Package com.google.cloud.automl.v1beta1

Class InputConfig

  • All Implemented Interfaces:
    InputConfigOrBuilder, com.google.protobuf.Message, com.google.protobuf.MessageLite, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Serializable
    public final class InputConfig
extends com.google.protobuf.GeneratedMessageV3
implements InputConfigOrBuilder
     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 \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}}
               }
             ],
           }\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 \n).:
           {
             "document": {
               "input_config": {
                 "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
                 }
               }
             }
           }\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,"@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.
    Protobuf type google.cloud.automl.v1beta1.InputConfig
    See Also:
    Serialized Form

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description
      static class  InputConfig.Builder
      Input configuration for ImportData Action.
      static class  InputConfig.SourceCase  

      • Nested classes/interfaces inherited from class com.google.protobuf.GeneratedMessageV3

        com.google.protobuf.GeneratedMessageV3.BuilderParent, com.google.protobuf.GeneratedMessageV3.ExtendableBuilder<MessageT extends com.google.protobuf.GeneratedMessageV3.ExtendableMessage<MessageT>,​BuilderT extends com.google.protobuf.GeneratedMessageV3.ExtendableBuilder<MessageT,​BuilderT>>, com.google.protobuf.GeneratedMessageV3.ExtendableMessage<MessageT extends com.google.protobuf.GeneratedMessageV3.ExtendableMessage<MessageT>>, com.google.protobuf.GeneratedMessageV3.ExtendableMessageOrBuilder<MessageT extends com.google.protobuf.GeneratedMessageV3.ExtendableMessage<MessageT>>, com.google.protobuf.GeneratedMessageV3.FieldAccessorTable, com.google.protobuf.GeneratedMessageV3.UnusedPrivateParameter

      • Nested classes/interfaces inherited from class com.google.protobuf.AbstractMessageLite

        com.google.protobuf.AbstractMessageLite.InternalOneOfEnum

    • Field Summary

      Fields 
      Modifier and Type Field Description
      static int BIGQUERY_SOURCE_FIELD_NUMBER  
      static int GCS_SOURCE_FIELD_NUMBER  
      static int PARAMS_FIELD_NUMBER  

      • Fields inherited from class com.google.protobuf.GeneratedMessageV3

        alwaysUseFieldBuilders, unknownFields

      • Fields inherited from class com.google.protobuf.AbstractMessage

        memoizedSize

      • Fields inherited from class com.google.protobuf.AbstractMessageLite

        memoizedHashCode

    • Method Detail

      • newInstance

        protected Object newInstance​(com.google.protobuf.GeneratedMessageV3.UnusedPrivateParameter unused)
        Overrides:
        newInstance in class com.google.protobuf.GeneratedMessageV3

      • 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

      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3

      • hasGcsSource

        public boolean hasGcsSource()
         The Google Cloud Storage location for the input content.
 In ImportData, the gcs_source points to a csv with structure described in
 the comment.
        .google.cloud.automl.v1beta1.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.
 In ImportData, the gcs_source points to a csv with structure described in
 the comment.
        .google.cloud.automl.v1beta1.GcsSource gcs_source = 1;
        Specified by:
        getGcsSource in interface InputConfigOrBuilder
        Returns:
        The gcsSource.

      • getGcsSourceOrBuilder

        public GcsSourceOrBuilder getGcsSourceOrBuilder()
         The Google Cloud Storage location for the input content.
 In ImportData, the gcs_source points to a csv with structure described in
 the comment.
        .google.cloud.automl.v1beta1.GcsSource gcs_source = 1;
        Specified by:
        getGcsSourceOrBuilder in interface InputConfigOrBuilder

      • hasBigquerySource

        public boolean hasBigquerySource()
         The BigQuery location for the input content.
        .google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;
        Specified by:
        hasBigquerySource in interface InputConfigOrBuilder
        Returns:
        Whether the bigquerySource field is set.

      • getBigquerySource

        public BigQuerySource getBigquerySource()
         The BigQuery location for the input content.
        .google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 3;
        Specified by:
        getBigquerySource in interface InputConfigOrBuilder
        Returns:
        The bigquerySource.

      • 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.

 *  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".
        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.

 *  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".
        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.

 *  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".
        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.

 *  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".
        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.

 *  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".
        map<string, string> params = 2;
        Specified by:
        getParamsOrThrow in interface InputConfigOrBuilder

      • isInitialized

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

      • writeTo

        public void writeTo​(com.google.protobuf.CodedOutputStream output)
             throws IOException
        Specified by:
        writeTo in interface com.google.protobuf.MessageLite
        Overrides:
        writeTo in class com.google.protobuf.GeneratedMessageV3
        Throws:
        IOException

      • getSerializedSize

        public int getSerializedSize()
        Specified by:
        getSerializedSize in interface com.google.protobuf.MessageLite
        Overrides:
        getSerializedSize in class com.google.protobuf.GeneratedMessageV3

      • equals

        public boolean equals​(Object obj)
        Specified by:
        equals in interface com.google.protobuf.Message
        Overrides:
        equals in class com.google.protobuf.AbstractMessage

      • hashCode

        public int hashCode()
        Specified by:
        hashCode in interface com.google.protobuf.Message
        Overrides:
        hashCode in class com.google.protobuf.AbstractMessage

      • parseFrom

        public static InputConfig parseFrom​(ByteBuffer data)
                             throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static InputConfig parseFrom​(ByteBuffer data,
                                    com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                             throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static InputConfig parseFrom​(com.google.protobuf.ByteString data)
                             throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static InputConfig parseFrom​(com.google.protobuf.ByteString data,
                                    com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                             throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static InputConfig parseFrom​(byte[] data)
                             throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static InputConfig parseFrom​(byte[] data,
                                    com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                             throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException

      • parseFrom

        public static InputConfig parseFrom​(com.google.protobuf.CodedInputStream input,
                                    com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                             throws IOException
        Throws:
        IOException

      • newBuilderForType

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

      • toBuilder

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

      • newBuilderForType

        protected InputConfig.Builder newBuilderForType​(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)
        Specified by:
        newBuilderForType in class com.google.protobuf.GeneratedMessageV3

      • getDefaultInstance

        public static InputConfig getDefaultInstance()

      • parser

        public static com.google.protobuf.Parser<InputConfig> parser()

      • getParserForType

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

      • getDefaultInstanceForType

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