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/*
* Copyright 2024 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: google/cloud/automl/v1beta1/io.proto
// Protobuf Java Version: 3.25.5
package com.google.cloud.automl.v1beta1;
/**
*
*
*
* Output configuration for BatchPredict Action.
*
* As destination the
*
* [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination]
* must be set unless specified otherwise for a domain. If gcs_destination is
* set then in the given directory a new directory is created. Its name
* will be
* "prediction-<model-display-name>-<timestamp-of-prediction-call>",
* where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents
* of it depends on the ML problem the predictions are made for.
*
* * For Image Classification:
* In the created directory files `image_classification_1.jsonl`,
* `image_classification_2.jsonl`,...,`image_classification_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of the successfully predicted images and annotations.
* A single image will be listed only once with all its annotations,
* and its annotations will never be split across files.
* Each .JSONL file will contain, per line, a JSON representation of a
* proto that wraps image's "ID" : "<id_value>" followed by a list of
* zero or more AnnotationPayload protos (called annotations), which
* have classification detail populated.
* If prediction for any image failed (partially or completely), then an
* additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
* files will be created (N depends on total number of failed
* predictions). These files will have a JSON representation of a proto
* that wraps the same "ID" : "<id_value>" but here followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`fields.
*
* * For Image Object Detection:
* In the created directory files `image_object_detection_1.jsonl`,
* `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of the successfully predicted images and annotations.
* Each .JSONL file will contain, per line, a JSON representation of a
* proto that wraps image's "ID" : "<id_value>" followed by a list of
* zero or more AnnotationPayload protos (called annotations), which
* have image_object_detection detail populated. A single image will
* be listed only once with all its annotations, and its annotations
* will never be split across files.
* If prediction for any image failed (partially or completely), then
* additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
* files will be created (N depends on total number of failed
* predictions). These files will have a JSON representation of a proto
* that wraps the same "ID" : "<id_value>" but here followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`fields.
* * For Video Classification:
* In the created directory a video_classification.csv file, and a .JSON
* file per each video classification requested in the input (i.e. each
* line in given CSV(s)), will be created.
*
* The format of video_classification.csv is:
*
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
* where:
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
* the prediction input lines (i.e. video_classification.csv has
* precisely the same number of lines as the prediction input had.)
* JSON_FILE_NAME = Name of .JSON file in the output directory, which
* contains prediction responses for the video time segment.
* STATUS = "OK" if prediction completed successfully, or an error code
* with message otherwise. If STATUS is not "OK" then the .JSON file
* for that line may not exist or be empty.
*
* Each .JSON file, assuming STATUS is "OK", will contain a list of
* AnnotationPayload protos in JSON format, which are the predictions
* for the video time segment the file is assigned to in the
* video_classification.csv. All AnnotationPayload protos will have
* video_classification field set, and will be sorted by
* video_classification.type field (note that the returned types are
* governed by `classifaction_types` parameter in
* [PredictService.BatchPredictRequest.params][]).
*
* * For Video Object Tracking:
* In the created directory a video_object_tracking.csv file will be
* created, and multiple files video_object_trackinng_1.json,
* video_object_trackinng_2.json,..., video_object_trackinng_N.json,
* where N is the number of requests in the input (i.e. the number of
* lines in given CSV(s)).
*
* The format of video_object_tracking.csv is:
*
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
* where:
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
* the prediction input lines (i.e. video_object_tracking.csv has
* precisely the same number of lines as the prediction input had.)
* JSON_FILE_NAME = Name of .JSON file in the output directory, which
* contains prediction responses for the video time segment.
* STATUS = "OK" if prediction completed successfully, or an error
* code with message otherwise. If STATUS is not "OK" then the .JSON
* file for that line may not exist or be empty.
*
* Each .JSON file, assuming STATUS is "OK", will contain a list of
* AnnotationPayload protos in JSON format, which are the predictions
* for each frame of the video time segment the file is assigned to in
* video_object_tracking.csv. All AnnotationPayload protos will have
* video_object_tracking field set.
* * For Text Classification:
* In the created directory files `text_classification_1.jsonl`,
* `text_classification_2.jsonl`,...,`text_classification_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of inputs and annotations found.
*
* Each .JSONL file will contain, per line, a JSON representation of a
* proto that wraps input text snippet or input text file and a list of
* zero or more AnnotationPayload protos (called annotations), which
* have classification detail populated. A single text snippet or file
* will be listed only once with all its annotations, and its
* annotations will never be split across files.
*
* If prediction for any text snippet or file failed (partially or
* completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
* `errors_N.jsonl` files will be created (N depends on total number of
* failed predictions). These files will have a JSON representation of a
* proto that wraps input text snippet or input text file followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`.
*
* * For Text Sentiment:
* In the created directory files `text_sentiment_1.jsonl`,
* `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of inputs and annotations found.
*
* Each .JSONL file will contain, per line, a JSON representation of a
* proto that wraps input text snippet or input text file and a list of
* zero or more AnnotationPayload protos (called annotations), which
* have text_sentiment detail populated. A single text snippet or file
* will be listed only once with all its annotations, and its
* annotations will never be split across files.
*
* If prediction for any text snippet or file failed (partially or
* completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
* `errors_N.jsonl` files will be created (N depends on total number of
* failed predictions). These files will have a JSON representation of a
* proto that wraps input text snippet or input text file followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`.
*
* * For Text Extraction:
* In the created directory files `text_extraction_1.jsonl`,
* `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of inputs and annotations found.
* The contents of these .JSONL file(s) depend on whether the input
* used inline text, or documents.
* If input was inline, then each .JSONL file will contain, per line,
* a JSON representation of a proto that wraps given in request text
* snippet's "id" (if specified), followed by input text snippet,
* and a list of zero or more
* AnnotationPayload protos (called annotations), which have
* text_extraction detail populated. A single text snippet will be
* listed only once with all its annotations, and its annotations will
* never be split across files.
* If input used documents, then each .JSONL file will contain, per
* line, a JSON representation of a proto that wraps given in request
* document proto, followed by its OCR-ed representation in the form
* of a text snippet, finally followed by a list of zero or more
* AnnotationPayload protos (called annotations), which have
* text_extraction detail populated and refer, via their indices, to
* the OCR-ed text snippet. A single document (and its text snippet)
* will be listed only once with all its annotations, and its
* annotations will never be split across files.
* If prediction for any text snippet failed (partially or completely),
* then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
* `errors_N.jsonl` files will be created (N depends on total number of
* failed predictions). These files will have a JSON representation of a
* proto that wraps either the "id" : "<id_value>" (in case of inline)
* or the document proto (in case of document) but here followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`.
*
* * For Tables:
* Output depends on whether
*
* [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination]
* or
*
* [bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination]
* is set (either is allowed).
* GCS case:
* In the created directory files `tables_1.csv`, `tables_2.csv`,...,
* `tables_N.csv` will be created, where N may be 1, and depends on
* the total number of the successfully predicted rows.
* For all CLASSIFICATION
*
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
* Each .csv file will contain a header, listing all columns'
*
* [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
* given on input followed by M target column names in the format of
*
* "<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
*
* [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>_<target
* value>_score" where M is the number of distinct target values,
* i.e. number of distinct values in the target column of the table
* used to train the model. Subsequent lines will contain the
* respective values of successfully predicted rows, with the last,
* i.e. the target, columns having the corresponding prediction
* [scores][google.cloud.automl.v1beta1.TablesAnnotation.score].
* For REGRESSION and FORECASTING
*
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
* Each .csv file will contain a header, listing all columns'
* [display_name-s][google.cloud.automl.v1beta1.display_name] given
* on input followed by the predicted target column with name in the
* format of
*
* "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
*
* [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>"
* Subsequent lines will contain the respective values of
* successfully predicted rows, with the last, i.e. the target,
* column having the predicted target value.
* If prediction for any rows failed, then an additional
* `errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be
* created (N depends on total number of failed rows). These files
* will have analogous format as `tables_*.csv`, but always with a
* single target column having
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* represented as a JSON string, and containing only `code` and
* `message`.
* BigQuery case:
*
* [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination]
* pointing to a BigQuery project must be set. In the given project a
* new dataset will be created with name
* `prediction_<model-display-name>_<timestamp-of-prediction-call>`
* where <model-display-name> will be made
* BigQuery-dataset-name compatible (e.g. most special characters will
* become underscores), and timestamp will be in
* YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset
* two tables will be created, `predictions`, and `errors`.
* The `predictions` table's column names will be the input columns'
*
* [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
* followed by the target column with name in the format of
*
* "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
*
* [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>"
* The input feature columns will contain the respective values of
* successfully predicted rows, with the target column having an
* ARRAY of
*
* [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload],
* represented as STRUCT-s, containing
* [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].
* The `errors` table contains rows for which the prediction has
* failed, it has analogous input columns while the target column name
* is in the format of
*
* "errors_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
*
* [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>",
* and as a value has
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* represented as a STRUCT, and containing only `code` and `message`.
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.BatchPredictOutputConfig}
*/
public final class BatchPredictOutputConfig extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.BatchPredictOutputConfig)
BatchPredictOutputConfigOrBuilder {
private static final long serialVersionUID = 0L;
// Use BatchPredictOutputConfig.newBuilder() to construct.
private BatchPredictOutputConfig(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private BatchPredictOutputConfig() {}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
return new BatchPredictOutputConfig();
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1beta1.Io
.internal_static_google_cloud_automl_v1beta1_BatchPredictOutputConfig_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.automl.v1beta1.Io
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com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.class,
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@SuppressWarnings("serial")
private java.lang.Object destination_;
public enum DestinationCase
implements
com.google.protobuf.Internal.EnumLite,
com.google.protobuf.AbstractMessage.InternalOneOfEnum {
GCS_DESTINATION(1),
BIGQUERY_DESTINATION(2),
DESTINATION_NOT_SET(0);
private final int value;
private DestinationCase(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 DestinationCase valueOf(int value) {
return forNumber(value);
}
public static DestinationCase forNumber(int value) {
switch (value) {
case 1:
return GCS_DESTINATION;
case 2:
return BIGQUERY_DESTINATION;
case 0:
return DESTINATION_NOT_SET;
default:
return null;
}
}
public int getNumber() {
return this.value;
}
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public DestinationCase getDestinationCase() {
return DestinationCase.forNumber(destinationCase_);
}
public static final int GCS_DESTINATION_FIELD_NUMBER = 1;
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*
* @return Whether the gcsDestination field is set.
*/
@java.lang.Override
public boolean hasGcsDestination() {
return destinationCase_ == 1;
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*
* @return The gcsDestination.
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.GcsDestination getGcsDestination() {
if (destinationCase_ == 1) {
return (com.google.cloud.automl.v1beta1.GcsDestination) destination_;
}
return com.google.cloud.automl.v1beta1.GcsDestination.getDefaultInstance();
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.GcsDestinationOrBuilder getGcsDestinationOrBuilder() {
if (destinationCase_ == 1) {
return (com.google.cloud.automl.v1beta1.GcsDestination) destination_;
}
return com.google.cloud.automl.v1beta1.GcsDestination.getDefaultInstance();
}
public static final int BIGQUERY_DESTINATION_FIELD_NUMBER = 2;
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*
* @return Whether the bigqueryDestination field is set.
*/
@java.lang.Override
public boolean hasBigqueryDestination() {
return destinationCase_ == 2;
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*
* @return The bigqueryDestination.
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.BigQueryDestination getBigqueryDestination() {
if (destinationCase_ == 2) {
return (com.google.cloud.automl.v1beta1.BigQueryDestination) destination_;
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return com.google.cloud.automl.v1beta1.BigQueryDestination.getDefaultInstance();
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.BigQueryDestinationOrBuilder
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if (destinationCase_ == 2) {
return (com.google.cloud.automl.v1beta1.BigQueryDestination) destination_;
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return com.google.cloud.automl.v1beta1.BigQueryDestination.getDefaultInstance();
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memoizedIsInitialized = 1;
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if (destinationCase_ == 1) {
output.writeMessage(1, (com.google.cloud.automl.v1beta1.GcsDestination) destination_);
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if (destinationCase_ == 2) {
output.writeMessage(2, (com.google.cloud.automl.v1beta1.BigQueryDestination) destination_);
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getUnknownFields().writeTo(output);
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int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (destinationCase_ == 1) {
size +=
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if (destinationCase_ == 2) {
size +=
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size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
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@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj instanceof com.google.cloud.automl.v1beta1.BatchPredictOutputConfig)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig other =
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if (!getDestinationCase().equals(other.getDestinationCase())) return false;
switch (destinationCase_) {
case 1:
if (!getGcsDestination().equals(other.getGcsDestination())) return false;
break;
case 2:
if (!getBigqueryDestination().equals(other.getBigqueryDestination())) return false;
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if (memoizedHashCode != 0) {
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int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
switch (destinationCase_) {
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hash = (53 * hash) + getGcsDestination().hashCode();
break;
case 2:
hash = (37 * hash) + BIGQUERY_DESTINATION_FIELD_NUMBER;
hash = (53 * hash) + getBigqueryDestination().hashCode();
break;
case 0:
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hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
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public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseFrom(
java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig 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.v1beta1.BatchPredictOutputConfig parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseFrom(
byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseFrom(
java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
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public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
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public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseDelimitedFrom(
java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
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public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseDelimitedFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
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public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig parseFrom(
com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
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public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig 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.v1beta1.BatchPredictOutputConfig 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;
}
/**
*
*
*
* Output configuration for BatchPredict Action.
*
* As destination the
*
* [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination]
* must be set unless specified otherwise for a domain. If gcs_destination is
* set then in the given directory a new directory is created. Its name
* will be
* "prediction-<model-display-name>-<timestamp-of-prediction-call>",
* where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents
* of it depends on the ML problem the predictions are made for.
*
* * For Image Classification:
* In the created directory files `image_classification_1.jsonl`,
* `image_classification_2.jsonl`,...,`image_classification_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of the successfully predicted images and annotations.
* A single image will be listed only once with all its annotations,
* and its annotations will never be split across files.
* Each .JSONL file will contain, per line, a JSON representation of a
* proto that wraps image's "ID" : "<id_value>" followed by a list of
* zero or more AnnotationPayload protos (called annotations), which
* have classification detail populated.
* If prediction for any image failed (partially or completely), then an
* additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
* files will be created (N depends on total number of failed
* predictions). These files will have a JSON representation of a proto
* that wraps the same "ID" : "<id_value>" but here followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`fields.
*
* * For Image Object Detection:
* In the created directory files `image_object_detection_1.jsonl`,
* `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of the successfully predicted images and annotations.
* Each .JSONL file will contain, per line, a JSON representation of a
* proto that wraps image's "ID" : "<id_value>" followed by a list of
* zero or more AnnotationPayload protos (called annotations), which
* have image_object_detection detail populated. A single image will
* be listed only once with all its annotations, and its annotations
* will never be split across files.
* If prediction for any image failed (partially or completely), then
* additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
* files will be created (N depends on total number of failed
* predictions). These files will have a JSON representation of a proto
* that wraps the same "ID" : "<id_value>" but here followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`fields.
* * For Video Classification:
* In the created directory a video_classification.csv file, and a .JSON
* file per each video classification requested in the input (i.e. each
* line in given CSV(s)), will be created.
*
* The format of video_classification.csv is:
*
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
* where:
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
* the prediction input lines (i.e. video_classification.csv has
* precisely the same number of lines as the prediction input had.)
* JSON_FILE_NAME = Name of .JSON file in the output directory, which
* contains prediction responses for the video time segment.
* STATUS = "OK" if prediction completed successfully, or an error code
* with message otherwise. If STATUS is not "OK" then the .JSON file
* for that line may not exist or be empty.
*
* Each .JSON file, assuming STATUS is "OK", will contain a list of
* AnnotationPayload protos in JSON format, which are the predictions
* for the video time segment the file is assigned to in the
* video_classification.csv. All AnnotationPayload protos will have
* video_classification field set, and will be sorted by
* video_classification.type field (note that the returned types are
* governed by `classifaction_types` parameter in
* [PredictService.BatchPredictRequest.params][]).
*
* * For Video Object Tracking:
* In the created directory a video_object_tracking.csv file will be
* created, and multiple files video_object_trackinng_1.json,
* video_object_trackinng_2.json,..., video_object_trackinng_N.json,
* where N is the number of requests in the input (i.e. the number of
* lines in given CSV(s)).
*
* The format of video_object_tracking.csv is:
*
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
* where:
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
* the prediction input lines (i.e. video_object_tracking.csv has
* precisely the same number of lines as the prediction input had.)
* JSON_FILE_NAME = Name of .JSON file in the output directory, which
* contains prediction responses for the video time segment.
* STATUS = "OK" if prediction completed successfully, or an error
* code with message otherwise. If STATUS is not "OK" then the .JSON
* file for that line may not exist or be empty.
*
* Each .JSON file, assuming STATUS is "OK", will contain a list of
* AnnotationPayload protos in JSON format, which are the predictions
* for each frame of the video time segment the file is assigned to in
* video_object_tracking.csv. All AnnotationPayload protos will have
* video_object_tracking field set.
* * For Text Classification:
* In the created directory files `text_classification_1.jsonl`,
* `text_classification_2.jsonl`,...,`text_classification_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of inputs and annotations found.
*
* Each .JSONL file will contain, per line, a JSON representation of a
* proto that wraps input text snippet or input text file and a list of
* zero or more AnnotationPayload protos (called annotations), which
* have classification detail populated. A single text snippet or file
* will be listed only once with all its annotations, and its
* annotations will never be split across files.
*
* If prediction for any text snippet or file failed (partially or
* completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
* `errors_N.jsonl` files will be created (N depends on total number of
* failed predictions). These files will have a JSON representation of a
* proto that wraps input text snippet or input text file followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`.
*
* * For Text Sentiment:
* In the created directory files `text_sentiment_1.jsonl`,
* `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of inputs and annotations found.
*
* Each .JSONL file will contain, per line, a JSON representation of a
* proto that wraps input text snippet or input text file and a list of
* zero or more AnnotationPayload protos (called annotations), which
* have text_sentiment detail populated. A single text snippet or file
* will be listed only once with all its annotations, and its
* annotations will never be split across files.
*
* If prediction for any text snippet or file failed (partially or
* completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
* `errors_N.jsonl` files will be created (N depends on total number of
* failed predictions). These files will have a JSON representation of a
* proto that wraps input text snippet or input text file followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`.
*
* * For Text Extraction:
* In the created directory files `text_extraction_1.jsonl`,
* `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl`
* will be created, where N may be 1, and depends on the
* total number of inputs and annotations found.
* The contents of these .JSONL file(s) depend on whether the input
* used inline text, or documents.
* If input was inline, then each .JSONL file will contain, per line,
* a JSON representation of a proto that wraps given in request text
* snippet's "id" (if specified), followed by input text snippet,
* and a list of zero or more
* AnnotationPayload protos (called annotations), which have
* text_extraction detail populated. A single text snippet will be
* listed only once with all its annotations, and its annotations will
* never be split across files.
* If input used documents, then each .JSONL file will contain, per
* line, a JSON representation of a proto that wraps given in request
* document proto, followed by its OCR-ed representation in the form
* of a text snippet, finally followed by a list of zero or more
* AnnotationPayload protos (called annotations), which have
* text_extraction detail populated and refer, via their indices, to
* the OCR-ed text snippet. A single document (and its text snippet)
* will be listed only once with all its annotations, and its
* annotations will never be split across files.
* If prediction for any text snippet failed (partially or completely),
* then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
* `errors_N.jsonl` files will be created (N depends on total number of
* failed predictions). These files will have a JSON representation of a
* proto that wraps either the "id" : "<id_value>" (in case of inline)
* or the document proto (in case of document) but here followed by
* exactly one
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* containing only `code` and `message`.
*
* * For Tables:
* Output depends on whether
*
* [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination]
* or
*
* [bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination]
* is set (either is allowed).
* GCS case:
* In the created directory files `tables_1.csv`, `tables_2.csv`,...,
* `tables_N.csv` will be created, where N may be 1, and depends on
* the total number of the successfully predicted rows.
* For all CLASSIFICATION
*
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
* Each .csv file will contain a header, listing all columns'
*
* [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
* given on input followed by M target column names in the format of
*
* "<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
*
* [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>_<target
* value>_score" where M is the number of distinct target values,
* i.e. number of distinct values in the target column of the table
* used to train the model. Subsequent lines will contain the
* respective values of successfully predicted rows, with the last,
* i.e. the target, columns having the corresponding prediction
* [scores][google.cloud.automl.v1beta1.TablesAnnotation.score].
* For REGRESSION and FORECASTING
*
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
* Each .csv file will contain a header, listing all columns'
* [display_name-s][google.cloud.automl.v1beta1.display_name] given
* on input followed by the predicted target column with name in the
* format of
*
* "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
*
* [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>"
* Subsequent lines will contain the respective values of
* successfully predicted rows, with the last, i.e. the target,
* column having the predicted target value.
* If prediction for any rows failed, then an additional
* `errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be
* created (N depends on total number of failed rows). These files
* will have analogous format as `tables_*.csv`, but always with a
* single target column having
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* represented as a JSON string, and containing only `code` and
* `message`.
* BigQuery case:
*
* [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination]
* pointing to a BigQuery project must be set. In the given project a
* new dataset will be created with name
* `prediction_<model-display-name>_<timestamp-of-prediction-call>`
* where <model-display-name> will be made
* BigQuery-dataset-name compatible (e.g. most special characters will
* become underscores), and timestamp will be in
* YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset
* two tables will be created, `predictions`, and `errors`.
* The `predictions` table's column names will be the input columns'
*
* [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
* followed by the target column with name in the format of
*
* "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
*
* [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>"
* The input feature columns will contain the respective values of
* successfully predicted rows, with the target column having an
* ARRAY of
*
* [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload],
* represented as STRUCT-s, containing
* [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].
* The `errors` table contains rows for which the prediction has
* failed, it has analogous input columns while the target column name
* is in the format of
*
* "errors_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
*
* [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>",
* and as a value has
*
* [`google.rpc.Status`](https:
* //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
* represented as a STRUCT, and containing only `code` and `message`.
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.BatchPredictOutputConfig}
*/
public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder
implements
// @@protoc_insertion_point(builder_implements:google.cloud.automl.v1beta1.BatchPredictOutputConfig)
com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder {
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return com.google.cloud.automl.v1beta1.Io
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// Construct using com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.newBuilder()
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@java.lang.Override
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
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@java.lang.Override
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfig buildPartial() {
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if (bitField0_ != 0) {
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buildPartialOneofs(result);
onBuilt();
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private void buildPartial0(com.google.cloud.automl.v1beta1.BatchPredictOutputConfig result) {
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private void buildPartialOneofs(
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@java.lang.Override
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public Builder setRepeatedField(
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@java.lang.Override
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if (other instanceof com.google.cloud.automl.v1beta1.BatchPredictOutputConfig) {
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super.mergeFrom(other);
return this;
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public Builder mergeFrom(com.google.cloud.automl.v1beta1.BatchPredictOutputConfig other) {
if (other == com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance())
return this;
switch (other.getDestinationCase()) {
case GCS_DESTINATION:
{
mergeGcsDestination(other.getGcsDestination());
break;
}
case BIGQUERY_DESTINATION:
{
mergeBigqueryDestination(other.getBigqueryDestination());
break;
}
case DESTINATION_NOT_SET:
{
break;
}
}
this.mergeUnknownFields(other.getUnknownFields());
onChanged();
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@java.lang.Override
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@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();
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case 10:
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break;
} // case 10
case 18:
{
input.readMessage(
getBigqueryDestinationFieldBuilder().getBuilder(), extensionRegistry);
destinationCase_ = 2;
break;
} // case 18
default:
{
if (!super.parseUnknownField(input, extensionRegistry, tag)) {
done = true; // was an endgroup tag
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} // default:
} // switch (tag)
} // while (!done)
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onChanged();
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private int destinationCase_ = 0;
private java.lang.Object destination_;
public DestinationCase getDestinationCase() {
return DestinationCase.forNumber(destinationCase_);
}
public Builder clearDestination() {
destinationCase_ = 0;
destination_ = null;
onChanged();
return this;
}
private int bitField0_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.GcsDestination,
com.google.cloud.automl.v1beta1.GcsDestination.Builder,
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gcsDestinationBuilder_;
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*
* @return Whether the gcsDestination field is set.
*/
@java.lang.Override
public boolean hasGcsDestination() {
return destinationCase_ == 1;
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*
* @return The gcsDestination.
*/
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if (gcsDestinationBuilder_ == null) {
if (destinationCase_ == 1) {
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if (destinationCase_ == 1) {
return gcsDestinationBuilder_.getMessage();
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return com.google.cloud.automl.v1beta1.GcsDestination.getDefaultInstance();
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}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*/
public Builder setGcsDestination(com.google.cloud.automl.v1beta1.GcsDestination value) {
if (gcsDestinationBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
destination_ = value;
onChanged();
} else {
gcsDestinationBuilder_.setMessage(value);
}
destinationCase_ = 1;
return this;
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*/
public Builder setGcsDestination(
com.google.cloud.automl.v1beta1.GcsDestination.Builder builderForValue) {
if (gcsDestinationBuilder_ == null) {
destination_ = builderForValue.build();
onChanged();
} else {
gcsDestinationBuilder_.setMessage(builderForValue.build());
}
destinationCase_ = 1;
return this;
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*/
public Builder mergeGcsDestination(com.google.cloud.automl.v1beta1.GcsDestination value) {
if (gcsDestinationBuilder_ == null) {
if (destinationCase_ == 1
&& destination_
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destination_ =
com.google.cloud.automl.v1beta1.GcsDestination.newBuilder(
(com.google.cloud.automl.v1beta1.GcsDestination) destination_)
.mergeFrom(value)
.buildPartial();
} else {
destination_ = value;
}
onChanged();
} else {
if (destinationCase_ == 1) {
gcsDestinationBuilder_.mergeFrom(value);
} else {
gcsDestinationBuilder_.setMessage(value);
}
}
destinationCase_ = 1;
return this;
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*/
public Builder clearGcsDestination() {
if (gcsDestinationBuilder_ == null) {
if (destinationCase_ == 1) {
destinationCase_ = 0;
destination_ = null;
onChanged();
}
} else {
if (destinationCase_ == 1) {
destinationCase_ = 0;
destination_ = null;
}
gcsDestinationBuilder_.clear();
}
return this;
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*/
public com.google.cloud.automl.v1beta1.GcsDestination.Builder getGcsDestinationBuilder() {
return getGcsDestinationFieldBuilder().getBuilder();
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.GcsDestinationOrBuilder getGcsDestinationOrBuilder() {
if ((destinationCase_ == 1) && (gcsDestinationBuilder_ != null)) {
return gcsDestinationBuilder_.getMessageOrBuilder();
} else {
if (destinationCase_ == 1) {
return (com.google.cloud.automl.v1beta1.GcsDestination) destination_;
}
return com.google.cloud.automl.v1beta1.GcsDestination.getDefaultInstance();
}
}
/**
*
*
*
* The Google Cloud Storage location of the directory where the output is to
* be written to.
*
*
* .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.GcsDestination,
com.google.cloud.automl.v1beta1.GcsDestination.Builder,
com.google.cloud.automl.v1beta1.GcsDestinationOrBuilder>
getGcsDestinationFieldBuilder() {
if (gcsDestinationBuilder_ == null) {
if (!(destinationCase_ == 1)) {
destination_ = com.google.cloud.automl.v1beta1.GcsDestination.getDefaultInstance();
}
gcsDestinationBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.GcsDestination,
com.google.cloud.automl.v1beta1.GcsDestination.Builder,
com.google.cloud.automl.v1beta1.GcsDestinationOrBuilder>(
(com.google.cloud.automl.v1beta1.GcsDestination) destination_,
getParentForChildren(),
isClean());
destination_ = null;
}
destinationCase_ = 1;
onChanged();
return gcsDestinationBuilder_;
}
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BigQueryDestination,
com.google.cloud.automl.v1beta1.BigQueryDestination.Builder,
com.google.cloud.automl.v1beta1.BigQueryDestinationOrBuilder>
bigqueryDestinationBuilder_;
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*
* @return Whether the bigqueryDestination field is set.
*/
@java.lang.Override
public boolean hasBigqueryDestination() {
return destinationCase_ == 2;
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*
* @return The bigqueryDestination.
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.BigQueryDestination getBigqueryDestination() {
if (bigqueryDestinationBuilder_ == null) {
if (destinationCase_ == 2) {
return (com.google.cloud.automl.v1beta1.BigQueryDestination) destination_;
}
return com.google.cloud.automl.v1beta1.BigQueryDestination.getDefaultInstance();
} else {
if (destinationCase_ == 2) {
return bigqueryDestinationBuilder_.getMessage();
}
return com.google.cloud.automl.v1beta1.BigQueryDestination.getDefaultInstance();
}
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*/
public Builder setBigqueryDestination(
com.google.cloud.automl.v1beta1.BigQueryDestination value) {
if (bigqueryDestinationBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
destination_ = value;
onChanged();
} else {
bigqueryDestinationBuilder_.setMessage(value);
}
destinationCase_ = 2;
return this;
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*/
public Builder setBigqueryDestination(
com.google.cloud.automl.v1beta1.BigQueryDestination.Builder builderForValue) {
if (bigqueryDestinationBuilder_ == null) {
destination_ = builderForValue.build();
onChanged();
} else {
bigqueryDestinationBuilder_.setMessage(builderForValue.build());
}
destinationCase_ = 2;
return this;
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*/
public Builder mergeBigqueryDestination(
com.google.cloud.automl.v1beta1.BigQueryDestination value) {
if (bigqueryDestinationBuilder_ == null) {
if (destinationCase_ == 2
&& destination_
!= com.google.cloud.automl.v1beta1.BigQueryDestination.getDefaultInstance()) {
destination_ =
com.google.cloud.automl.v1beta1.BigQueryDestination.newBuilder(
(com.google.cloud.automl.v1beta1.BigQueryDestination) destination_)
.mergeFrom(value)
.buildPartial();
} else {
destination_ = value;
}
onChanged();
} else {
if (destinationCase_ == 2) {
bigqueryDestinationBuilder_.mergeFrom(value);
} else {
bigqueryDestinationBuilder_.setMessage(value);
}
}
destinationCase_ = 2;
return this;
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*/
public Builder clearBigqueryDestination() {
if (bigqueryDestinationBuilder_ == null) {
if (destinationCase_ == 2) {
destinationCase_ = 0;
destination_ = null;
onChanged();
}
} else {
if (destinationCase_ == 2) {
destinationCase_ = 0;
destination_ = null;
}
bigqueryDestinationBuilder_.clear();
}
return this;
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*/
public com.google.cloud.automl.v1beta1.BigQueryDestination.Builder
getBigqueryDestinationBuilder() {
return getBigqueryDestinationFieldBuilder().getBuilder();
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.BigQueryDestinationOrBuilder
getBigqueryDestinationOrBuilder() {
if ((destinationCase_ == 2) && (bigqueryDestinationBuilder_ != null)) {
return bigqueryDestinationBuilder_.getMessageOrBuilder();
} else {
if (destinationCase_ == 2) {
return (com.google.cloud.automl.v1beta1.BigQueryDestination) destination_;
}
return com.google.cloud.automl.v1beta1.BigQueryDestination.getDefaultInstance();
}
}
/**
*
*
*
* The BigQuery location where the output is to be written to.
*
*
* .google.cloud.automl.v1beta1.BigQueryDestination bigquery_destination = 2;
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BigQueryDestination,
com.google.cloud.automl.v1beta1.BigQueryDestination.Builder,
com.google.cloud.automl.v1beta1.BigQueryDestinationOrBuilder>
getBigqueryDestinationFieldBuilder() {
if (bigqueryDestinationBuilder_ == null) {
if (!(destinationCase_ == 2)) {
destination_ = com.google.cloud.automl.v1beta1.BigQueryDestination.getDefaultInstance();
}
bigqueryDestinationBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BigQueryDestination,
com.google.cloud.automl.v1beta1.BigQueryDestination.Builder,
com.google.cloud.automl.v1beta1.BigQueryDestinationOrBuilder>(
(com.google.cloud.automl.v1beta1.BigQueryDestination) destination_,
getParentForChildren(),
isClean());
destination_ = null;
}
destinationCase_ = 2;
onChanged();
return bigqueryDestinationBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.BatchPredictOutputConfig)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.BatchPredictOutputConfig)
private static final com.google.cloud.automl.v1beta1.BatchPredictOutputConfig DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.cloud.automl.v1beta1.BatchPredictOutputConfig();
}
public static com.google.cloud.automl.v1beta1.BatchPredictOutputConfig getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser PARSER =
new com.google.protobuf.AbstractParser() {
@java.lang.Override
public BatchPredictOutputConfig 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.v1beta1.BatchPredictOutputConfig getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}