.protobuf.Message.Builder
        Specified by:
        mergeFrom in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        mergeFrom in class com.google.protobuf.AbstractMessage.Builder<InputConfig.Builder>
        Throws:
        IOException

      • hasGcsSource

        public boolean hasGcsSource()
         The Google Cloud Storage location for the input content.
 For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
 a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
        .google.cloud.automl.v1.GcsSource gcs_source = 1;
        Specified by:
        hasGcsSource in interface InputConfigOrBuilder
        Returns:
        Whether the gcsSource field is set.

      • getGcsSource

        public GcsSource getGcsSource()
         The Google Cloud Storage location for the input content.
 For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
 a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
        .google.cloud.automl.v1.GcsSource gcs_source = 1;
        Specified by:
        getGcsSource in interface InputConfigOrBuilder
        Returns:
        The gcsSource.

      • setGcsSource

        public InputConfig.Builder setGcsSource​(GcsSource value)
         The Google Cloud Storage location for the input content.
 For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
 a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
        .google.cloud.automl.v1.GcsSource gcs_source = 1;

      • setGcsSource

        public InputConfig.Builder setGcsSource​(GcsSource.Builder builderForValue)
         The Google Cloud Storage location for the input content.
 For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
 a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
        .google.cloud.automl.v1.GcsSource gcs_source = 1;

      • mergeGcsSource

        public InputConfig.Builder mergeGcsSource​(GcsSource value)
         The Google Cloud Storage location for the input content.
 For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
 a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
        .google.cloud.automl.v1.GcsSource gcs_source = 1;

      • clearGcsSource

        public InputConfig.Builder clearGcsSource()
         The Google Cloud Storage location for the input content.
 For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
 a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
        .google.cloud.automl.v1.GcsSource gcs_source = 1;

      • getGcsSourceBuilder

        public GcsSource.Builder getGcsSourceBuilder()
         The Google Cloud Storage location for the input content.
 For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
 a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
        .google.cloud.automl.v1.GcsSource gcs_source = 1;

      • getGcsSourceOrBuilder

        public GcsSourceOrBuilder getGcsSourceOrBuilder()
         The Google Cloud Storage location for the input content.
 For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
 a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
        .google.cloud.automl.v1.GcsSource gcs_source = 1;
        Specified by:
        getGcsSourceOrBuilder in interface InputConfigOrBuilder

      • getParamsCount

        public int getParamsCount()
        Description copied from interface: InputConfigOrBuilder
         Additional domain-specific parameters describing the semantic of the
 imported data, any string must be up to 25000
 characters long.

 <h4>AutoML Tables</h4>

 `schema_inference_version`
 : (integer) This value must be supplied.
   The version of the
   algorithm to use for the initial inference of the
   column data types of the imported table. Allowed values: "1".
        map<string, string> params = 2;
        Specified by:
        getParamsCount in interface InputConfigOrBuilder

      • containsParams

        public boolean containsParams​(String key)
         Additional domain-specific parameters describing the semantic of the
 imported data, any string must be up to 25000
 characters long.

 <h4>AutoML Tables</h4>

 `schema_inference_version`
 : (integer) This value must be supplied.
   The version of the
   algorithm to use for the initial inference of the
   column data types of the imported table. Allowed values: "1".
        map<string, string> params = 2;
        Specified by:
        containsParams in interface InputConfigOrBuilder

      • getParamsMap

        public Map<String,​String> getParamsMap()
         Additional domain-specific parameters describing the semantic of the
 imported data, any string must be up to 25000
 characters long.

 <h4>AutoML Tables</h4>

 `schema_inference_version`
 : (integer) This value must be supplied.
   The version of the
   algorithm to use for the initial inference of the
   column data types of the imported table. Allowed values: "1".
        map<string, string> params = 2;
        Specified by:
        getParamsMap in interface InputConfigOrBuilder

      • getParamsOrDefault

        public String getParamsOrDefault​(String key,
                                 String defaultValue)
         Additional domain-specific parameters describing the semantic of the
 imported data, any string must be up to 25000
 characters long.

 <h4>AutoML Tables</h4>

 `schema_inference_version`
 : (integer) This value must be supplied.
   The version of the
   algorithm to use for the initial inference of the
   column data types of the imported table. Allowed values: "1".
        map<string, string> params = 2;
        Specified by:
        getParamsOrDefault in interface InputConfigOrBuilder

      • getParamsOrThrow

        public String getParamsOrThrow​(String key)
         Additional domain-specific parameters describing the semantic of the
 imported data, any string must be up to 25000
 characters long.

 <h4>AutoML Tables</h4>

 `schema_inference_version`
 : (integer) This value must be supplied.
   The version of the
   algorithm to use for the initial inference of the
   column data types of the imported table. Allowed values: "1".
        map<string, string> params = 2;
        Specified by:
        getParamsOrThrow in interface InputConfigOrBuilder

      • removeParams

        public InputConfig.Builder removeParams​(String key)
         Additional domain-specific parameters describing the semantic of the
 imported data, any string must be up to 25000
 characters long.

 <h4>AutoML Tables</h4>

 `schema_inference_version`
 : (integer) This value must be supplied.
   The version of the
   algorithm to use for the initial inference of the
   column data types of the imported table. Allowed values: "1".
        map<string, string> params = 2;

      • getMutableParams

        @Deprecated
public Map<String,​String> getMutableParams()
        Deprecated.
        Use alternate mutation accessors instead.

      • putParams

        public InputConfig.Builder putParams​(String key,
                                     String value)
         Additional domain-specific parameters describing the semantic of the
 imported data, any string must be up to 25000
 characters long.

 <h4>AutoML Tables</h4>

 `schema_inference_version`
 : (integer) This value must be supplied.
   The version of the
   algorithm to use for the initial inference of the
   column data types of the imported table. Allowed values: "1".
        map<string, string> params = 2;

      • putAllParams

        public InputConfig.Builder putAllParams​(Map<String,​String> values)
         Additional domain-specific parameters describing the semantic of the
 imported data, any string must be up to 25000
 characters long.

 <h4>AutoML Tables</h4>

 `schema_inference_version`
 : (integer) This value must be supplied.
   The version of the
   algorithm to use for the initial inference of the
   column data types of the imported table. Allowed values: "1".
        map<string, string> params = 2;

      • setUnknownFields

        public final InputConfig.Builder setUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        setUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>

      • mergeUnknownFields

        public final InputConfig.Builder mergeUnknownFields​(com.google.protobuf.UnknownFieldSet unknownFields)
        Specified by:
        mergeUnknownFields in interface com.google.protobuf.Message.Builder
        Overrides:
        mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<InputConfig.Builder>