/*
* Copyright 2020 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: google/cloud/automl/v1/io.proto
package com.google.cloud.automl.v1;
/**
*
*
*
* 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 {@code google.cloud.automl.v1.InputConfig}
*/
public final class InputConfig extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.automl.v1.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();
}
@java.lang.Override
public final com.google.protobuf.UnknownFieldSet getUnknownFields() {
return this.unknownFields;
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1.Io
.internal_static_google_cloud_automl_v1_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.v1.Io
.internal_static_google_cloud_automl_v1_InputConfig_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.automl.v1.InputConfig.class,
com.google.cloud.automl.v1.InputConfig.Builder.class);
}
private int sourceCase_ = 0;
private java.lang.Object source_;
public enum SourceCase
implements
com.google.protobuf.Internal.EnumLite,
com.google.protobuf.AbstractMessage.InternalOneOfEnum {
GCS_SOURCE(1),
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 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;
/**
*
*
*
* 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;
*
* @return Whether the gcsSource field is set.
*/
@java.lang.Override
public boolean hasGcsSource() {
return sourceCase_ == 1;
}
/**
*
*
*
* 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;
*
* @return The gcsSource.
*/
@java.lang.Override
public com.google.cloud.automl.v1.GcsSource getGcsSource() {
if (sourceCase_ == 1) {
return (com.google.cloud.automl.v1.GcsSource) source_;
}
return com.google.cloud.automl.v1.GcsSource.getDefaultInstance();
}
/**
*
*
*
* 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;
*/
@java.lang.Override
public com.google.cloud.automl.v1.GcsSourceOrBuilder getGcsSourceOrBuilder() {
if (sourceCase_ == 1) {
return (com.google.cloud.automl.v1.GcsSource) source_;
}
return com.google.cloud.automl.v1.GcsSource.getDefaultInstance();
}
public static final int PARAMS_FIELD_NUMBER = 2;
private static final class ParamsDefaultEntryHolder {
static final com.google.protobuf.MapEntry defaultEntry =
com.google.protobuf.MapEntry.newDefaultInstance(
com.google.cloud.automl.v1.Io
.internal_static_google_cloud_automl_v1_InputConfig_ParamsEntry_descriptor,
com.google.protobuf.WireFormat.FieldType.STRING,
"",
com.google.protobuf.WireFormat.FieldType.STRING,
"");
}
@SuppressWarnings("serial")
private com.google.protobuf.MapField params_;
private com.google.protobuf.MapField internalGetParams() {
if (params_ == null) {
return com.google.protobuf.MapField.emptyMapField(ParamsDefaultEntryHolder.defaultEntry);
}
return params_;
}
public int getParamsCount() {
return internalGetParams().getMap().size();
}
/**
*
*
*
* 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;
*/
@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 getParams() {
return 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;
*/
@java.lang.Override
public java.util.Map getParamsMap() {
return internalGetParams().getMap();
}
/**
*
*
*
* 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;
*/
@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 map = internalGetParams().getMap();
return map.containsKey(key) ? map.get(key) : 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;
*/
@java.lang.Override
public java.lang.String getParamsOrThrow(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map = internalGetParams().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
if (sourceCase_ == 1) {
output.writeMessage(1, (com.google.cloud.automl.v1.GcsSource) source_);
}
com.google.protobuf.GeneratedMessageV3.serializeStringMapTo(
output, internalGetParams(), ParamsDefaultEntryHolder.defaultEntry, 2);
getUnknownFields().writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (sourceCase_ == 1) {
size +=
com.google.protobuf.CodedOutputStream.computeMessageSize(
1, (com.google.cloud.automl.v1.GcsSource) source_);
}
for (java.util.Map.Entry entry :
internalGetParams().getMap().entrySet()) {
com.google.protobuf.MapEntry params__ =
ParamsDefaultEntryHolder.defaultEntry
.newBuilderForType()
.setKey(entry.getKey())
.setValue(entry.getValue())
.build();
size += com.google.protobuf.CodedOutputStream.computeMessageSize(2, params__);
}
size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj instanceof com.google.cloud.automl.v1.InputConfig)) {
return super.equals(obj);
}
com.google.cloud.automl.v1.InputConfig other = (com.google.cloud.automl.v1.InputConfig) obj;
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 0:
default:
}
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
if (!internalGetParams().getMap().isEmpty()) {
hash = (37 * hash) + PARAMS_FIELD_NUMBER;
hash = (53 * hash) + internalGetParams().hashCode();
}
switch (sourceCase_) {
case 1:
hash = (37 * hash) + GCS_SOURCE_FIELD_NUMBER;
hash = (53 * hash) + getGcsSource().hashCode();
break;
case 0:
default:
}
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(
byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(java.io.InputStream input)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1.InputConfig parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1.InputConfig parseDelimitedFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(
com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1.InputConfig parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(com.google.cloud.automl.v1.InputConfig prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
*
*
* 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 {@code google.cloud.automl.v1.InputConfig}
*/
public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder
implements
// @@protoc_insertion_point(builder_implements:google.cloud.automl.v1.InputConfig)
com.google.cloud.automl.v1.InputConfigOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1.Io
.internal_static_google_cloud_automl_v1_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.v1.Io
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.ensureFieldAccessorsInitialized(
com.google.cloud.automl.v1.InputConfig.class,
com.google.cloud.automl.v1.InputConfig.Builder.class);
}
// Construct using com.google.cloud.automl.v1.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();
}
internalGetMutableParams().clear();
sourceCase_ = 0;
source_ = null;
return this;
}
@java.lang.Override
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
return com.google.cloud.automl.v1.Io
.internal_static_google_cloud_automl_v1_InputConfig_descriptor;
}
@java.lang.Override
public com.google.cloud.automl.v1.InputConfig getDefaultInstanceForType() {
return com.google.cloud.automl.v1.InputConfig.getDefaultInstance();
}
@java.lang.Override
public com.google.cloud.automl.v1.InputConfig build() {
com.google.cloud.automl.v1.InputConfig result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
@java.lang.Override
public com.google.cloud.automl.v1.InputConfig buildPartial() {
com.google.cloud.automl.v1.InputConfig result =
new com.google.cloud.automl.v1.InputConfig(this);
if (bitField0_ != 0) {
buildPartial0(result);
}
buildPartialOneofs(result);
onBuilt();
return result;
}
private void buildPartial0(com.google.cloud.automl.v1.InputConfig result) {
int from_bitField0_ = bitField0_;
if (((from_bitField0_ & 0x00000002) != 0)) {
result.params_ = internalGetParams();
result.params_.makeImmutable();
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private void buildPartialOneofs(com.google.cloud.automl.v1.InputConfig result) {
result.sourceCase_ = sourceCase_;
result.source_ = this.source_;
if (sourceCase_ == 1 && gcsSourceBuilder_ != null) {
result.source_ = gcsSourceBuilder_.build();
}
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@java.lang.Override
public Builder clone() {
return super.clone();
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@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.v1.InputConfig) {
return mergeFrom((com.google.cloud.automl.v1.InputConfig) other);
} else {
super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(com.google.cloud.automl.v1.InputConfig other) {
if (other == com.google.cloud.automl.v1.InputConfig.getDefaultInstance()) return this;
internalGetMutableParams().mergeFrom(other.internalGetParams());
bitField0_ |= 0x00000002;
switch (other.getSourceCase()) {
case GCS_SOURCE:
{
mergeGcsSource(other.getGcsSource());
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 params__ =
input.readMessage(
ParamsDefaultEntryHolder.defaultEntry.getParserForType(),
extensionRegistry);
internalGetMutableParams()
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bitField0_ |= 0x00000002;
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} // case 18
default:
{
if (!super.parseUnknownField(input, extensionRegistry, tag)) {
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} // 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.v1.GcsSource,
com.google.cloud.automl.v1.GcsSource.Builder,
com.google.cloud.automl.v1.GcsSourceOrBuilder>
gcsSourceBuilder_;
/**
*
*
*
* 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;
*
* @return Whether the gcsSource field is set.
*/
@java.lang.Override
public boolean hasGcsSource() {
return sourceCase_ == 1;
}
/**
*
*
*
* 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;
*
* @return The gcsSource.
*/
@java.lang.Override
public com.google.cloud.automl.v1.GcsSource getGcsSource() {
if (gcsSourceBuilder_ == null) {
if (sourceCase_ == 1) {
return (com.google.cloud.automl.v1.GcsSource) source_;
}
return com.google.cloud.automl.v1.GcsSource.getDefaultInstance();
} else {
if (sourceCase_ == 1) {
return gcsSourceBuilder_.getMessage();
}
return com.google.cloud.automl.v1.GcsSource.getDefaultInstance();
}
}
/**
*
*
*
* 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;
*/
public Builder setGcsSource(com.google.cloud.automl.v1.GcsSource value) {
if (gcsSourceBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
source_ = value;
onChanged();
} else {
gcsSourceBuilder_.setMessage(value);
}
sourceCase_ = 1;
return this;
}
/**
*
*
*
* 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;
*/
public Builder setGcsSource(com.google.cloud.automl.v1.GcsSource.Builder builderForValue) {
if (gcsSourceBuilder_ == null) {
source_ = builderForValue.build();
onChanged();
} else {
gcsSourceBuilder_.setMessage(builderForValue.build());
}
sourceCase_ = 1;
return this;
}
/**
*
*
*
* 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;
*/
public Builder mergeGcsSource(com.google.cloud.automl.v1.GcsSource value) {
if (gcsSourceBuilder_ == null) {
if (sourceCase_ == 1
&& source_ != com.google.cloud.automl.v1.GcsSource.getDefaultInstance()) {
source_ =
com.google.cloud.automl.v1.GcsSource.newBuilder(
(com.google.cloud.automl.v1.GcsSource) source_)
.mergeFrom(value)
.buildPartial();
} else {
source_ = value;
}
onChanged();
} else {
if (sourceCase_ == 1) {
gcsSourceBuilder_.mergeFrom(value);
} else {
gcsSourceBuilder_.setMessage(value);
}
}
sourceCase_ = 1;
return this;
}
/**
*
*
*
* 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;
*/
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;
}
/**
*
*
*
* 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;
*/
public com.google.cloud.automl.v1.GcsSource.Builder getGcsSourceBuilder() {
return getGcsSourceFieldBuilder().getBuilder();
}
/**
*
*
*
* 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;
*/
@java.lang.Override
public com.google.cloud.automl.v1.GcsSourceOrBuilder getGcsSourceOrBuilder() {
if ((sourceCase_ == 1) && (gcsSourceBuilder_ != null)) {
return gcsSourceBuilder_.getMessageOrBuilder();
} else {
if (sourceCase_ == 1) {
return (com.google.cloud.automl.v1.GcsSource) source_;
}
return com.google.cloud.automl.v1.GcsSource.getDefaultInstance();
}
}
/**
*
*
*
* 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;
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1.GcsSource,
com.google.cloud.automl.v1.GcsSource.Builder,
com.google.cloud.automl.v1.GcsSourceOrBuilder>
getGcsSourceFieldBuilder() {
if (gcsSourceBuilder_ == null) {
if (!(sourceCase_ == 1)) {
source_ = com.google.cloud.automl.v1.GcsSource.getDefaultInstance();
}
gcsSourceBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1.GcsSource,
com.google.cloud.automl.v1.GcsSource.Builder,
com.google.cloud.automl.v1.GcsSourceOrBuilder>(
(com.google.cloud.automl.v1.GcsSource) source_, getParentForChildren(), isClean());
source_ = null;
}
sourceCase_ = 1;
onChanged();
return gcsSourceBuilder_;
}
private com.google.protobuf.MapField params_;
private com.google.protobuf.MapField internalGetParams() {
if (params_ == null) {
return com.google.protobuf.MapField.emptyMapField(ParamsDefaultEntryHolder.defaultEntry);
}
return params_;
}
private com.google.protobuf.MapField
internalGetMutableParams() {
if (params_ == null) {
params_ = com.google.protobuf.MapField.newMapField(ParamsDefaultEntryHolder.defaultEntry);
}
if (!params_.isMutable()) {
params_ = params_.copy();
}
bitField0_ |= 0x00000002;
onChanged();
return params_;
}
public int getParamsCount() {
return internalGetParams().getMap().size();
}
/**
*
*
*
* 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;
*/
@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 getParams() {
return 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;
*/
@java.lang.Override
public java.util.Map getParamsMap() {
return internalGetParams().getMap();
}
/**
*
*
*
* 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;
*/
@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 map = internalGetParams().getMap();
return map.containsKey(key) ? map.get(key) : 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;
*/
@java.lang.Override
public java.lang.String getParamsOrThrow(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map = internalGetParams().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public Builder clearParams() {
bitField0_ = (bitField0_ & ~0x00000002);
internalGetMutableParams().getMutableMap().clear();
return this;
}
/**
*
*
*
* 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;
*/
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 getMutableParams() {
bitField0_ |= 0x00000002;
return internalGetMutableParams().getMutableMap();
}
/**
*
*
*
* 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;
*/
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_ |= 0x00000002;
return this;
}
/**
*
*
*
* 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;
*/
public Builder putAllParams(java.util.Map values) {
internalGetMutableParams().getMutableMap().putAll(values);
bitField0_ |= 0x00000002;
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.v1.InputConfig)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1.InputConfig)
private static final com.google.cloud.automl.v1.InputConfig DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.cloud.automl.v1.InputConfig();
}
public static com.google.cloud.automl.v1.InputConfig getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser PARSER =
new com.google.protobuf.AbstractParser() {
@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 parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public com.google.cloud.automl.v1.InputConfig getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}