com.google.protobuf.AbstractMessageLite.Builder

com.google.protobuf.AbstractMessage.Builder<BuilderT>

com.google.protobuf.GeneratedMessage.Builder<InputConfig.Builder>

com.google.cloud.automl.v1.InputConfig.Builder

All Implemented Interfaces:: InputConfigOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable

Enclosing class:: InputConfig

public static final class InputConfig.Builder extends com.google.protobuf.GeneratedMessage.Builder<InputConfig.Builder> implements InputConfigOrBuilder

Input configuration for
[AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] action.

The format of input depends on dataset_metadata the Dataset into which
the import is happening has. As input source the
[gcs_source][google.cloud.automl.v1.InputConfig.gcs_source]
is expected, unless specified otherwise. Additionally any input .CSV file
by itself must be 100MB or smaller, unless specified otherwise.
If an "example" file (that is, image, video etc.) with identical content
(even if it had different `GCS_FILE_PATH`) is mentioned multiple times, then
its label, bounding boxes etc. are appended. The same file should be always
provided with the same `ML_USE` and `GCS_FILE_PATH`, if it is not, then
these values are nondeterministically selected from the given ones.

The formats are represented in EBNF with commas being literal and with
non-terminal symbols defined near the end of this comment. The formats are:

#### AutoML Vision

##### Classification

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

##### Object Detection
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>

#### AutoML Video Intelligence

##### Classification

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,,,

##### Object Tracking

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,,,,,,,,,,,

#### AutoML Natural Language

##### Entity Extraction

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}
}
},
],

##### Classification

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

##### Sentiment Analysis

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

#### AutoML Tables

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.

**Input field definitions:**

`ML_USE`
: ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED")
Describes how the given example (file) should be used for model
training. "UNASSIGNED" can be used when user has no preference.

`GCS_FILE_PATH`
: The path to a file on Google Cloud Storage. For example,
"gs://folder/image1.png".

`LABEL`
: A display name of an object on an image, video etc., e.g. "dog".
Must be up to 32 characters long and can consist only of ASCII
Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
For each label an AnnotationSpec is created which display_name
becomes the label; AnnotationSpecs are given back in predictions.

`INSTANCE_ID`
: A positive integer that identifies a specific instance of a
labeled entity on an example. Used e.g. to track two cars on
a video while being able to tell apart which one is which.

`BOUNDING_BOX`
: (`VERTEX,VERTEX,VERTEX,VERTEX` | `VERTEX,,,VERTEX,,`)
A rectangle parallel to the frame of the example (image,
video). If 4 vertices are given they are connected by edges
in the order provided, if 2 are given they are recognized
as diagonally opposite vertices of the rectangle.

`VERTEX`
: (`COORDINATE,COORDINATE`)
First coordinate is horizontal (x), the second is vertical (y).

`COORDINATE`
: A float in 0 to 1 range, relative to total length of
image or video in given dimension. For fractions the
leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
Point 0,0 is in top left.

`TIME_SEGMENT_START`
: (`TIME_OFFSET`)
Expresses a beginning, inclusive, of a time segment
within an example that has a time dimension
(e.g. video).

`TIME_SEGMENT_END`
: (`TIME_OFFSET`)
Expresses an end, exclusive, of a time segment within
n example that has a time dimension (e.g. video).

`TIME_OFFSET`
: A number of seconds as measured from the start of an
example (e.g. video). Fractions are allowed, up to a
microsecond precision. "inf" is allowed, and it means the end
of the example.

`TEXT_SNIPPET`
: The content of a text snippet, UTF-8 encoded, enclosed within
double quotes ("").

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

**Errors:**

If any of the provided CSV files can't be parsed or if more than certain
percent of CSV rows cannot be processed then the operation fails and
nothing is imported. Regardless of overall success or failure the per-row
failures, up to a certain count cap, is listed in
Operation.metadata.partial_failures.

Protobuf type google.cloud.automl.v1.InputConfig

Method Summary

Modifier and Type

Method

Description

InputConfig

build()

InputConfig

buildPartial()

InputConfig.Builder

clear()

InputConfig.Builder

clearGcsSource()

The Google Cloud Storage location for the input content.

InputConfig.Builder

clearParams()

InputConfig.Builder

clearSource()

boolean

containsParams(String key)

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied.

InputConfig

getDefaultInstanceForType()

static final com.google.protobuf.Descriptors.Descriptor

getDescriptor()

com.google.protobuf.Descriptors.Descriptor

getDescriptorForType()

GcsSource

getGcsSource()

The Google Cloud Storage location for the input content.

GcsSource.Builder

getGcsSourceBuilder()

The Google Cloud Storage location for the input content.

GcsSourceOrBuilder

getGcsSourceOrBuilder()

The Google Cloud Storage location for the input content.

Map<String,String>

getMutableParams()

Deprecated.

Map<String,String>

getParams()

Deprecated.

int

getParamsCount()

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied.

Map<String,String>

getParamsMap()

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied.

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. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied.

String

getParamsOrThrow(String key)

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied.

InputConfig.SourceCase

getSourceCase()

boolean

hasGcsSource()

The Google Cloud Storage location for the input content.

protected com.google.protobuf.GeneratedMessage.FieldAccessorTable

internalGetFieldAccessorTable()

protected com.google.protobuf.MapFieldReflectionAccessor

internalGetMapFieldReflection(int number)

protected com.google.protobuf.MapFieldReflectionAccessor

internalGetMutableMapFieldReflection(int number)

final boolean

isInitialized()

InputConfig.Builder

mergeFrom(InputConfig other)

InputConfig.Builder

mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

InputConfig.Builder

mergeFrom(com.google.protobuf.Message other)

InputConfig.Builder

mergeGcsSource(GcsSource value)

The Google Cloud Storage location for the input content.

InputConfig.Builder

putAllParams(Map<String,String> values)

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied.

InputConfig.Builder

putParams(String key, String value)

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied.

InputConfig.Builder

removeParams(String key)

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied.

InputConfig.Builder

setGcsSource(GcsSource value)

The Google Cloud Storage location for the input content.

InputConfig.Builder

setGcsSource(GcsSource.Builder builderForValue)

The Google Cloud Storage location for the input content.

Methods inherited from class com.google.protobuf.GeneratedMessage.Builder
addRepeatedField, clearField, clearOneof, clone, getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMutableMapField, isClean, markClean, mergeUnknownFields, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setField, setRepeatedField, setUnknownFields, setUnknownFieldSetBuilder, setUnknownFieldsProto3

Methods inherited from class com.google.protobuf.AbstractMessage.Builder
findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder
addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, mergeFrom, newUninitializedMessageException

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.google.protobuf.Message.Builder
mergeDelimitedFrom, mergeDelimitedFrom

Methods inherited from interface com.google.protobuf.MessageLite.Builder
mergeFrom

Methods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Method Details
- getDescriptor
  
  public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
- internalGetMapFieldReflection
  
  protected com.google.protobuf.MapFieldReflectionAccessor internalGetMapFieldReflection(int number)
  
  Overrides:
  
  internalGetMapFieldReflection in class com.google.protobuf.GeneratedMessage.Builder<InputConfig.Builder>
- internalGetMutableMapFieldReflection
  
  protected com.google.protobuf.MapFieldReflectionAccessor internalGetMutableMapFieldReflection(int number)
  
  Overrides:
  
  internalGetMutableMapFieldReflection in class com.google.protobuf.GeneratedMessage.Builder<InputConfig.Builder>
- internalGetFieldAccessorTable
  
  protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
  
  Specified by:
  
  internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<InputConfig.Builder>
- clear
  
  public InputConfig.Builder clear()
  
  Specified by:
  
  clear in interface com.google.protobuf.Message.Builder
  
  Specified by:
  
  clear in interface com.google.protobuf.MessageLite.Builder
  
  Overrides:
  
  clear in class com.google.protobuf.GeneratedMessage.Builder<InputConfig.Builder>
- getDescriptorForType
  
  public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
  
  Specified by:
  
  getDescriptorForType in interface com.google.protobuf.Message.Builder
  
  Specified by:
  
  getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
  
  Overrides:
  
  getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<InputConfig.Builder>
- getDefaultInstanceForType
  
  public InputConfig getDefaultInstanceForType()
  
  Specified by:
  
  getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
  
  Specified by:
  
  getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder
- build
  
  public InputConfig build()
  
  Specified by:
  
  build in interface com.google.protobuf.Message.Builder
  
  Specified by:
  
  build in interface com.google.protobuf.MessageLite.Builder
- buildPartial
  
  public InputConfig buildPartial()
  
  Specified by:
  
  buildPartial in interface com.google.protobuf.Message.Builder
  
  Specified by:
  
  buildPartial in interface com.google.protobuf.MessageLite.Builder
- mergeFrom
  
  public InputConfig.Builder mergeFrom(com.google.protobuf.Message other)
  
  Specified by:
  
  mergeFrom in interface com.google.protobuf.Message.Builder
  
  Overrides:
  
  mergeFrom in class com.google.protobuf.AbstractMessage.Builder<InputConfig.Builder>
- mergeFrom
  
  public InputConfig.Builder mergeFrom(InputConfig other)
- isInitialized
  
  public final boolean isInitialized()
  
  Specified by:
  
  isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
  
  Overrides:
  
  isInitialized in class com.google.protobuf.GeneratedMessage.Builder<InputConfig.Builder>
- mergeFrom
  
  public InputConfig.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
  
  Specified by:
  
  mergeFrom in interface com.google.protobuf.Message.Builder
  
  Specified by:
  
  mergeFrom in interface com.google.protobuf.MessageLite.Builder
  
  Overrides:
  
  mergeFrom in class com.google.protobuf.AbstractMessage.Builder<InputConfig.Builder>
  
  Throws:
  
  IOException
- getSourceCase
  
  public InputConfig.SourceCase getSourceCase()
  
  Specified by:
  
  getSourceCase in interface InputConfigOrBuilder
- clearSource
  
  public InputConfig.Builder clearSource()
- hasGcsSource
  
  public boolean hasGcsSource()
  The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
  .google.cloud.automl.v1.GcsSource gcs_source = 1;
  Specified by:
  
  hasGcsSource in interface InputConfigOrBuilder
  
  Returns:
  
  Whether the gcsSource field is set.
- getGcsSource
  
  public GcsSource getGcsSource()
  The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
  .google.cloud.automl.v1.GcsSource gcs_source = 1;
  Specified by:
  
  getGcsSource in interface InputConfigOrBuilder
  
  Returns:
  
  The gcsSource.
- setGcsSource
  
  public InputConfig.Builder setGcsSource(GcsSource value)
  The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
  .google.cloud.automl.v1.GcsSource gcs_source = 1;
- setGcsSource
  
  public InputConfig.Builder setGcsSource(GcsSource.Builder builderForValue)
  The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
  .google.cloud.automl.v1.GcsSource gcs_source = 1;
- mergeGcsSource
  
  public InputConfig.Builder mergeGcsSource(GcsSource value)
  The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
  .google.cloud.automl.v1.GcsSource gcs_source = 1;
- clearGcsSource
  
  public InputConfig.Builder clearGcsSource()
  The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
  .google.cloud.automl.v1.GcsSource gcs_source = 1;
- getGcsSourceBuilder
  
  public GcsSource.Builder getGcsSourceBuilder()
  The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
  .google.cloud.automl.v1.GcsSource gcs_source = 1;
- getGcsSourceOrBuilder
  
  public GcsSourceOrBuilder getGcsSourceOrBuilder()
  The Google Cloud Storage location for the input content. For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
  .google.cloud.automl.v1.GcsSource gcs_source = 1;
  Specified by:
  
  getGcsSourceOrBuilder in interface InputConfigOrBuilder
- getParamsCount
  
  public int getParamsCount()
  
  Description copied from interface: InputConfigOrBuilder
  Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: "1".
  map<string, string> params = 2;
  Specified by:
  
  getParamsCount in interface InputConfigOrBuilder
- containsParams
  
  public boolean containsParams(String key)
  Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: "1".
  map<string, string> params = 2;
  Specified by:
  
  containsParams in interface InputConfigOrBuilder
- getParams
  
  @Deprecated public Map<String,String> getParams()
  
  Deprecated.
  
  Use getParamsMap() instead.
  
  Specified by:
  
  getParams 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. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: "1".
  map<string, string> params = 2;
  Specified by:
  
  getParamsMap in interface InputConfigOrBuilder
- getParamsOrDefault
  
  public String getParamsOrDefault(String key, String defaultValue)
  Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: "1".
  map<string, string> params = 2;
  Specified by:
  
  getParamsOrDefault in interface InputConfigOrBuilder
- getParamsOrThrow
  
  public String getParamsOrThrow(String key)
  Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: "1".
  map<string, string> params = 2;
  Specified by:
  
  getParamsOrThrow in interface InputConfigOrBuilder
- clearParams
  
  public InputConfig.Builder clearParams()
- removeParams
  
  public InputConfig.Builder removeParams(String key)
  Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: "1".
  map<string, string> params = 2;
- getMutableParams
  
  @Deprecated public Map<String,String> getMutableParams()
  
  Deprecated.
  
  Use alternate mutation accessors instead.
- putParams
  
  public InputConfig.Builder putParams(String key, String value)
  Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `schema_inference_version` : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: "1".
  map<string, string> params = 2;
- putAllParams
  
  public InputConfig.Builder putAllParams(Map<String,String> values)
  Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long. #### AutoML Tables `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;

Class InputConfig.Builder

Method Summary

Methods inherited from class com.google.protobuf.GeneratedMessage.Builder

Methods inherited from class com.google.protobuf.AbstractMessage.Builder

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder

Methods inherited from class java.lang.Object

Methods inherited from interface com.google.protobuf.Message.Builder

Methods inherited from interface com.google.protobuf.MessageLite.Builder

Methods inherited from interface com.google.protobuf.MessageOrBuilder

Method Details

getDescriptor

internalGetMapFieldReflection

internalGetMutableMapFieldReflection

internalGetFieldAccessorTable

clear

getDescriptorForType

getDefaultInstanceForType

build

buildPartial

mergeFrom

mergeFrom

isInitialized

mergeFrom

getSourceCase

clearSource

hasGcsSource

getGcsSource

setGcsSource

setGcsSource

mergeGcsSource

clearGcsSource

getGcsSourceBuilder

getGcsSourceOrBuilder

getParamsCount

containsParams

getParams

getParamsMap

getParamsOrDefault

getParamsOrThrow

clearParams

removeParams

getMutableParams

putParams

putAllParams