1 /* 2 * Copyright 2020 Google LLC 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * https://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 // Generated by the protocol buffer compiler. DO NOT EDIT! 17 // source: google/cloud/aiplatform/v1/training_pipeline.proto 18 19 package com.google.cloud.aiplatform.v1; 20 21 /** 22 * 23 * 24 * <pre> 25 * Specifies Vertex AI owned input data to be used for training, and 26 * possibly evaluating, the Model. 27 * </pre> 28 * 29 * Protobuf type {@code google.cloud.aiplatform.v1.InputDataConfig} 30 */ 31 public final class InputDataConfig extends com.google.protobuf.GeneratedMessageV3 32 implements 33 // @@protoc_insertion_point(message_implements:google.cloud.aiplatform.v1.InputDataConfig) 34 InputDataConfigOrBuilder { 35 private static final long serialVersionUID = 0L; 36 // Use InputDataConfig.newBuilder() to construct. InputDataConfig(com.google.protobuf.GeneratedMessageV3.Builder<?> builder)37 private InputDataConfig(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) { 38 super(builder); 39 } 40 InputDataConfig()41 private InputDataConfig() { 42 datasetId_ = ""; 43 annotationsFilter_ = ""; 44 annotationSchemaUri_ = ""; 45 savedQueryId_ = ""; 46 } 47 48 @java.lang.Override 49 @SuppressWarnings({"unused"}) newInstance(UnusedPrivateParameter unused)50 protected java.lang.Object newInstance(UnusedPrivateParameter unused) { 51 return new InputDataConfig(); 52 } 53 54 @java.lang.Override getUnknownFields()55 public final com.google.protobuf.UnknownFieldSet getUnknownFields() { 56 return this.unknownFields; 57 } 58 getDescriptor()59 public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { 60 return com.google.cloud.aiplatform.v1.TrainingPipelineProto 61 .internal_static_google_cloud_aiplatform_v1_InputDataConfig_descriptor; 62 } 63 64 @java.lang.Override 65 protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()66 internalGetFieldAccessorTable() { 67 return com.google.cloud.aiplatform.v1.TrainingPipelineProto 68 .internal_static_google_cloud_aiplatform_v1_InputDataConfig_fieldAccessorTable 69 .ensureFieldAccessorsInitialized( 70 com.google.cloud.aiplatform.v1.InputDataConfig.class, 71 com.google.cloud.aiplatform.v1.InputDataConfig.Builder.class); 72 } 73 74 private int splitCase_ = 0; 75 private java.lang.Object split_; 76 77 public enum SplitCase 78 implements 79 com.google.protobuf.Internal.EnumLite, 80 com.google.protobuf.AbstractMessage.InternalOneOfEnum { 81 FRACTION_SPLIT(2), 82 FILTER_SPLIT(3), 83 PREDEFINED_SPLIT(4), 84 TIMESTAMP_SPLIT(5), 85 STRATIFIED_SPLIT(12), 86 SPLIT_NOT_SET(0); 87 private final int value; 88 SplitCase(int value)89 private SplitCase(int value) { 90 this.value = value; 91 } 92 /** 93 * @param value The number of the enum to look for. 94 * @return The enum associated with the given number. 95 * @deprecated Use {@link #forNumber(int)} instead. 96 */ 97 @java.lang.Deprecated valueOf(int value)98 public static SplitCase valueOf(int value) { 99 return forNumber(value); 100 } 101 forNumber(int value)102 public static SplitCase forNumber(int value) { 103 switch (value) { 104 case 2: 105 return FRACTION_SPLIT; 106 case 3: 107 return FILTER_SPLIT; 108 case 4: 109 return PREDEFINED_SPLIT; 110 case 5: 111 return TIMESTAMP_SPLIT; 112 case 12: 113 return STRATIFIED_SPLIT; 114 case 0: 115 return SPLIT_NOT_SET; 116 default: 117 return null; 118 } 119 } 120 getNumber()121 public int getNumber() { 122 return this.value; 123 } 124 }; 125 getSplitCase()126 public SplitCase getSplitCase() { 127 return SplitCase.forNumber(splitCase_); 128 } 129 130 private int destinationCase_ = 0; 131 private java.lang.Object destination_; 132 133 public enum DestinationCase 134 implements 135 com.google.protobuf.Internal.EnumLite, 136 com.google.protobuf.AbstractMessage.InternalOneOfEnum { 137 GCS_DESTINATION(8), 138 BIGQUERY_DESTINATION(10), 139 DESTINATION_NOT_SET(0); 140 private final int value; 141 DestinationCase(int value)142 private DestinationCase(int value) { 143 this.value = value; 144 } 145 /** 146 * @param value The number of the enum to look for. 147 * @return The enum associated with the given number. 148 * @deprecated Use {@link #forNumber(int)} instead. 149 */ 150 @java.lang.Deprecated valueOf(int value)151 public static DestinationCase valueOf(int value) { 152 return forNumber(value); 153 } 154 forNumber(int value)155 public static DestinationCase forNumber(int value) { 156 switch (value) { 157 case 8: 158 return GCS_DESTINATION; 159 case 10: 160 return BIGQUERY_DESTINATION; 161 case 0: 162 return DESTINATION_NOT_SET; 163 default: 164 return null; 165 } 166 } 167 getNumber()168 public int getNumber() { 169 return this.value; 170 } 171 }; 172 getDestinationCase()173 public DestinationCase getDestinationCase() { 174 return DestinationCase.forNumber(destinationCase_); 175 } 176 177 public static final int FRACTION_SPLIT_FIELD_NUMBER = 2; 178 /** 179 * 180 * 181 * <pre> 182 * Split based on fractions defining the size of each set. 183 * </pre> 184 * 185 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 186 * 187 * @return Whether the fractionSplit field is set. 188 */ 189 @java.lang.Override hasFractionSplit()190 public boolean hasFractionSplit() { 191 return splitCase_ == 2; 192 } 193 /** 194 * 195 * 196 * <pre> 197 * Split based on fractions defining the size of each set. 198 * </pre> 199 * 200 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 201 * 202 * @return The fractionSplit. 203 */ 204 @java.lang.Override getFractionSplit()205 public com.google.cloud.aiplatform.v1.FractionSplit getFractionSplit() { 206 if (splitCase_ == 2) { 207 return (com.google.cloud.aiplatform.v1.FractionSplit) split_; 208 } 209 return com.google.cloud.aiplatform.v1.FractionSplit.getDefaultInstance(); 210 } 211 /** 212 * 213 * 214 * <pre> 215 * Split based on fractions defining the size of each set. 216 * </pre> 217 * 218 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 219 */ 220 @java.lang.Override getFractionSplitOrBuilder()221 public com.google.cloud.aiplatform.v1.FractionSplitOrBuilder getFractionSplitOrBuilder() { 222 if (splitCase_ == 2) { 223 return (com.google.cloud.aiplatform.v1.FractionSplit) split_; 224 } 225 return com.google.cloud.aiplatform.v1.FractionSplit.getDefaultInstance(); 226 } 227 228 public static final int FILTER_SPLIT_FIELD_NUMBER = 3; 229 /** 230 * 231 * 232 * <pre> 233 * Split based on the provided filters for each set. 234 * </pre> 235 * 236 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 237 * 238 * @return Whether the filterSplit field is set. 239 */ 240 @java.lang.Override hasFilterSplit()241 public boolean hasFilterSplit() { 242 return splitCase_ == 3; 243 } 244 /** 245 * 246 * 247 * <pre> 248 * Split based on the provided filters for each set. 249 * </pre> 250 * 251 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 252 * 253 * @return The filterSplit. 254 */ 255 @java.lang.Override getFilterSplit()256 public com.google.cloud.aiplatform.v1.FilterSplit getFilterSplit() { 257 if (splitCase_ == 3) { 258 return (com.google.cloud.aiplatform.v1.FilterSplit) split_; 259 } 260 return com.google.cloud.aiplatform.v1.FilterSplit.getDefaultInstance(); 261 } 262 /** 263 * 264 * 265 * <pre> 266 * Split based on the provided filters for each set. 267 * </pre> 268 * 269 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 270 */ 271 @java.lang.Override getFilterSplitOrBuilder()272 public com.google.cloud.aiplatform.v1.FilterSplitOrBuilder getFilterSplitOrBuilder() { 273 if (splitCase_ == 3) { 274 return (com.google.cloud.aiplatform.v1.FilterSplit) split_; 275 } 276 return com.google.cloud.aiplatform.v1.FilterSplit.getDefaultInstance(); 277 } 278 279 public static final int PREDEFINED_SPLIT_FIELD_NUMBER = 4; 280 /** 281 * 282 * 283 * <pre> 284 * Supported only for tabular Datasets. 285 * Split based on a predefined key. 286 * </pre> 287 * 288 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 289 * 290 * @return Whether the predefinedSplit field is set. 291 */ 292 @java.lang.Override hasPredefinedSplit()293 public boolean hasPredefinedSplit() { 294 return splitCase_ == 4; 295 } 296 /** 297 * 298 * 299 * <pre> 300 * Supported only for tabular Datasets. 301 * Split based on a predefined key. 302 * </pre> 303 * 304 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 305 * 306 * @return The predefinedSplit. 307 */ 308 @java.lang.Override getPredefinedSplit()309 public com.google.cloud.aiplatform.v1.PredefinedSplit getPredefinedSplit() { 310 if (splitCase_ == 4) { 311 return (com.google.cloud.aiplatform.v1.PredefinedSplit) split_; 312 } 313 return com.google.cloud.aiplatform.v1.PredefinedSplit.getDefaultInstance(); 314 } 315 /** 316 * 317 * 318 * <pre> 319 * Supported only for tabular Datasets. 320 * Split based on a predefined key. 321 * </pre> 322 * 323 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 324 */ 325 @java.lang.Override getPredefinedSplitOrBuilder()326 public com.google.cloud.aiplatform.v1.PredefinedSplitOrBuilder getPredefinedSplitOrBuilder() { 327 if (splitCase_ == 4) { 328 return (com.google.cloud.aiplatform.v1.PredefinedSplit) split_; 329 } 330 return com.google.cloud.aiplatform.v1.PredefinedSplit.getDefaultInstance(); 331 } 332 333 public static final int TIMESTAMP_SPLIT_FIELD_NUMBER = 5; 334 /** 335 * 336 * 337 * <pre> 338 * Supported only for tabular Datasets. 339 * Split based on the timestamp of the input data pieces. 340 * </pre> 341 * 342 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 343 * 344 * @return Whether the timestampSplit field is set. 345 */ 346 @java.lang.Override hasTimestampSplit()347 public boolean hasTimestampSplit() { 348 return splitCase_ == 5; 349 } 350 /** 351 * 352 * 353 * <pre> 354 * Supported only for tabular Datasets. 355 * Split based on the timestamp of the input data pieces. 356 * </pre> 357 * 358 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 359 * 360 * @return The timestampSplit. 361 */ 362 @java.lang.Override getTimestampSplit()363 public com.google.cloud.aiplatform.v1.TimestampSplit getTimestampSplit() { 364 if (splitCase_ == 5) { 365 return (com.google.cloud.aiplatform.v1.TimestampSplit) split_; 366 } 367 return com.google.cloud.aiplatform.v1.TimestampSplit.getDefaultInstance(); 368 } 369 /** 370 * 371 * 372 * <pre> 373 * Supported only for tabular Datasets. 374 * Split based on the timestamp of the input data pieces. 375 * </pre> 376 * 377 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 378 */ 379 @java.lang.Override getTimestampSplitOrBuilder()380 public com.google.cloud.aiplatform.v1.TimestampSplitOrBuilder getTimestampSplitOrBuilder() { 381 if (splitCase_ == 5) { 382 return (com.google.cloud.aiplatform.v1.TimestampSplit) split_; 383 } 384 return com.google.cloud.aiplatform.v1.TimestampSplit.getDefaultInstance(); 385 } 386 387 public static final int STRATIFIED_SPLIT_FIELD_NUMBER = 12; 388 /** 389 * 390 * 391 * <pre> 392 * Supported only for tabular Datasets. 393 * Split based on the distribution of the specified column. 394 * </pre> 395 * 396 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 397 * 398 * @return Whether the stratifiedSplit field is set. 399 */ 400 @java.lang.Override hasStratifiedSplit()401 public boolean hasStratifiedSplit() { 402 return splitCase_ == 12; 403 } 404 /** 405 * 406 * 407 * <pre> 408 * Supported only for tabular Datasets. 409 * Split based on the distribution of the specified column. 410 * </pre> 411 * 412 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 413 * 414 * @return The stratifiedSplit. 415 */ 416 @java.lang.Override getStratifiedSplit()417 public com.google.cloud.aiplatform.v1.StratifiedSplit getStratifiedSplit() { 418 if (splitCase_ == 12) { 419 return (com.google.cloud.aiplatform.v1.StratifiedSplit) split_; 420 } 421 return com.google.cloud.aiplatform.v1.StratifiedSplit.getDefaultInstance(); 422 } 423 /** 424 * 425 * 426 * <pre> 427 * Supported only for tabular Datasets. 428 * Split based on the distribution of the specified column. 429 * </pre> 430 * 431 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 432 */ 433 @java.lang.Override getStratifiedSplitOrBuilder()434 public com.google.cloud.aiplatform.v1.StratifiedSplitOrBuilder getStratifiedSplitOrBuilder() { 435 if (splitCase_ == 12) { 436 return (com.google.cloud.aiplatform.v1.StratifiedSplit) split_; 437 } 438 return com.google.cloud.aiplatform.v1.StratifiedSplit.getDefaultInstance(); 439 } 440 441 public static final int GCS_DESTINATION_FIELD_NUMBER = 8; 442 /** 443 * 444 * 445 * <pre> 446 * The Cloud Storage location where the training data is to be 447 * written to. In the given directory a new directory is created with 448 * name: 449 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 450 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 451 * All training input data is written into that directory. 452 * The Vertex AI environment variables representing Cloud Storage 453 * data URIs are represented in the Cloud Storage wildcard 454 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 455 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 456 * * AIP_TRAINING_DATA_URI = 457 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 458 * * AIP_VALIDATION_DATA_URI = 459 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 460 * * AIP_TEST_DATA_URI = 461 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 462 * </pre> 463 * 464 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 465 * 466 * @return Whether the gcsDestination field is set. 467 */ 468 @java.lang.Override hasGcsDestination()469 public boolean hasGcsDestination() { 470 return destinationCase_ == 8; 471 } 472 /** 473 * 474 * 475 * <pre> 476 * The Cloud Storage location where the training data is to be 477 * written to. In the given directory a new directory is created with 478 * name: 479 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 480 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 481 * All training input data is written into that directory. 482 * The Vertex AI environment variables representing Cloud Storage 483 * data URIs are represented in the Cloud Storage wildcard 484 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 485 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 486 * * AIP_TRAINING_DATA_URI = 487 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 488 * * AIP_VALIDATION_DATA_URI = 489 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 490 * * AIP_TEST_DATA_URI = 491 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 492 * </pre> 493 * 494 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 495 * 496 * @return The gcsDestination. 497 */ 498 @java.lang.Override getGcsDestination()499 public com.google.cloud.aiplatform.v1.GcsDestination getGcsDestination() { 500 if (destinationCase_ == 8) { 501 return (com.google.cloud.aiplatform.v1.GcsDestination) destination_; 502 } 503 return com.google.cloud.aiplatform.v1.GcsDestination.getDefaultInstance(); 504 } 505 /** 506 * 507 * 508 * <pre> 509 * The Cloud Storage location where the training data is to be 510 * written to. In the given directory a new directory is created with 511 * name: 512 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 513 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 514 * All training input data is written into that directory. 515 * The Vertex AI environment variables representing Cloud Storage 516 * data URIs are represented in the Cloud Storage wildcard 517 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 518 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 519 * * AIP_TRAINING_DATA_URI = 520 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 521 * * AIP_VALIDATION_DATA_URI = 522 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 523 * * AIP_TEST_DATA_URI = 524 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 525 * </pre> 526 * 527 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 528 */ 529 @java.lang.Override getGcsDestinationOrBuilder()530 public com.google.cloud.aiplatform.v1.GcsDestinationOrBuilder getGcsDestinationOrBuilder() { 531 if (destinationCase_ == 8) { 532 return (com.google.cloud.aiplatform.v1.GcsDestination) destination_; 533 } 534 return com.google.cloud.aiplatform.v1.GcsDestination.getDefaultInstance(); 535 } 536 537 public static final int BIGQUERY_DESTINATION_FIELD_NUMBER = 10; 538 /** 539 * 540 * 541 * <pre> 542 * Only applicable to custom training with tabular Dataset with BigQuery 543 * source. 544 * The BigQuery project location where the training data is to be written 545 * to. In the given project a new dataset is created with name 546 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 547 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 548 * input data is written into that dataset. In the dataset three 549 * tables are created, `training`, `validation` and `test`. 550 * * AIP_DATA_FORMAT = "bigquery". 551 * * AIP_TRAINING_DATA_URI = 552 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 553 * * AIP_VALIDATION_DATA_URI = 554 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 555 * * AIP_TEST_DATA_URI = 556 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 557 * </pre> 558 * 559 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 560 * 561 * @return Whether the bigqueryDestination field is set. 562 */ 563 @java.lang.Override hasBigqueryDestination()564 public boolean hasBigqueryDestination() { 565 return destinationCase_ == 10; 566 } 567 /** 568 * 569 * 570 * <pre> 571 * Only applicable to custom training with tabular Dataset with BigQuery 572 * source. 573 * The BigQuery project location where the training data is to be written 574 * to. In the given project a new dataset is created with name 575 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 576 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 577 * input data is written into that dataset. In the dataset three 578 * tables are created, `training`, `validation` and `test`. 579 * * AIP_DATA_FORMAT = "bigquery". 580 * * AIP_TRAINING_DATA_URI = 581 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 582 * * AIP_VALIDATION_DATA_URI = 583 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 584 * * AIP_TEST_DATA_URI = 585 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 586 * </pre> 587 * 588 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 589 * 590 * @return The bigqueryDestination. 591 */ 592 @java.lang.Override getBigqueryDestination()593 public com.google.cloud.aiplatform.v1.BigQueryDestination getBigqueryDestination() { 594 if (destinationCase_ == 10) { 595 return (com.google.cloud.aiplatform.v1.BigQueryDestination) destination_; 596 } 597 return com.google.cloud.aiplatform.v1.BigQueryDestination.getDefaultInstance(); 598 } 599 /** 600 * 601 * 602 * <pre> 603 * Only applicable to custom training with tabular Dataset with BigQuery 604 * source. 605 * The BigQuery project location where the training data is to be written 606 * to. In the given project a new dataset is created with name 607 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 608 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 609 * input data is written into that dataset. In the dataset three 610 * tables are created, `training`, `validation` and `test`. 611 * * AIP_DATA_FORMAT = "bigquery". 612 * * AIP_TRAINING_DATA_URI = 613 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 614 * * AIP_VALIDATION_DATA_URI = 615 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 616 * * AIP_TEST_DATA_URI = 617 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 618 * </pre> 619 * 620 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 621 */ 622 @java.lang.Override 623 public com.google.cloud.aiplatform.v1.BigQueryDestinationOrBuilder getBigqueryDestinationOrBuilder()624 getBigqueryDestinationOrBuilder() { 625 if (destinationCase_ == 10) { 626 return (com.google.cloud.aiplatform.v1.BigQueryDestination) destination_; 627 } 628 return com.google.cloud.aiplatform.v1.BigQueryDestination.getDefaultInstance(); 629 } 630 631 public static final int DATASET_ID_FIELD_NUMBER = 1; 632 633 @SuppressWarnings("serial") 634 private volatile java.lang.Object datasetId_ = ""; 635 /** 636 * 637 * 638 * <pre> 639 * Required. The ID of the Dataset in the same Project and Location which data 640 * will be used to train the Model. The Dataset must use schema compatible 641 * with Model being trained, and what is compatible should be described in the 642 * used TrainingPipeline's [training_task_definition] 643 * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. 644 * For tabular Datasets, all their data is exported to training, to pick 645 * and choose from. 646 * </pre> 647 * 648 * <code>string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];</code> 649 * 650 * @return The datasetId. 651 */ 652 @java.lang.Override getDatasetId()653 public java.lang.String getDatasetId() { 654 java.lang.Object ref = datasetId_; 655 if (ref instanceof java.lang.String) { 656 return (java.lang.String) ref; 657 } else { 658 com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref; 659 java.lang.String s = bs.toStringUtf8(); 660 datasetId_ = s; 661 return s; 662 } 663 } 664 /** 665 * 666 * 667 * <pre> 668 * Required. The ID of the Dataset in the same Project and Location which data 669 * will be used to train the Model. The Dataset must use schema compatible 670 * with Model being trained, and what is compatible should be described in the 671 * used TrainingPipeline's [training_task_definition] 672 * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. 673 * For tabular Datasets, all their data is exported to training, to pick 674 * and choose from. 675 * </pre> 676 * 677 * <code>string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];</code> 678 * 679 * @return The bytes for datasetId. 680 */ 681 @java.lang.Override getDatasetIdBytes()682 public com.google.protobuf.ByteString getDatasetIdBytes() { 683 java.lang.Object ref = datasetId_; 684 if (ref instanceof java.lang.String) { 685 com.google.protobuf.ByteString b = 686 com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref); 687 datasetId_ = b; 688 return b; 689 } else { 690 return (com.google.protobuf.ByteString) ref; 691 } 692 } 693 694 public static final int ANNOTATIONS_FILTER_FIELD_NUMBER = 6; 695 696 @SuppressWarnings("serial") 697 private volatile java.lang.Object annotationsFilter_ = ""; 698 /** 699 * 700 * 701 * <pre> 702 * Applicable only to Datasets that have DataItems and Annotations. 703 * A filter on Annotations of the Dataset. Only Annotations that both 704 * match this filter and belong to DataItems not ignored by the split method 705 * are used in respectively training, validation or test role, depending on 706 * the role of the DataItem they are on (for the auto-assigned that role is 707 * decided by Vertex AI). A filter with same syntax as the one used in 708 * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] 709 * may be used, but note here it filters across all Annotations of the 710 * Dataset, and not just within a single DataItem. 711 * </pre> 712 * 713 * <code>string annotations_filter = 6;</code> 714 * 715 * @return The annotationsFilter. 716 */ 717 @java.lang.Override getAnnotationsFilter()718 public java.lang.String getAnnotationsFilter() { 719 java.lang.Object ref = annotationsFilter_; 720 if (ref instanceof java.lang.String) { 721 return (java.lang.String) ref; 722 } else { 723 com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref; 724 java.lang.String s = bs.toStringUtf8(); 725 annotationsFilter_ = s; 726 return s; 727 } 728 } 729 /** 730 * 731 * 732 * <pre> 733 * Applicable only to Datasets that have DataItems and Annotations. 734 * A filter on Annotations of the Dataset. Only Annotations that both 735 * match this filter and belong to DataItems not ignored by the split method 736 * are used in respectively training, validation or test role, depending on 737 * the role of the DataItem they are on (for the auto-assigned that role is 738 * decided by Vertex AI). A filter with same syntax as the one used in 739 * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] 740 * may be used, but note here it filters across all Annotations of the 741 * Dataset, and not just within a single DataItem. 742 * </pre> 743 * 744 * <code>string annotations_filter = 6;</code> 745 * 746 * @return The bytes for annotationsFilter. 747 */ 748 @java.lang.Override getAnnotationsFilterBytes()749 public com.google.protobuf.ByteString getAnnotationsFilterBytes() { 750 java.lang.Object ref = annotationsFilter_; 751 if (ref instanceof java.lang.String) { 752 com.google.protobuf.ByteString b = 753 com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref); 754 annotationsFilter_ = b; 755 return b; 756 } else { 757 return (com.google.protobuf.ByteString) ref; 758 } 759 } 760 761 public static final int ANNOTATION_SCHEMA_URI_FIELD_NUMBER = 9; 762 763 @SuppressWarnings("serial") 764 private volatile java.lang.Object annotationSchemaUri_ = ""; 765 /** 766 * 767 * 768 * <pre> 769 * Applicable only to custom training with Datasets that have DataItems and 770 * Annotations. 771 * Cloud Storage URI that points to a YAML file describing the annotation 772 * schema. The schema is defined as an OpenAPI 3.0.2 [Schema 773 * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). 774 * The schema files that can be used here are found in 775 * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the 776 * chosen schema must be consistent with 777 * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the 778 * Dataset specified by 779 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. 780 * Only Annotations that both match this schema and belong to DataItems not 781 * ignored by the split method are used in respectively training, validation 782 * or test role, depending on the role of the DataItem they are on. 783 * When used in conjunction with 784 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 785 * the Annotations used for training are filtered by both 786 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] 787 * and 788 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. 789 * </pre> 790 * 791 * <code>string annotation_schema_uri = 9;</code> 792 * 793 * @return The annotationSchemaUri. 794 */ 795 @java.lang.Override getAnnotationSchemaUri()796 public java.lang.String getAnnotationSchemaUri() { 797 java.lang.Object ref = annotationSchemaUri_; 798 if (ref instanceof java.lang.String) { 799 return (java.lang.String) ref; 800 } else { 801 com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref; 802 java.lang.String s = bs.toStringUtf8(); 803 annotationSchemaUri_ = s; 804 return s; 805 } 806 } 807 /** 808 * 809 * 810 * <pre> 811 * Applicable only to custom training with Datasets that have DataItems and 812 * Annotations. 813 * Cloud Storage URI that points to a YAML file describing the annotation 814 * schema. The schema is defined as an OpenAPI 3.0.2 [Schema 815 * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). 816 * The schema files that can be used here are found in 817 * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the 818 * chosen schema must be consistent with 819 * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the 820 * Dataset specified by 821 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. 822 * Only Annotations that both match this schema and belong to DataItems not 823 * ignored by the split method are used in respectively training, validation 824 * or test role, depending on the role of the DataItem they are on. 825 * When used in conjunction with 826 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 827 * the Annotations used for training are filtered by both 828 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] 829 * and 830 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. 831 * </pre> 832 * 833 * <code>string annotation_schema_uri = 9;</code> 834 * 835 * @return The bytes for annotationSchemaUri. 836 */ 837 @java.lang.Override getAnnotationSchemaUriBytes()838 public com.google.protobuf.ByteString getAnnotationSchemaUriBytes() { 839 java.lang.Object ref = annotationSchemaUri_; 840 if (ref instanceof java.lang.String) { 841 com.google.protobuf.ByteString b = 842 com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref); 843 annotationSchemaUri_ = b; 844 return b; 845 } else { 846 return (com.google.protobuf.ByteString) ref; 847 } 848 } 849 850 public static final int SAVED_QUERY_ID_FIELD_NUMBER = 7; 851 852 @SuppressWarnings("serial") 853 private volatile java.lang.Object savedQueryId_ = ""; 854 /** 855 * 856 * 857 * <pre> 858 * Only applicable to Datasets that have SavedQueries. 859 * The ID of a SavedQuery (annotation set) under the Dataset specified by 860 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used 861 * for filtering Annotations for training. 862 * Only Annotations that are associated with this SavedQuery are used in 863 * respectively training. When used in conjunction with 864 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 865 * the Annotations used for training are filtered by both 866 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 867 * and 868 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. 869 * Only one of 870 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 871 * and 872 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] 873 * should be specified as both of them represent the same thing: problem type. 874 * </pre> 875 * 876 * <code>string saved_query_id = 7;</code> 877 * 878 * @return The savedQueryId. 879 */ 880 @java.lang.Override getSavedQueryId()881 public java.lang.String getSavedQueryId() { 882 java.lang.Object ref = savedQueryId_; 883 if (ref instanceof java.lang.String) { 884 return (java.lang.String) ref; 885 } else { 886 com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref; 887 java.lang.String s = bs.toStringUtf8(); 888 savedQueryId_ = s; 889 return s; 890 } 891 } 892 /** 893 * 894 * 895 * <pre> 896 * Only applicable to Datasets that have SavedQueries. 897 * The ID of a SavedQuery (annotation set) under the Dataset specified by 898 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used 899 * for filtering Annotations for training. 900 * Only Annotations that are associated with this SavedQuery are used in 901 * respectively training. When used in conjunction with 902 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 903 * the Annotations used for training are filtered by both 904 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 905 * and 906 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. 907 * Only one of 908 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 909 * and 910 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] 911 * should be specified as both of them represent the same thing: problem type. 912 * </pre> 913 * 914 * <code>string saved_query_id = 7;</code> 915 * 916 * @return The bytes for savedQueryId. 917 */ 918 @java.lang.Override getSavedQueryIdBytes()919 public com.google.protobuf.ByteString getSavedQueryIdBytes() { 920 java.lang.Object ref = savedQueryId_; 921 if (ref instanceof java.lang.String) { 922 com.google.protobuf.ByteString b = 923 com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref); 924 savedQueryId_ = b; 925 return b; 926 } else { 927 return (com.google.protobuf.ByteString) ref; 928 } 929 } 930 931 public static final int PERSIST_ML_USE_ASSIGNMENT_FIELD_NUMBER = 11; 932 private boolean persistMlUseAssignment_ = false; 933 /** 934 * 935 * 936 * <pre> 937 * Whether to persist the ML use assignment to data item system labels. 938 * </pre> 939 * 940 * <code>bool persist_ml_use_assignment = 11;</code> 941 * 942 * @return The persistMlUseAssignment. 943 */ 944 @java.lang.Override getPersistMlUseAssignment()945 public boolean getPersistMlUseAssignment() { 946 return persistMlUseAssignment_; 947 } 948 949 private byte memoizedIsInitialized = -1; 950 951 @java.lang.Override isInitialized()952 public final boolean isInitialized() { 953 byte isInitialized = memoizedIsInitialized; 954 if (isInitialized == 1) return true; 955 if (isInitialized == 0) return false; 956 957 memoizedIsInitialized = 1; 958 return true; 959 } 960 961 @java.lang.Override writeTo(com.google.protobuf.CodedOutputStream output)962 public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException { 963 if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(datasetId_)) { 964 com.google.protobuf.GeneratedMessageV3.writeString(output, 1, datasetId_); 965 } 966 if (splitCase_ == 2) { 967 output.writeMessage(2, (com.google.cloud.aiplatform.v1.FractionSplit) split_); 968 } 969 if (splitCase_ == 3) { 970 output.writeMessage(3, (com.google.cloud.aiplatform.v1.FilterSplit) split_); 971 } 972 if (splitCase_ == 4) { 973 output.writeMessage(4, (com.google.cloud.aiplatform.v1.PredefinedSplit) split_); 974 } 975 if (splitCase_ == 5) { 976 output.writeMessage(5, (com.google.cloud.aiplatform.v1.TimestampSplit) split_); 977 } 978 if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(annotationsFilter_)) { 979 com.google.protobuf.GeneratedMessageV3.writeString(output, 6, annotationsFilter_); 980 } 981 if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(savedQueryId_)) { 982 com.google.protobuf.GeneratedMessageV3.writeString(output, 7, savedQueryId_); 983 } 984 if (destinationCase_ == 8) { 985 output.writeMessage(8, (com.google.cloud.aiplatform.v1.GcsDestination) destination_); 986 } 987 if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(annotationSchemaUri_)) { 988 com.google.protobuf.GeneratedMessageV3.writeString(output, 9, annotationSchemaUri_); 989 } 990 if (destinationCase_ == 10) { 991 output.writeMessage(10, (com.google.cloud.aiplatform.v1.BigQueryDestination) destination_); 992 } 993 if (persistMlUseAssignment_ != false) { 994 output.writeBool(11, persistMlUseAssignment_); 995 } 996 if (splitCase_ == 12) { 997 output.writeMessage(12, (com.google.cloud.aiplatform.v1.StratifiedSplit) split_); 998 } 999 getUnknownFields().writeTo(output); 1000 } 1001 1002 @java.lang.Override getSerializedSize()1003 public int getSerializedSize() { 1004 int size = memoizedSize; 1005 if (size != -1) return size; 1006 1007 size = 0; 1008 if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(datasetId_)) { 1009 size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, datasetId_); 1010 } 1011 if (splitCase_ == 2) { 1012 size += 1013 com.google.protobuf.CodedOutputStream.computeMessageSize( 1014 2, (com.google.cloud.aiplatform.v1.FractionSplit) split_); 1015 } 1016 if (splitCase_ == 3) { 1017 size += 1018 com.google.protobuf.CodedOutputStream.computeMessageSize( 1019 3, (com.google.cloud.aiplatform.v1.FilterSplit) split_); 1020 } 1021 if (splitCase_ == 4) { 1022 size += 1023 com.google.protobuf.CodedOutputStream.computeMessageSize( 1024 4, (com.google.cloud.aiplatform.v1.PredefinedSplit) split_); 1025 } 1026 if (splitCase_ == 5) { 1027 size += 1028 com.google.protobuf.CodedOutputStream.computeMessageSize( 1029 5, (com.google.cloud.aiplatform.v1.TimestampSplit) split_); 1030 } 1031 if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(annotationsFilter_)) { 1032 size += com.google.protobuf.GeneratedMessageV3.computeStringSize(6, annotationsFilter_); 1033 } 1034 if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(savedQueryId_)) { 1035 size += com.google.protobuf.GeneratedMessageV3.computeStringSize(7, savedQueryId_); 1036 } 1037 if (destinationCase_ == 8) { 1038 size += 1039 com.google.protobuf.CodedOutputStream.computeMessageSize( 1040 8, (com.google.cloud.aiplatform.v1.GcsDestination) destination_); 1041 } 1042 if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(annotationSchemaUri_)) { 1043 size += com.google.protobuf.GeneratedMessageV3.computeStringSize(9, annotationSchemaUri_); 1044 } 1045 if (destinationCase_ == 10) { 1046 size += 1047 com.google.protobuf.CodedOutputStream.computeMessageSize( 1048 10, (com.google.cloud.aiplatform.v1.BigQueryDestination) destination_); 1049 } 1050 if (persistMlUseAssignment_ != false) { 1051 size += com.google.protobuf.CodedOutputStream.computeBoolSize(11, persistMlUseAssignment_); 1052 } 1053 if (splitCase_ == 12) { 1054 size += 1055 com.google.protobuf.CodedOutputStream.computeMessageSize( 1056 12, (com.google.cloud.aiplatform.v1.StratifiedSplit) split_); 1057 } 1058 size += getUnknownFields().getSerializedSize(); 1059 memoizedSize = size; 1060 return size; 1061 } 1062 1063 @java.lang.Override equals(final java.lang.Object obj)1064 public boolean equals(final java.lang.Object obj) { 1065 if (obj == this) { 1066 return true; 1067 } 1068 if (!(obj instanceof com.google.cloud.aiplatform.v1.InputDataConfig)) { 1069 return super.equals(obj); 1070 } 1071 com.google.cloud.aiplatform.v1.InputDataConfig other = 1072 (com.google.cloud.aiplatform.v1.InputDataConfig) obj; 1073 1074 if (!getDatasetId().equals(other.getDatasetId())) return false; 1075 if (!getAnnotationsFilter().equals(other.getAnnotationsFilter())) return false; 1076 if (!getAnnotationSchemaUri().equals(other.getAnnotationSchemaUri())) return false; 1077 if (!getSavedQueryId().equals(other.getSavedQueryId())) return false; 1078 if (getPersistMlUseAssignment() != other.getPersistMlUseAssignment()) return false; 1079 if (!getSplitCase().equals(other.getSplitCase())) return false; 1080 switch (splitCase_) { 1081 case 2: 1082 if (!getFractionSplit().equals(other.getFractionSplit())) return false; 1083 break; 1084 case 3: 1085 if (!getFilterSplit().equals(other.getFilterSplit())) return false; 1086 break; 1087 case 4: 1088 if (!getPredefinedSplit().equals(other.getPredefinedSplit())) return false; 1089 break; 1090 case 5: 1091 if (!getTimestampSplit().equals(other.getTimestampSplit())) return false; 1092 break; 1093 case 12: 1094 if (!getStratifiedSplit().equals(other.getStratifiedSplit())) return false; 1095 break; 1096 case 0: 1097 default: 1098 } 1099 if (!getDestinationCase().equals(other.getDestinationCase())) return false; 1100 switch (destinationCase_) { 1101 case 8: 1102 if (!getGcsDestination().equals(other.getGcsDestination())) return false; 1103 break; 1104 case 10: 1105 if (!getBigqueryDestination().equals(other.getBigqueryDestination())) return false; 1106 break; 1107 case 0: 1108 default: 1109 } 1110 if (!getUnknownFields().equals(other.getUnknownFields())) return false; 1111 return true; 1112 } 1113 1114 @java.lang.Override hashCode()1115 public int hashCode() { 1116 if (memoizedHashCode != 0) { 1117 return memoizedHashCode; 1118 } 1119 int hash = 41; 1120 hash = (19 * hash) + getDescriptor().hashCode(); 1121 hash = (37 * hash) + DATASET_ID_FIELD_NUMBER; 1122 hash = (53 * hash) + getDatasetId().hashCode(); 1123 hash = (37 * hash) + ANNOTATIONS_FILTER_FIELD_NUMBER; 1124 hash = (53 * hash) + getAnnotationsFilter().hashCode(); 1125 hash = (37 * hash) + ANNOTATION_SCHEMA_URI_FIELD_NUMBER; 1126 hash = (53 * hash) + getAnnotationSchemaUri().hashCode(); 1127 hash = (37 * hash) + SAVED_QUERY_ID_FIELD_NUMBER; 1128 hash = (53 * hash) + getSavedQueryId().hashCode(); 1129 hash = (37 * hash) + PERSIST_ML_USE_ASSIGNMENT_FIELD_NUMBER; 1130 hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(getPersistMlUseAssignment()); 1131 switch (splitCase_) { 1132 case 2: 1133 hash = (37 * hash) + FRACTION_SPLIT_FIELD_NUMBER; 1134 hash = (53 * hash) + getFractionSplit().hashCode(); 1135 break; 1136 case 3: 1137 hash = (37 * hash) + FILTER_SPLIT_FIELD_NUMBER; 1138 hash = (53 * hash) + getFilterSplit().hashCode(); 1139 break; 1140 case 4: 1141 hash = (37 * hash) + PREDEFINED_SPLIT_FIELD_NUMBER; 1142 hash = (53 * hash) + getPredefinedSplit().hashCode(); 1143 break; 1144 case 5: 1145 hash = (37 * hash) + TIMESTAMP_SPLIT_FIELD_NUMBER; 1146 hash = (53 * hash) + getTimestampSplit().hashCode(); 1147 break; 1148 case 12: 1149 hash = (37 * hash) + STRATIFIED_SPLIT_FIELD_NUMBER; 1150 hash = (53 * hash) + getStratifiedSplit().hashCode(); 1151 break; 1152 case 0: 1153 default: 1154 } 1155 switch (destinationCase_) { 1156 case 8: 1157 hash = (37 * hash) + GCS_DESTINATION_FIELD_NUMBER; 1158 hash = (53 * hash) + getGcsDestination().hashCode(); 1159 break; 1160 case 10: 1161 hash = (37 * hash) + BIGQUERY_DESTINATION_FIELD_NUMBER; 1162 hash = (53 * hash) + getBigqueryDestination().hashCode(); 1163 break; 1164 case 0: 1165 default: 1166 } 1167 hash = (29 * hash) + getUnknownFields().hashCode(); 1168 memoizedHashCode = hash; 1169 return hash; 1170 } 1171 parseFrom(java.nio.ByteBuffer data)1172 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom(java.nio.ByteBuffer data) 1173 throws com.google.protobuf.InvalidProtocolBufferException { 1174 return PARSER.parseFrom(data); 1175 } 1176 parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)1177 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom( 1178 java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) 1179 throws com.google.protobuf.InvalidProtocolBufferException { 1180 return PARSER.parseFrom(data, extensionRegistry); 1181 } 1182 parseFrom( com.google.protobuf.ByteString data)1183 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom( 1184 com.google.protobuf.ByteString data) 1185 throws com.google.protobuf.InvalidProtocolBufferException { 1186 return PARSER.parseFrom(data); 1187 } 1188 parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)1189 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom( 1190 com.google.protobuf.ByteString data, 1191 com.google.protobuf.ExtensionRegistryLite extensionRegistry) 1192 throws com.google.protobuf.InvalidProtocolBufferException { 1193 return PARSER.parseFrom(data, extensionRegistry); 1194 } 1195 parseFrom(byte[] data)1196 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom(byte[] data) 1197 throws com.google.protobuf.InvalidProtocolBufferException { 1198 return PARSER.parseFrom(data); 1199 } 1200 parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)1201 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom( 1202 byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) 1203 throws com.google.protobuf.InvalidProtocolBufferException { 1204 return PARSER.parseFrom(data, extensionRegistry); 1205 } 1206 parseFrom(java.io.InputStream input)1207 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom(java.io.InputStream input) 1208 throws java.io.IOException { 1209 return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input); 1210 } 1211 parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)1212 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom( 1213 java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) 1214 throws java.io.IOException { 1215 return com.google.protobuf.GeneratedMessageV3.parseWithIOException( 1216 PARSER, input, extensionRegistry); 1217 } 1218 parseDelimitedFrom( java.io.InputStream input)1219 public static com.google.cloud.aiplatform.v1.InputDataConfig parseDelimitedFrom( 1220 java.io.InputStream input) throws java.io.IOException { 1221 return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input); 1222 } 1223 parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)1224 public static com.google.cloud.aiplatform.v1.InputDataConfig parseDelimitedFrom( 1225 java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) 1226 throws java.io.IOException { 1227 return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException( 1228 PARSER, input, extensionRegistry); 1229 } 1230 parseFrom( com.google.protobuf.CodedInputStream input)1231 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom( 1232 com.google.protobuf.CodedInputStream input) throws java.io.IOException { 1233 return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input); 1234 } 1235 parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)1236 public static com.google.cloud.aiplatform.v1.InputDataConfig parseFrom( 1237 com.google.protobuf.CodedInputStream input, 1238 com.google.protobuf.ExtensionRegistryLite extensionRegistry) 1239 throws java.io.IOException { 1240 return com.google.protobuf.GeneratedMessageV3.parseWithIOException( 1241 PARSER, input, extensionRegistry); 1242 } 1243 1244 @java.lang.Override newBuilderForType()1245 public Builder newBuilderForType() { 1246 return newBuilder(); 1247 } 1248 newBuilder()1249 public static Builder newBuilder() { 1250 return DEFAULT_INSTANCE.toBuilder(); 1251 } 1252 newBuilder(com.google.cloud.aiplatform.v1.InputDataConfig prototype)1253 public static Builder newBuilder(com.google.cloud.aiplatform.v1.InputDataConfig prototype) { 1254 return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); 1255 } 1256 1257 @java.lang.Override toBuilder()1258 public Builder toBuilder() { 1259 return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this); 1260 } 1261 1262 @java.lang.Override newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)1263 protected Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { 1264 Builder builder = new Builder(parent); 1265 return builder; 1266 } 1267 /** 1268 * 1269 * 1270 * <pre> 1271 * Specifies Vertex AI owned input data to be used for training, and 1272 * possibly evaluating, the Model. 1273 * </pre> 1274 * 1275 * Protobuf type {@code google.cloud.aiplatform.v1.InputDataConfig} 1276 */ 1277 public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder<Builder> 1278 implements 1279 // @@protoc_insertion_point(builder_implements:google.cloud.aiplatform.v1.InputDataConfig) 1280 com.google.cloud.aiplatform.v1.InputDataConfigOrBuilder { getDescriptor()1281 public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { 1282 return com.google.cloud.aiplatform.v1.TrainingPipelineProto 1283 .internal_static_google_cloud_aiplatform_v1_InputDataConfig_descriptor; 1284 } 1285 1286 @java.lang.Override 1287 protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()1288 internalGetFieldAccessorTable() { 1289 return com.google.cloud.aiplatform.v1.TrainingPipelineProto 1290 .internal_static_google_cloud_aiplatform_v1_InputDataConfig_fieldAccessorTable 1291 .ensureFieldAccessorsInitialized( 1292 com.google.cloud.aiplatform.v1.InputDataConfig.class, 1293 com.google.cloud.aiplatform.v1.InputDataConfig.Builder.class); 1294 } 1295 1296 // Construct using com.google.cloud.aiplatform.v1.InputDataConfig.newBuilder() Builder()1297 private Builder() {} 1298 Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)1299 private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { 1300 super(parent); 1301 } 1302 1303 @java.lang.Override clear()1304 public Builder clear() { 1305 super.clear(); 1306 bitField0_ = 0; 1307 if (fractionSplitBuilder_ != null) { 1308 fractionSplitBuilder_.clear(); 1309 } 1310 if (filterSplitBuilder_ != null) { 1311 filterSplitBuilder_.clear(); 1312 } 1313 if (predefinedSplitBuilder_ != null) { 1314 predefinedSplitBuilder_.clear(); 1315 } 1316 if (timestampSplitBuilder_ != null) { 1317 timestampSplitBuilder_.clear(); 1318 } 1319 if (stratifiedSplitBuilder_ != null) { 1320 stratifiedSplitBuilder_.clear(); 1321 } 1322 if (gcsDestinationBuilder_ != null) { 1323 gcsDestinationBuilder_.clear(); 1324 } 1325 if (bigqueryDestinationBuilder_ != null) { 1326 bigqueryDestinationBuilder_.clear(); 1327 } 1328 datasetId_ = ""; 1329 annotationsFilter_ = ""; 1330 annotationSchemaUri_ = ""; 1331 savedQueryId_ = ""; 1332 persistMlUseAssignment_ = false; 1333 splitCase_ = 0; 1334 split_ = null; 1335 destinationCase_ = 0; 1336 destination_ = null; 1337 return this; 1338 } 1339 1340 @java.lang.Override getDescriptorForType()1341 public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { 1342 return com.google.cloud.aiplatform.v1.TrainingPipelineProto 1343 .internal_static_google_cloud_aiplatform_v1_InputDataConfig_descriptor; 1344 } 1345 1346 @java.lang.Override getDefaultInstanceForType()1347 public com.google.cloud.aiplatform.v1.InputDataConfig getDefaultInstanceForType() { 1348 return com.google.cloud.aiplatform.v1.InputDataConfig.getDefaultInstance(); 1349 } 1350 1351 @java.lang.Override build()1352 public com.google.cloud.aiplatform.v1.InputDataConfig build() { 1353 com.google.cloud.aiplatform.v1.InputDataConfig result = buildPartial(); 1354 if (!result.isInitialized()) { 1355 throw newUninitializedMessageException(result); 1356 } 1357 return result; 1358 } 1359 1360 @java.lang.Override buildPartial()1361 public com.google.cloud.aiplatform.v1.InputDataConfig buildPartial() { 1362 com.google.cloud.aiplatform.v1.InputDataConfig result = 1363 new com.google.cloud.aiplatform.v1.InputDataConfig(this); 1364 if (bitField0_ != 0) { 1365 buildPartial0(result); 1366 } 1367 buildPartialOneofs(result); 1368 onBuilt(); 1369 return result; 1370 } 1371 buildPartial0(com.google.cloud.aiplatform.v1.InputDataConfig result)1372 private void buildPartial0(com.google.cloud.aiplatform.v1.InputDataConfig result) { 1373 int from_bitField0_ = bitField0_; 1374 if (((from_bitField0_ & 0x00000080) != 0)) { 1375 result.datasetId_ = datasetId_; 1376 } 1377 if (((from_bitField0_ & 0x00000100) != 0)) { 1378 result.annotationsFilter_ = annotationsFilter_; 1379 } 1380 if (((from_bitField0_ & 0x00000200) != 0)) { 1381 result.annotationSchemaUri_ = annotationSchemaUri_; 1382 } 1383 if (((from_bitField0_ & 0x00000400) != 0)) { 1384 result.savedQueryId_ = savedQueryId_; 1385 } 1386 if (((from_bitField0_ & 0x00000800) != 0)) { 1387 result.persistMlUseAssignment_ = persistMlUseAssignment_; 1388 } 1389 } 1390 buildPartialOneofs(com.google.cloud.aiplatform.v1.InputDataConfig result)1391 private void buildPartialOneofs(com.google.cloud.aiplatform.v1.InputDataConfig result) { 1392 result.splitCase_ = splitCase_; 1393 result.split_ = this.split_; 1394 if (splitCase_ == 2 && fractionSplitBuilder_ != null) { 1395 result.split_ = fractionSplitBuilder_.build(); 1396 } 1397 if (splitCase_ == 3 && filterSplitBuilder_ != null) { 1398 result.split_ = filterSplitBuilder_.build(); 1399 } 1400 if (splitCase_ == 4 && predefinedSplitBuilder_ != null) { 1401 result.split_ = predefinedSplitBuilder_.build(); 1402 } 1403 if (splitCase_ == 5 && timestampSplitBuilder_ != null) { 1404 result.split_ = timestampSplitBuilder_.build(); 1405 } 1406 if (splitCase_ == 12 && stratifiedSplitBuilder_ != null) { 1407 result.split_ = stratifiedSplitBuilder_.build(); 1408 } 1409 result.destinationCase_ = destinationCase_; 1410 result.destination_ = this.destination_; 1411 if (destinationCase_ == 8 && gcsDestinationBuilder_ != null) { 1412 result.destination_ = gcsDestinationBuilder_.build(); 1413 } 1414 if (destinationCase_ == 10 && bigqueryDestinationBuilder_ != null) { 1415 result.destination_ = bigqueryDestinationBuilder_.build(); 1416 } 1417 } 1418 1419 @java.lang.Override clone()1420 public Builder clone() { 1421 return super.clone(); 1422 } 1423 1424 @java.lang.Override setField( com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)1425 public Builder setField( 1426 com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { 1427 return super.setField(field, value); 1428 } 1429 1430 @java.lang.Override clearField(com.google.protobuf.Descriptors.FieldDescriptor field)1431 public Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field) { 1432 return super.clearField(field); 1433 } 1434 1435 @java.lang.Override clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)1436 public Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) { 1437 return super.clearOneof(oneof); 1438 } 1439 1440 @java.lang.Override setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value)1441 public Builder setRepeatedField( 1442 com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) { 1443 return super.setRepeatedField(field, index, value); 1444 } 1445 1446 @java.lang.Override addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)1447 public Builder addRepeatedField( 1448 com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { 1449 return super.addRepeatedField(field, value); 1450 } 1451 1452 @java.lang.Override mergeFrom(com.google.protobuf.Message other)1453 public Builder mergeFrom(com.google.protobuf.Message other) { 1454 if (other instanceof com.google.cloud.aiplatform.v1.InputDataConfig) { 1455 return mergeFrom((com.google.cloud.aiplatform.v1.InputDataConfig) other); 1456 } else { 1457 super.mergeFrom(other); 1458 return this; 1459 } 1460 } 1461 mergeFrom(com.google.cloud.aiplatform.v1.InputDataConfig other)1462 public Builder mergeFrom(com.google.cloud.aiplatform.v1.InputDataConfig other) { 1463 if (other == com.google.cloud.aiplatform.v1.InputDataConfig.getDefaultInstance()) return this; 1464 if (!other.getDatasetId().isEmpty()) { 1465 datasetId_ = other.datasetId_; 1466 bitField0_ |= 0x00000080; 1467 onChanged(); 1468 } 1469 if (!other.getAnnotationsFilter().isEmpty()) { 1470 annotationsFilter_ = other.annotationsFilter_; 1471 bitField0_ |= 0x00000100; 1472 onChanged(); 1473 } 1474 if (!other.getAnnotationSchemaUri().isEmpty()) { 1475 annotationSchemaUri_ = other.annotationSchemaUri_; 1476 bitField0_ |= 0x00000200; 1477 onChanged(); 1478 } 1479 if (!other.getSavedQueryId().isEmpty()) { 1480 savedQueryId_ = other.savedQueryId_; 1481 bitField0_ |= 0x00000400; 1482 onChanged(); 1483 } 1484 if (other.getPersistMlUseAssignment() != false) { 1485 setPersistMlUseAssignment(other.getPersistMlUseAssignment()); 1486 } 1487 switch (other.getSplitCase()) { 1488 case FRACTION_SPLIT: 1489 { 1490 mergeFractionSplit(other.getFractionSplit()); 1491 break; 1492 } 1493 case FILTER_SPLIT: 1494 { 1495 mergeFilterSplit(other.getFilterSplit()); 1496 break; 1497 } 1498 case PREDEFINED_SPLIT: 1499 { 1500 mergePredefinedSplit(other.getPredefinedSplit()); 1501 break; 1502 } 1503 case TIMESTAMP_SPLIT: 1504 { 1505 mergeTimestampSplit(other.getTimestampSplit()); 1506 break; 1507 } 1508 case STRATIFIED_SPLIT: 1509 { 1510 mergeStratifiedSplit(other.getStratifiedSplit()); 1511 break; 1512 } 1513 case SPLIT_NOT_SET: 1514 { 1515 break; 1516 } 1517 } 1518 switch (other.getDestinationCase()) { 1519 case GCS_DESTINATION: 1520 { 1521 mergeGcsDestination(other.getGcsDestination()); 1522 break; 1523 } 1524 case BIGQUERY_DESTINATION: 1525 { 1526 mergeBigqueryDestination(other.getBigqueryDestination()); 1527 break; 1528 } 1529 case DESTINATION_NOT_SET: 1530 { 1531 break; 1532 } 1533 } 1534 this.mergeUnknownFields(other.getUnknownFields()); 1535 onChanged(); 1536 return this; 1537 } 1538 1539 @java.lang.Override isInitialized()1540 public final boolean isInitialized() { 1541 return true; 1542 } 1543 1544 @java.lang.Override mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)1545 public Builder mergeFrom( 1546 com.google.protobuf.CodedInputStream input, 1547 com.google.protobuf.ExtensionRegistryLite extensionRegistry) 1548 throws java.io.IOException { 1549 if (extensionRegistry == null) { 1550 throw new java.lang.NullPointerException(); 1551 } 1552 try { 1553 boolean done = false; 1554 while (!done) { 1555 int tag = input.readTag(); 1556 switch (tag) { 1557 case 0: 1558 done = true; 1559 break; 1560 case 10: 1561 { 1562 datasetId_ = input.readStringRequireUtf8(); 1563 bitField0_ |= 0x00000080; 1564 break; 1565 } // case 10 1566 case 18: 1567 { 1568 input.readMessage(getFractionSplitFieldBuilder().getBuilder(), extensionRegistry); 1569 splitCase_ = 2; 1570 break; 1571 } // case 18 1572 case 26: 1573 { 1574 input.readMessage(getFilterSplitFieldBuilder().getBuilder(), extensionRegistry); 1575 splitCase_ = 3; 1576 break; 1577 } // case 26 1578 case 34: 1579 { 1580 input.readMessage(getPredefinedSplitFieldBuilder().getBuilder(), extensionRegistry); 1581 splitCase_ = 4; 1582 break; 1583 } // case 34 1584 case 42: 1585 { 1586 input.readMessage(getTimestampSplitFieldBuilder().getBuilder(), extensionRegistry); 1587 splitCase_ = 5; 1588 break; 1589 } // case 42 1590 case 50: 1591 { 1592 annotationsFilter_ = input.readStringRequireUtf8(); 1593 bitField0_ |= 0x00000100; 1594 break; 1595 } // case 50 1596 case 58: 1597 { 1598 savedQueryId_ = input.readStringRequireUtf8(); 1599 bitField0_ |= 0x00000400; 1600 break; 1601 } // case 58 1602 case 66: 1603 { 1604 input.readMessage(getGcsDestinationFieldBuilder().getBuilder(), extensionRegistry); 1605 destinationCase_ = 8; 1606 break; 1607 } // case 66 1608 case 74: 1609 { 1610 annotationSchemaUri_ = input.readStringRequireUtf8(); 1611 bitField0_ |= 0x00000200; 1612 break; 1613 } // case 74 1614 case 82: 1615 { 1616 input.readMessage( 1617 getBigqueryDestinationFieldBuilder().getBuilder(), extensionRegistry); 1618 destinationCase_ = 10; 1619 break; 1620 } // case 82 1621 case 88: 1622 { 1623 persistMlUseAssignment_ = input.readBool(); 1624 bitField0_ |= 0x00000800; 1625 break; 1626 } // case 88 1627 case 98: 1628 { 1629 input.readMessage(getStratifiedSplitFieldBuilder().getBuilder(), extensionRegistry); 1630 splitCase_ = 12; 1631 break; 1632 } // case 98 1633 default: 1634 { 1635 if (!super.parseUnknownField(input, extensionRegistry, tag)) { 1636 done = true; // was an endgroup tag 1637 } 1638 break; 1639 } // default: 1640 } // switch (tag) 1641 } // while (!done) 1642 } catch (com.google.protobuf.InvalidProtocolBufferException e) { 1643 throw e.unwrapIOException(); 1644 } finally { 1645 onChanged(); 1646 } // finally 1647 return this; 1648 } 1649 1650 private int splitCase_ = 0; 1651 private java.lang.Object split_; 1652 getSplitCase()1653 public SplitCase getSplitCase() { 1654 return SplitCase.forNumber(splitCase_); 1655 } 1656 clearSplit()1657 public Builder clearSplit() { 1658 splitCase_ = 0; 1659 split_ = null; 1660 onChanged(); 1661 return this; 1662 } 1663 1664 private int destinationCase_ = 0; 1665 private java.lang.Object destination_; 1666 getDestinationCase()1667 public DestinationCase getDestinationCase() { 1668 return DestinationCase.forNumber(destinationCase_); 1669 } 1670 clearDestination()1671 public Builder clearDestination() { 1672 destinationCase_ = 0; 1673 destination_ = null; 1674 onChanged(); 1675 return this; 1676 } 1677 1678 private int bitField0_; 1679 1680 private com.google.protobuf.SingleFieldBuilderV3< 1681 com.google.cloud.aiplatform.v1.FractionSplit, 1682 com.google.cloud.aiplatform.v1.FractionSplit.Builder, 1683 com.google.cloud.aiplatform.v1.FractionSplitOrBuilder> 1684 fractionSplitBuilder_; 1685 /** 1686 * 1687 * 1688 * <pre> 1689 * Split based on fractions defining the size of each set. 1690 * </pre> 1691 * 1692 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1693 * 1694 * @return Whether the fractionSplit field is set. 1695 */ 1696 @java.lang.Override hasFractionSplit()1697 public boolean hasFractionSplit() { 1698 return splitCase_ == 2; 1699 } 1700 /** 1701 * 1702 * 1703 * <pre> 1704 * Split based on fractions defining the size of each set. 1705 * </pre> 1706 * 1707 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1708 * 1709 * @return The fractionSplit. 1710 */ 1711 @java.lang.Override getFractionSplit()1712 public com.google.cloud.aiplatform.v1.FractionSplit getFractionSplit() { 1713 if (fractionSplitBuilder_ == null) { 1714 if (splitCase_ == 2) { 1715 return (com.google.cloud.aiplatform.v1.FractionSplit) split_; 1716 } 1717 return com.google.cloud.aiplatform.v1.FractionSplit.getDefaultInstance(); 1718 } else { 1719 if (splitCase_ == 2) { 1720 return fractionSplitBuilder_.getMessage(); 1721 } 1722 return com.google.cloud.aiplatform.v1.FractionSplit.getDefaultInstance(); 1723 } 1724 } 1725 /** 1726 * 1727 * 1728 * <pre> 1729 * Split based on fractions defining the size of each set. 1730 * </pre> 1731 * 1732 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1733 */ setFractionSplit(com.google.cloud.aiplatform.v1.FractionSplit value)1734 public Builder setFractionSplit(com.google.cloud.aiplatform.v1.FractionSplit value) { 1735 if (fractionSplitBuilder_ == null) { 1736 if (value == null) { 1737 throw new NullPointerException(); 1738 } 1739 split_ = value; 1740 onChanged(); 1741 } else { 1742 fractionSplitBuilder_.setMessage(value); 1743 } 1744 splitCase_ = 2; 1745 return this; 1746 } 1747 /** 1748 * 1749 * 1750 * <pre> 1751 * Split based on fractions defining the size of each set. 1752 * </pre> 1753 * 1754 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1755 */ setFractionSplit( com.google.cloud.aiplatform.v1.FractionSplit.Builder builderForValue)1756 public Builder setFractionSplit( 1757 com.google.cloud.aiplatform.v1.FractionSplit.Builder builderForValue) { 1758 if (fractionSplitBuilder_ == null) { 1759 split_ = builderForValue.build(); 1760 onChanged(); 1761 } else { 1762 fractionSplitBuilder_.setMessage(builderForValue.build()); 1763 } 1764 splitCase_ = 2; 1765 return this; 1766 } 1767 /** 1768 * 1769 * 1770 * <pre> 1771 * Split based on fractions defining the size of each set. 1772 * </pre> 1773 * 1774 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1775 */ mergeFractionSplit(com.google.cloud.aiplatform.v1.FractionSplit value)1776 public Builder mergeFractionSplit(com.google.cloud.aiplatform.v1.FractionSplit value) { 1777 if (fractionSplitBuilder_ == null) { 1778 if (splitCase_ == 2 1779 && split_ != com.google.cloud.aiplatform.v1.FractionSplit.getDefaultInstance()) { 1780 split_ = 1781 com.google.cloud.aiplatform.v1.FractionSplit.newBuilder( 1782 (com.google.cloud.aiplatform.v1.FractionSplit) split_) 1783 .mergeFrom(value) 1784 .buildPartial(); 1785 } else { 1786 split_ = value; 1787 } 1788 onChanged(); 1789 } else { 1790 if (splitCase_ == 2) { 1791 fractionSplitBuilder_.mergeFrom(value); 1792 } else { 1793 fractionSplitBuilder_.setMessage(value); 1794 } 1795 } 1796 splitCase_ = 2; 1797 return this; 1798 } 1799 /** 1800 * 1801 * 1802 * <pre> 1803 * Split based on fractions defining the size of each set. 1804 * </pre> 1805 * 1806 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1807 */ clearFractionSplit()1808 public Builder clearFractionSplit() { 1809 if (fractionSplitBuilder_ == null) { 1810 if (splitCase_ == 2) { 1811 splitCase_ = 0; 1812 split_ = null; 1813 onChanged(); 1814 } 1815 } else { 1816 if (splitCase_ == 2) { 1817 splitCase_ = 0; 1818 split_ = null; 1819 } 1820 fractionSplitBuilder_.clear(); 1821 } 1822 return this; 1823 } 1824 /** 1825 * 1826 * 1827 * <pre> 1828 * Split based on fractions defining the size of each set. 1829 * </pre> 1830 * 1831 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1832 */ getFractionSplitBuilder()1833 public com.google.cloud.aiplatform.v1.FractionSplit.Builder getFractionSplitBuilder() { 1834 return getFractionSplitFieldBuilder().getBuilder(); 1835 } 1836 /** 1837 * 1838 * 1839 * <pre> 1840 * Split based on fractions defining the size of each set. 1841 * </pre> 1842 * 1843 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1844 */ 1845 @java.lang.Override getFractionSplitOrBuilder()1846 public com.google.cloud.aiplatform.v1.FractionSplitOrBuilder getFractionSplitOrBuilder() { 1847 if ((splitCase_ == 2) && (fractionSplitBuilder_ != null)) { 1848 return fractionSplitBuilder_.getMessageOrBuilder(); 1849 } else { 1850 if (splitCase_ == 2) { 1851 return (com.google.cloud.aiplatform.v1.FractionSplit) split_; 1852 } 1853 return com.google.cloud.aiplatform.v1.FractionSplit.getDefaultInstance(); 1854 } 1855 } 1856 /** 1857 * 1858 * 1859 * <pre> 1860 * Split based on fractions defining the size of each set. 1861 * </pre> 1862 * 1863 * <code>.google.cloud.aiplatform.v1.FractionSplit fraction_split = 2;</code> 1864 */ 1865 private com.google.protobuf.SingleFieldBuilderV3< 1866 com.google.cloud.aiplatform.v1.FractionSplit, 1867 com.google.cloud.aiplatform.v1.FractionSplit.Builder, 1868 com.google.cloud.aiplatform.v1.FractionSplitOrBuilder> getFractionSplitFieldBuilder()1869 getFractionSplitFieldBuilder() { 1870 if (fractionSplitBuilder_ == null) { 1871 if (!(splitCase_ == 2)) { 1872 split_ = com.google.cloud.aiplatform.v1.FractionSplit.getDefaultInstance(); 1873 } 1874 fractionSplitBuilder_ = 1875 new com.google.protobuf.SingleFieldBuilderV3< 1876 com.google.cloud.aiplatform.v1.FractionSplit, 1877 com.google.cloud.aiplatform.v1.FractionSplit.Builder, 1878 com.google.cloud.aiplatform.v1.FractionSplitOrBuilder>( 1879 (com.google.cloud.aiplatform.v1.FractionSplit) split_, 1880 getParentForChildren(), 1881 isClean()); 1882 split_ = null; 1883 } 1884 splitCase_ = 2; 1885 onChanged(); 1886 return fractionSplitBuilder_; 1887 } 1888 1889 private com.google.protobuf.SingleFieldBuilderV3< 1890 com.google.cloud.aiplatform.v1.FilterSplit, 1891 com.google.cloud.aiplatform.v1.FilterSplit.Builder, 1892 com.google.cloud.aiplatform.v1.FilterSplitOrBuilder> 1893 filterSplitBuilder_; 1894 /** 1895 * 1896 * 1897 * <pre> 1898 * Split based on the provided filters for each set. 1899 * </pre> 1900 * 1901 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 1902 * 1903 * @return Whether the filterSplit field is set. 1904 */ 1905 @java.lang.Override hasFilterSplit()1906 public boolean hasFilterSplit() { 1907 return splitCase_ == 3; 1908 } 1909 /** 1910 * 1911 * 1912 * <pre> 1913 * Split based on the provided filters for each set. 1914 * </pre> 1915 * 1916 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 1917 * 1918 * @return The filterSplit. 1919 */ 1920 @java.lang.Override getFilterSplit()1921 public com.google.cloud.aiplatform.v1.FilterSplit getFilterSplit() { 1922 if (filterSplitBuilder_ == null) { 1923 if (splitCase_ == 3) { 1924 return (com.google.cloud.aiplatform.v1.FilterSplit) split_; 1925 } 1926 return com.google.cloud.aiplatform.v1.FilterSplit.getDefaultInstance(); 1927 } else { 1928 if (splitCase_ == 3) { 1929 return filterSplitBuilder_.getMessage(); 1930 } 1931 return com.google.cloud.aiplatform.v1.FilterSplit.getDefaultInstance(); 1932 } 1933 } 1934 /** 1935 * 1936 * 1937 * <pre> 1938 * Split based on the provided filters for each set. 1939 * </pre> 1940 * 1941 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 1942 */ setFilterSplit(com.google.cloud.aiplatform.v1.FilterSplit value)1943 public Builder setFilterSplit(com.google.cloud.aiplatform.v1.FilterSplit value) { 1944 if (filterSplitBuilder_ == null) { 1945 if (value == null) { 1946 throw new NullPointerException(); 1947 } 1948 split_ = value; 1949 onChanged(); 1950 } else { 1951 filterSplitBuilder_.setMessage(value); 1952 } 1953 splitCase_ = 3; 1954 return this; 1955 } 1956 /** 1957 * 1958 * 1959 * <pre> 1960 * Split based on the provided filters for each set. 1961 * </pre> 1962 * 1963 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 1964 */ setFilterSplit( com.google.cloud.aiplatform.v1.FilterSplit.Builder builderForValue)1965 public Builder setFilterSplit( 1966 com.google.cloud.aiplatform.v1.FilterSplit.Builder builderForValue) { 1967 if (filterSplitBuilder_ == null) { 1968 split_ = builderForValue.build(); 1969 onChanged(); 1970 } else { 1971 filterSplitBuilder_.setMessage(builderForValue.build()); 1972 } 1973 splitCase_ = 3; 1974 return this; 1975 } 1976 /** 1977 * 1978 * 1979 * <pre> 1980 * Split based on the provided filters for each set. 1981 * </pre> 1982 * 1983 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 1984 */ mergeFilterSplit(com.google.cloud.aiplatform.v1.FilterSplit value)1985 public Builder mergeFilterSplit(com.google.cloud.aiplatform.v1.FilterSplit value) { 1986 if (filterSplitBuilder_ == null) { 1987 if (splitCase_ == 3 1988 && split_ != com.google.cloud.aiplatform.v1.FilterSplit.getDefaultInstance()) { 1989 split_ = 1990 com.google.cloud.aiplatform.v1.FilterSplit.newBuilder( 1991 (com.google.cloud.aiplatform.v1.FilterSplit) split_) 1992 .mergeFrom(value) 1993 .buildPartial(); 1994 } else { 1995 split_ = value; 1996 } 1997 onChanged(); 1998 } else { 1999 if (splitCase_ == 3) { 2000 filterSplitBuilder_.mergeFrom(value); 2001 } else { 2002 filterSplitBuilder_.setMessage(value); 2003 } 2004 } 2005 splitCase_ = 3; 2006 return this; 2007 } 2008 /** 2009 * 2010 * 2011 * <pre> 2012 * Split based on the provided filters for each set. 2013 * </pre> 2014 * 2015 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 2016 */ clearFilterSplit()2017 public Builder clearFilterSplit() { 2018 if (filterSplitBuilder_ == null) { 2019 if (splitCase_ == 3) { 2020 splitCase_ = 0; 2021 split_ = null; 2022 onChanged(); 2023 } 2024 } else { 2025 if (splitCase_ == 3) { 2026 splitCase_ = 0; 2027 split_ = null; 2028 } 2029 filterSplitBuilder_.clear(); 2030 } 2031 return this; 2032 } 2033 /** 2034 * 2035 * 2036 * <pre> 2037 * Split based on the provided filters for each set. 2038 * </pre> 2039 * 2040 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 2041 */ getFilterSplitBuilder()2042 public com.google.cloud.aiplatform.v1.FilterSplit.Builder getFilterSplitBuilder() { 2043 return getFilterSplitFieldBuilder().getBuilder(); 2044 } 2045 /** 2046 * 2047 * 2048 * <pre> 2049 * Split based on the provided filters for each set. 2050 * </pre> 2051 * 2052 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 2053 */ 2054 @java.lang.Override getFilterSplitOrBuilder()2055 public com.google.cloud.aiplatform.v1.FilterSplitOrBuilder getFilterSplitOrBuilder() { 2056 if ((splitCase_ == 3) && (filterSplitBuilder_ != null)) { 2057 return filterSplitBuilder_.getMessageOrBuilder(); 2058 } else { 2059 if (splitCase_ == 3) { 2060 return (com.google.cloud.aiplatform.v1.FilterSplit) split_; 2061 } 2062 return com.google.cloud.aiplatform.v1.FilterSplit.getDefaultInstance(); 2063 } 2064 } 2065 /** 2066 * 2067 * 2068 * <pre> 2069 * Split based on the provided filters for each set. 2070 * </pre> 2071 * 2072 * <code>.google.cloud.aiplatform.v1.FilterSplit filter_split = 3;</code> 2073 */ 2074 private com.google.protobuf.SingleFieldBuilderV3< 2075 com.google.cloud.aiplatform.v1.FilterSplit, 2076 com.google.cloud.aiplatform.v1.FilterSplit.Builder, 2077 com.google.cloud.aiplatform.v1.FilterSplitOrBuilder> getFilterSplitFieldBuilder()2078 getFilterSplitFieldBuilder() { 2079 if (filterSplitBuilder_ == null) { 2080 if (!(splitCase_ == 3)) { 2081 split_ = com.google.cloud.aiplatform.v1.FilterSplit.getDefaultInstance(); 2082 } 2083 filterSplitBuilder_ = 2084 new com.google.protobuf.SingleFieldBuilderV3< 2085 com.google.cloud.aiplatform.v1.FilterSplit, 2086 com.google.cloud.aiplatform.v1.FilterSplit.Builder, 2087 com.google.cloud.aiplatform.v1.FilterSplitOrBuilder>( 2088 (com.google.cloud.aiplatform.v1.FilterSplit) split_, 2089 getParentForChildren(), 2090 isClean()); 2091 split_ = null; 2092 } 2093 splitCase_ = 3; 2094 onChanged(); 2095 return filterSplitBuilder_; 2096 } 2097 2098 private com.google.protobuf.SingleFieldBuilderV3< 2099 com.google.cloud.aiplatform.v1.PredefinedSplit, 2100 com.google.cloud.aiplatform.v1.PredefinedSplit.Builder, 2101 com.google.cloud.aiplatform.v1.PredefinedSplitOrBuilder> 2102 predefinedSplitBuilder_; 2103 /** 2104 * 2105 * 2106 * <pre> 2107 * Supported only for tabular Datasets. 2108 * Split based on a predefined key. 2109 * </pre> 2110 * 2111 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2112 * 2113 * @return Whether the predefinedSplit field is set. 2114 */ 2115 @java.lang.Override hasPredefinedSplit()2116 public boolean hasPredefinedSplit() { 2117 return splitCase_ == 4; 2118 } 2119 /** 2120 * 2121 * 2122 * <pre> 2123 * Supported only for tabular Datasets. 2124 * Split based on a predefined key. 2125 * </pre> 2126 * 2127 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2128 * 2129 * @return The predefinedSplit. 2130 */ 2131 @java.lang.Override getPredefinedSplit()2132 public com.google.cloud.aiplatform.v1.PredefinedSplit getPredefinedSplit() { 2133 if (predefinedSplitBuilder_ == null) { 2134 if (splitCase_ == 4) { 2135 return (com.google.cloud.aiplatform.v1.PredefinedSplit) split_; 2136 } 2137 return com.google.cloud.aiplatform.v1.PredefinedSplit.getDefaultInstance(); 2138 } else { 2139 if (splitCase_ == 4) { 2140 return predefinedSplitBuilder_.getMessage(); 2141 } 2142 return com.google.cloud.aiplatform.v1.PredefinedSplit.getDefaultInstance(); 2143 } 2144 } 2145 /** 2146 * 2147 * 2148 * <pre> 2149 * Supported only for tabular Datasets. 2150 * Split based on a predefined key. 2151 * </pre> 2152 * 2153 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2154 */ setPredefinedSplit(com.google.cloud.aiplatform.v1.PredefinedSplit value)2155 public Builder setPredefinedSplit(com.google.cloud.aiplatform.v1.PredefinedSplit value) { 2156 if (predefinedSplitBuilder_ == null) { 2157 if (value == null) { 2158 throw new NullPointerException(); 2159 } 2160 split_ = value; 2161 onChanged(); 2162 } else { 2163 predefinedSplitBuilder_.setMessage(value); 2164 } 2165 splitCase_ = 4; 2166 return this; 2167 } 2168 /** 2169 * 2170 * 2171 * <pre> 2172 * Supported only for tabular Datasets. 2173 * Split based on a predefined key. 2174 * </pre> 2175 * 2176 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2177 */ setPredefinedSplit( com.google.cloud.aiplatform.v1.PredefinedSplit.Builder builderForValue)2178 public Builder setPredefinedSplit( 2179 com.google.cloud.aiplatform.v1.PredefinedSplit.Builder builderForValue) { 2180 if (predefinedSplitBuilder_ == null) { 2181 split_ = builderForValue.build(); 2182 onChanged(); 2183 } else { 2184 predefinedSplitBuilder_.setMessage(builderForValue.build()); 2185 } 2186 splitCase_ = 4; 2187 return this; 2188 } 2189 /** 2190 * 2191 * 2192 * <pre> 2193 * Supported only for tabular Datasets. 2194 * Split based on a predefined key. 2195 * </pre> 2196 * 2197 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2198 */ mergePredefinedSplit(com.google.cloud.aiplatform.v1.PredefinedSplit value)2199 public Builder mergePredefinedSplit(com.google.cloud.aiplatform.v1.PredefinedSplit value) { 2200 if (predefinedSplitBuilder_ == null) { 2201 if (splitCase_ == 4 2202 && split_ != com.google.cloud.aiplatform.v1.PredefinedSplit.getDefaultInstance()) { 2203 split_ = 2204 com.google.cloud.aiplatform.v1.PredefinedSplit.newBuilder( 2205 (com.google.cloud.aiplatform.v1.PredefinedSplit) split_) 2206 .mergeFrom(value) 2207 .buildPartial(); 2208 } else { 2209 split_ = value; 2210 } 2211 onChanged(); 2212 } else { 2213 if (splitCase_ == 4) { 2214 predefinedSplitBuilder_.mergeFrom(value); 2215 } else { 2216 predefinedSplitBuilder_.setMessage(value); 2217 } 2218 } 2219 splitCase_ = 4; 2220 return this; 2221 } 2222 /** 2223 * 2224 * 2225 * <pre> 2226 * Supported only for tabular Datasets. 2227 * Split based on a predefined key. 2228 * </pre> 2229 * 2230 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2231 */ clearPredefinedSplit()2232 public Builder clearPredefinedSplit() { 2233 if (predefinedSplitBuilder_ == null) { 2234 if (splitCase_ == 4) { 2235 splitCase_ = 0; 2236 split_ = null; 2237 onChanged(); 2238 } 2239 } else { 2240 if (splitCase_ == 4) { 2241 splitCase_ = 0; 2242 split_ = null; 2243 } 2244 predefinedSplitBuilder_.clear(); 2245 } 2246 return this; 2247 } 2248 /** 2249 * 2250 * 2251 * <pre> 2252 * Supported only for tabular Datasets. 2253 * Split based on a predefined key. 2254 * </pre> 2255 * 2256 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2257 */ getPredefinedSplitBuilder()2258 public com.google.cloud.aiplatform.v1.PredefinedSplit.Builder getPredefinedSplitBuilder() { 2259 return getPredefinedSplitFieldBuilder().getBuilder(); 2260 } 2261 /** 2262 * 2263 * 2264 * <pre> 2265 * Supported only for tabular Datasets. 2266 * Split based on a predefined key. 2267 * </pre> 2268 * 2269 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2270 */ 2271 @java.lang.Override getPredefinedSplitOrBuilder()2272 public com.google.cloud.aiplatform.v1.PredefinedSplitOrBuilder getPredefinedSplitOrBuilder() { 2273 if ((splitCase_ == 4) && (predefinedSplitBuilder_ != null)) { 2274 return predefinedSplitBuilder_.getMessageOrBuilder(); 2275 } else { 2276 if (splitCase_ == 4) { 2277 return (com.google.cloud.aiplatform.v1.PredefinedSplit) split_; 2278 } 2279 return com.google.cloud.aiplatform.v1.PredefinedSplit.getDefaultInstance(); 2280 } 2281 } 2282 /** 2283 * 2284 * 2285 * <pre> 2286 * Supported only for tabular Datasets. 2287 * Split based on a predefined key. 2288 * </pre> 2289 * 2290 * <code>.google.cloud.aiplatform.v1.PredefinedSplit predefined_split = 4;</code> 2291 */ 2292 private com.google.protobuf.SingleFieldBuilderV3< 2293 com.google.cloud.aiplatform.v1.PredefinedSplit, 2294 com.google.cloud.aiplatform.v1.PredefinedSplit.Builder, 2295 com.google.cloud.aiplatform.v1.PredefinedSplitOrBuilder> getPredefinedSplitFieldBuilder()2296 getPredefinedSplitFieldBuilder() { 2297 if (predefinedSplitBuilder_ == null) { 2298 if (!(splitCase_ == 4)) { 2299 split_ = com.google.cloud.aiplatform.v1.PredefinedSplit.getDefaultInstance(); 2300 } 2301 predefinedSplitBuilder_ = 2302 new com.google.protobuf.SingleFieldBuilderV3< 2303 com.google.cloud.aiplatform.v1.PredefinedSplit, 2304 com.google.cloud.aiplatform.v1.PredefinedSplit.Builder, 2305 com.google.cloud.aiplatform.v1.PredefinedSplitOrBuilder>( 2306 (com.google.cloud.aiplatform.v1.PredefinedSplit) split_, 2307 getParentForChildren(), 2308 isClean()); 2309 split_ = null; 2310 } 2311 splitCase_ = 4; 2312 onChanged(); 2313 return predefinedSplitBuilder_; 2314 } 2315 2316 private com.google.protobuf.SingleFieldBuilderV3< 2317 com.google.cloud.aiplatform.v1.TimestampSplit, 2318 com.google.cloud.aiplatform.v1.TimestampSplit.Builder, 2319 com.google.cloud.aiplatform.v1.TimestampSplitOrBuilder> 2320 timestampSplitBuilder_; 2321 /** 2322 * 2323 * 2324 * <pre> 2325 * Supported only for tabular Datasets. 2326 * Split based on the timestamp of the input data pieces. 2327 * </pre> 2328 * 2329 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2330 * 2331 * @return Whether the timestampSplit field is set. 2332 */ 2333 @java.lang.Override hasTimestampSplit()2334 public boolean hasTimestampSplit() { 2335 return splitCase_ == 5; 2336 } 2337 /** 2338 * 2339 * 2340 * <pre> 2341 * Supported only for tabular Datasets. 2342 * Split based on the timestamp of the input data pieces. 2343 * </pre> 2344 * 2345 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2346 * 2347 * @return The timestampSplit. 2348 */ 2349 @java.lang.Override getTimestampSplit()2350 public com.google.cloud.aiplatform.v1.TimestampSplit getTimestampSplit() { 2351 if (timestampSplitBuilder_ == null) { 2352 if (splitCase_ == 5) { 2353 return (com.google.cloud.aiplatform.v1.TimestampSplit) split_; 2354 } 2355 return com.google.cloud.aiplatform.v1.TimestampSplit.getDefaultInstance(); 2356 } else { 2357 if (splitCase_ == 5) { 2358 return timestampSplitBuilder_.getMessage(); 2359 } 2360 return com.google.cloud.aiplatform.v1.TimestampSplit.getDefaultInstance(); 2361 } 2362 } 2363 /** 2364 * 2365 * 2366 * <pre> 2367 * Supported only for tabular Datasets. 2368 * Split based on the timestamp of the input data pieces. 2369 * </pre> 2370 * 2371 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2372 */ setTimestampSplit(com.google.cloud.aiplatform.v1.TimestampSplit value)2373 public Builder setTimestampSplit(com.google.cloud.aiplatform.v1.TimestampSplit value) { 2374 if (timestampSplitBuilder_ == null) { 2375 if (value == null) { 2376 throw new NullPointerException(); 2377 } 2378 split_ = value; 2379 onChanged(); 2380 } else { 2381 timestampSplitBuilder_.setMessage(value); 2382 } 2383 splitCase_ = 5; 2384 return this; 2385 } 2386 /** 2387 * 2388 * 2389 * <pre> 2390 * Supported only for tabular Datasets. 2391 * Split based on the timestamp of the input data pieces. 2392 * </pre> 2393 * 2394 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2395 */ setTimestampSplit( com.google.cloud.aiplatform.v1.TimestampSplit.Builder builderForValue)2396 public Builder setTimestampSplit( 2397 com.google.cloud.aiplatform.v1.TimestampSplit.Builder builderForValue) { 2398 if (timestampSplitBuilder_ == null) { 2399 split_ = builderForValue.build(); 2400 onChanged(); 2401 } else { 2402 timestampSplitBuilder_.setMessage(builderForValue.build()); 2403 } 2404 splitCase_ = 5; 2405 return this; 2406 } 2407 /** 2408 * 2409 * 2410 * <pre> 2411 * Supported only for tabular Datasets. 2412 * Split based on the timestamp of the input data pieces. 2413 * </pre> 2414 * 2415 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2416 */ mergeTimestampSplit(com.google.cloud.aiplatform.v1.TimestampSplit value)2417 public Builder mergeTimestampSplit(com.google.cloud.aiplatform.v1.TimestampSplit value) { 2418 if (timestampSplitBuilder_ == null) { 2419 if (splitCase_ == 5 2420 && split_ != com.google.cloud.aiplatform.v1.TimestampSplit.getDefaultInstance()) { 2421 split_ = 2422 com.google.cloud.aiplatform.v1.TimestampSplit.newBuilder( 2423 (com.google.cloud.aiplatform.v1.TimestampSplit) split_) 2424 .mergeFrom(value) 2425 .buildPartial(); 2426 } else { 2427 split_ = value; 2428 } 2429 onChanged(); 2430 } else { 2431 if (splitCase_ == 5) { 2432 timestampSplitBuilder_.mergeFrom(value); 2433 } else { 2434 timestampSplitBuilder_.setMessage(value); 2435 } 2436 } 2437 splitCase_ = 5; 2438 return this; 2439 } 2440 /** 2441 * 2442 * 2443 * <pre> 2444 * Supported only for tabular Datasets. 2445 * Split based on the timestamp of the input data pieces. 2446 * </pre> 2447 * 2448 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2449 */ clearTimestampSplit()2450 public Builder clearTimestampSplit() { 2451 if (timestampSplitBuilder_ == null) { 2452 if (splitCase_ == 5) { 2453 splitCase_ = 0; 2454 split_ = null; 2455 onChanged(); 2456 } 2457 } else { 2458 if (splitCase_ == 5) { 2459 splitCase_ = 0; 2460 split_ = null; 2461 } 2462 timestampSplitBuilder_.clear(); 2463 } 2464 return this; 2465 } 2466 /** 2467 * 2468 * 2469 * <pre> 2470 * Supported only for tabular Datasets. 2471 * Split based on the timestamp of the input data pieces. 2472 * </pre> 2473 * 2474 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2475 */ getTimestampSplitBuilder()2476 public com.google.cloud.aiplatform.v1.TimestampSplit.Builder getTimestampSplitBuilder() { 2477 return getTimestampSplitFieldBuilder().getBuilder(); 2478 } 2479 /** 2480 * 2481 * 2482 * <pre> 2483 * Supported only for tabular Datasets. 2484 * Split based on the timestamp of the input data pieces. 2485 * </pre> 2486 * 2487 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2488 */ 2489 @java.lang.Override getTimestampSplitOrBuilder()2490 public com.google.cloud.aiplatform.v1.TimestampSplitOrBuilder getTimestampSplitOrBuilder() { 2491 if ((splitCase_ == 5) && (timestampSplitBuilder_ != null)) { 2492 return timestampSplitBuilder_.getMessageOrBuilder(); 2493 } else { 2494 if (splitCase_ == 5) { 2495 return (com.google.cloud.aiplatform.v1.TimestampSplit) split_; 2496 } 2497 return com.google.cloud.aiplatform.v1.TimestampSplit.getDefaultInstance(); 2498 } 2499 } 2500 /** 2501 * 2502 * 2503 * <pre> 2504 * Supported only for tabular Datasets. 2505 * Split based on the timestamp of the input data pieces. 2506 * </pre> 2507 * 2508 * <code>.google.cloud.aiplatform.v1.TimestampSplit timestamp_split = 5;</code> 2509 */ 2510 private com.google.protobuf.SingleFieldBuilderV3< 2511 com.google.cloud.aiplatform.v1.TimestampSplit, 2512 com.google.cloud.aiplatform.v1.TimestampSplit.Builder, 2513 com.google.cloud.aiplatform.v1.TimestampSplitOrBuilder> getTimestampSplitFieldBuilder()2514 getTimestampSplitFieldBuilder() { 2515 if (timestampSplitBuilder_ == null) { 2516 if (!(splitCase_ == 5)) { 2517 split_ = com.google.cloud.aiplatform.v1.TimestampSplit.getDefaultInstance(); 2518 } 2519 timestampSplitBuilder_ = 2520 new com.google.protobuf.SingleFieldBuilderV3< 2521 com.google.cloud.aiplatform.v1.TimestampSplit, 2522 com.google.cloud.aiplatform.v1.TimestampSplit.Builder, 2523 com.google.cloud.aiplatform.v1.TimestampSplitOrBuilder>( 2524 (com.google.cloud.aiplatform.v1.TimestampSplit) split_, 2525 getParentForChildren(), 2526 isClean()); 2527 split_ = null; 2528 } 2529 splitCase_ = 5; 2530 onChanged(); 2531 return timestampSplitBuilder_; 2532 } 2533 2534 private com.google.protobuf.SingleFieldBuilderV3< 2535 com.google.cloud.aiplatform.v1.StratifiedSplit, 2536 com.google.cloud.aiplatform.v1.StratifiedSplit.Builder, 2537 com.google.cloud.aiplatform.v1.StratifiedSplitOrBuilder> 2538 stratifiedSplitBuilder_; 2539 /** 2540 * 2541 * 2542 * <pre> 2543 * Supported only for tabular Datasets. 2544 * Split based on the distribution of the specified column. 2545 * </pre> 2546 * 2547 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2548 * 2549 * @return Whether the stratifiedSplit field is set. 2550 */ 2551 @java.lang.Override hasStratifiedSplit()2552 public boolean hasStratifiedSplit() { 2553 return splitCase_ == 12; 2554 } 2555 /** 2556 * 2557 * 2558 * <pre> 2559 * Supported only for tabular Datasets. 2560 * Split based on the distribution of the specified column. 2561 * </pre> 2562 * 2563 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2564 * 2565 * @return The stratifiedSplit. 2566 */ 2567 @java.lang.Override getStratifiedSplit()2568 public com.google.cloud.aiplatform.v1.StratifiedSplit getStratifiedSplit() { 2569 if (stratifiedSplitBuilder_ == null) { 2570 if (splitCase_ == 12) { 2571 return (com.google.cloud.aiplatform.v1.StratifiedSplit) split_; 2572 } 2573 return com.google.cloud.aiplatform.v1.StratifiedSplit.getDefaultInstance(); 2574 } else { 2575 if (splitCase_ == 12) { 2576 return stratifiedSplitBuilder_.getMessage(); 2577 } 2578 return com.google.cloud.aiplatform.v1.StratifiedSplit.getDefaultInstance(); 2579 } 2580 } 2581 /** 2582 * 2583 * 2584 * <pre> 2585 * Supported only for tabular Datasets. 2586 * Split based on the distribution of the specified column. 2587 * </pre> 2588 * 2589 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2590 */ setStratifiedSplit(com.google.cloud.aiplatform.v1.StratifiedSplit value)2591 public Builder setStratifiedSplit(com.google.cloud.aiplatform.v1.StratifiedSplit value) { 2592 if (stratifiedSplitBuilder_ == null) { 2593 if (value == null) { 2594 throw new NullPointerException(); 2595 } 2596 split_ = value; 2597 onChanged(); 2598 } else { 2599 stratifiedSplitBuilder_.setMessage(value); 2600 } 2601 splitCase_ = 12; 2602 return this; 2603 } 2604 /** 2605 * 2606 * 2607 * <pre> 2608 * Supported only for tabular Datasets. 2609 * Split based on the distribution of the specified column. 2610 * </pre> 2611 * 2612 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2613 */ setStratifiedSplit( com.google.cloud.aiplatform.v1.StratifiedSplit.Builder builderForValue)2614 public Builder setStratifiedSplit( 2615 com.google.cloud.aiplatform.v1.StratifiedSplit.Builder builderForValue) { 2616 if (stratifiedSplitBuilder_ == null) { 2617 split_ = builderForValue.build(); 2618 onChanged(); 2619 } else { 2620 stratifiedSplitBuilder_.setMessage(builderForValue.build()); 2621 } 2622 splitCase_ = 12; 2623 return this; 2624 } 2625 /** 2626 * 2627 * 2628 * <pre> 2629 * Supported only for tabular Datasets. 2630 * Split based on the distribution of the specified column. 2631 * </pre> 2632 * 2633 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2634 */ mergeStratifiedSplit(com.google.cloud.aiplatform.v1.StratifiedSplit value)2635 public Builder mergeStratifiedSplit(com.google.cloud.aiplatform.v1.StratifiedSplit value) { 2636 if (stratifiedSplitBuilder_ == null) { 2637 if (splitCase_ == 12 2638 && split_ != com.google.cloud.aiplatform.v1.StratifiedSplit.getDefaultInstance()) { 2639 split_ = 2640 com.google.cloud.aiplatform.v1.StratifiedSplit.newBuilder( 2641 (com.google.cloud.aiplatform.v1.StratifiedSplit) split_) 2642 .mergeFrom(value) 2643 .buildPartial(); 2644 } else { 2645 split_ = value; 2646 } 2647 onChanged(); 2648 } else { 2649 if (splitCase_ == 12) { 2650 stratifiedSplitBuilder_.mergeFrom(value); 2651 } else { 2652 stratifiedSplitBuilder_.setMessage(value); 2653 } 2654 } 2655 splitCase_ = 12; 2656 return this; 2657 } 2658 /** 2659 * 2660 * 2661 * <pre> 2662 * Supported only for tabular Datasets. 2663 * Split based on the distribution of the specified column. 2664 * </pre> 2665 * 2666 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2667 */ clearStratifiedSplit()2668 public Builder clearStratifiedSplit() { 2669 if (stratifiedSplitBuilder_ == null) { 2670 if (splitCase_ == 12) { 2671 splitCase_ = 0; 2672 split_ = null; 2673 onChanged(); 2674 } 2675 } else { 2676 if (splitCase_ == 12) { 2677 splitCase_ = 0; 2678 split_ = null; 2679 } 2680 stratifiedSplitBuilder_.clear(); 2681 } 2682 return this; 2683 } 2684 /** 2685 * 2686 * 2687 * <pre> 2688 * Supported only for tabular Datasets. 2689 * Split based on the distribution of the specified column. 2690 * </pre> 2691 * 2692 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2693 */ getStratifiedSplitBuilder()2694 public com.google.cloud.aiplatform.v1.StratifiedSplit.Builder getStratifiedSplitBuilder() { 2695 return getStratifiedSplitFieldBuilder().getBuilder(); 2696 } 2697 /** 2698 * 2699 * 2700 * <pre> 2701 * Supported only for tabular Datasets. 2702 * Split based on the distribution of the specified column. 2703 * </pre> 2704 * 2705 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2706 */ 2707 @java.lang.Override getStratifiedSplitOrBuilder()2708 public com.google.cloud.aiplatform.v1.StratifiedSplitOrBuilder getStratifiedSplitOrBuilder() { 2709 if ((splitCase_ == 12) && (stratifiedSplitBuilder_ != null)) { 2710 return stratifiedSplitBuilder_.getMessageOrBuilder(); 2711 } else { 2712 if (splitCase_ == 12) { 2713 return (com.google.cloud.aiplatform.v1.StratifiedSplit) split_; 2714 } 2715 return com.google.cloud.aiplatform.v1.StratifiedSplit.getDefaultInstance(); 2716 } 2717 } 2718 /** 2719 * 2720 * 2721 * <pre> 2722 * Supported only for tabular Datasets. 2723 * Split based on the distribution of the specified column. 2724 * </pre> 2725 * 2726 * <code>.google.cloud.aiplatform.v1.StratifiedSplit stratified_split = 12;</code> 2727 */ 2728 private com.google.protobuf.SingleFieldBuilderV3< 2729 com.google.cloud.aiplatform.v1.StratifiedSplit, 2730 com.google.cloud.aiplatform.v1.StratifiedSplit.Builder, 2731 com.google.cloud.aiplatform.v1.StratifiedSplitOrBuilder> getStratifiedSplitFieldBuilder()2732 getStratifiedSplitFieldBuilder() { 2733 if (stratifiedSplitBuilder_ == null) { 2734 if (!(splitCase_ == 12)) { 2735 split_ = com.google.cloud.aiplatform.v1.StratifiedSplit.getDefaultInstance(); 2736 } 2737 stratifiedSplitBuilder_ = 2738 new com.google.protobuf.SingleFieldBuilderV3< 2739 com.google.cloud.aiplatform.v1.StratifiedSplit, 2740 com.google.cloud.aiplatform.v1.StratifiedSplit.Builder, 2741 com.google.cloud.aiplatform.v1.StratifiedSplitOrBuilder>( 2742 (com.google.cloud.aiplatform.v1.StratifiedSplit) split_, 2743 getParentForChildren(), 2744 isClean()); 2745 split_ = null; 2746 } 2747 splitCase_ = 12; 2748 onChanged(); 2749 return stratifiedSplitBuilder_; 2750 } 2751 2752 private com.google.protobuf.SingleFieldBuilderV3< 2753 com.google.cloud.aiplatform.v1.GcsDestination, 2754 com.google.cloud.aiplatform.v1.GcsDestination.Builder, 2755 com.google.cloud.aiplatform.v1.GcsDestinationOrBuilder> 2756 gcsDestinationBuilder_; 2757 /** 2758 * 2759 * 2760 * <pre> 2761 * The Cloud Storage location where the training data is to be 2762 * written to. In the given directory a new directory is created with 2763 * name: 2764 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 2765 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 2766 * All training input data is written into that directory. 2767 * The Vertex AI environment variables representing Cloud Storage 2768 * data URIs are represented in the Cloud Storage wildcard 2769 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 2770 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 2771 * * AIP_TRAINING_DATA_URI = 2772 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 2773 * * AIP_VALIDATION_DATA_URI = 2774 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 2775 * * AIP_TEST_DATA_URI = 2776 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 2777 * </pre> 2778 * 2779 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 2780 * 2781 * @return Whether the gcsDestination field is set. 2782 */ 2783 @java.lang.Override hasGcsDestination()2784 public boolean hasGcsDestination() { 2785 return destinationCase_ == 8; 2786 } 2787 /** 2788 * 2789 * 2790 * <pre> 2791 * The Cloud Storage location where the training data is to be 2792 * written to. In the given directory a new directory is created with 2793 * name: 2794 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 2795 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 2796 * All training input data is written into that directory. 2797 * The Vertex AI environment variables representing Cloud Storage 2798 * data URIs are represented in the Cloud Storage wildcard 2799 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 2800 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 2801 * * AIP_TRAINING_DATA_URI = 2802 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 2803 * * AIP_VALIDATION_DATA_URI = 2804 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 2805 * * AIP_TEST_DATA_URI = 2806 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 2807 * </pre> 2808 * 2809 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 2810 * 2811 * @return The gcsDestination. 2812 */ 2813 @java.lang.Override getGcsDestination()2814 public com.google.cloud.aiplatform.v1.GcsDestination getGcsDestination() { 2815 if (gcsDestinationBuilder_ == null) { 2816 if (destinationCase_ == 8) { 2817 return (com.google.cloud.aiplatform.v1.GcsDestination) destination_; 2818 } 2819 return com.google.cloud.aiplatform.v1.GcsDestination.getDefaultInstance(); 2820 } else { 2821 if (destinationCase_ == 8) { 2822 return gcsDestinationBuilder_.getMessage(); 2823 } 2824 return com.google.cloud.aiplatform.v1.GcsDestination.getDefaultInstance(); 2825 } 2826 } 2827 /** 2828 * 2829 * 2830 * <pre> 2831 * The Cloud Storage location where the training data is to be 2832 * written to. In the given directory a new directory is created with 2833 * name: 2834 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 2835 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 2836 * All training input data is written into that directory. 2837 * The Vertex AI environment variables representing Cloud Storage 2838 * data URIs are represented in the Cloud Storage wildcard 2839 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 2840 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 2841 * * AIP_TRAINING_DATA_URI = 2842 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 2843 * * AIP_VALIDATION_DATA_URI = 2844 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 2845 * * AIP_TEST_DATA_URI = 2846 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 2847 * </pre> 2848 * 2849 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 2850 */ setGcsDestination(com.google.cloud.aiplatform.v1.GcsDestination value)2851 public Builder setGcsDestination(com.google.cloud.aiplatform.v1.GcsDestination value) { 2852 if (gcsDestinationBuilder_ == null) { 2853 if (value == null) { 2854 throw new NullPointerException(); 2855 } 2856 destination_ = value; 2857 onChanged(); 2858 } else { 2859 gcsDestinationBuilder_.setMessage(value); 2860 } 2861 destinationCase_ = 8; 2862 return this; 2863 } 2864 /** 2865 * 2866 * 2867 * <pre> 2868 * The Cloud Storage location where the training data is to be 2869 * written to. In the given directory a new directory is created with 2870 * name: 2871 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 2872 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 2873 * All training input data is written into that directory. 2874 * The Vertex AI environment variables representing Cloud Storage 2875 * data URIs are represented in the Cloud Storage wildcard 2876 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 2877 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 2878 * * AIP_TRAINING_DATA_URI = 2879 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 2880 * * AIP_VALIDATION_DATA_URI = 2881 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 2882 * * AIP_TEST_DATA_URI = 2883 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 2884 * </pre> 2885 * 2886 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 2887 */ setGcsDestination( com.google.cloud.aiplatform.v1.GcsDestination.Builder builderForValue)2888 public Builder setGcsDestination( 2889 com.google.cloud.aiplatform.v1.GcsDestination.Builder builderForValue) { 2890 if (gcsDestinationBuilder_ == null) { 2891 destination_ = builderForValue.build(); 2892 onChanged(); 2893 } else { 2894 gcsDestinationBuilder_.setMessage(builderForValue.build()); 2895 } 2896 destinationCase_ = 8; 2897 return this; 2898 } 2899 /** 2900 * 2901 * 2902 * <pre> 2903 * The Cloud Storage location where the training data is to be 2904 * written to. In the given directory a new directory is created with 2905 * name: 2906 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 2907 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 2908 * All training input data is written into that directory. 2909 * The Vertex AI environment variables representing Cloud Storage 2910 * data URIs are represented in the Cloud Storage wildcard 2911 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 2912 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 2913 * * AIP_TRAINING_DATA_URI = 2914 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 2915 * * AIP_VALIDATION_DATA_URI = 2916 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 2917 * * AIP_TEST_DATA_URI = 2918 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 2919 * </pre> 2920 * 2921 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 2922 */ mergeGcsDestination(com.google.cloud.aiplatform.v1.GcsDestination value)2923 public Builder mergeGcsDestination(com.google.cloud.aiplatform.v1.GcsDestination value) { 2924 if (gcsDestinationBuilder_ == null) { 2925 if (destinationCase_ == 8 2926 && destination_ != com.google.cloud.aiplatform.v1.GcsDestination.getDefaultInstance()) { 2927 destination_ = 2928 com.google.cloud.aiplatform.v1.GcsDestination.newBuilder( 2929 (com.google.cloud.aiplatform.v1.GcsDestination) destination_) 2930 .mergeFrom(value) 2931 .buildPartial(); 2932 } else { 2933 destination_ = value; 2934 } 2935 onChanged(); 2936 } else { 2937 if (destinationCase_ == 8) { 2938 gcsDestinationBuilder_.mergeFrom(value); 2939 } else { 2940 gcsDestinationBuilder_.setMessage(value); 2941 } 2942 } 2943 destinationCase_ = 8; 2944 return this; 2945 } 2946 /** 2947 * 2948 * 2949 * <pre> 2950 * The Cloud Storage location where the training data is to be 2951 * written to. In the given directory a new directory is created with 2952 * name: 2953 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 2954 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 2955 * All training input data is written into that directory. 2956 * The Vertex AI environment variables representing Cloud Storage 2957 * data URIs are represented in the Cloud Storage wildcard 2958 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 2959 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 2960 * * AIP_TRAINING_DATA_URI = 2961 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 2962 * * AIP_VALIDATION_DATA_URI = 2963 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 2964 * * AIP_TEST_DATA_URI = 2965 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 2966 * </pre> 2967 * 2968 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 2969 */ clearGcsDestination()2970 public Builder clearGcsDestination() { 2971 if (gcsDestinationBuilder_ == null) { 2972 if (destinationCase_ == 8) { 2973 destinationCase_ = 0; 2974 destination_ = null; 2975 onChanged(); 2976 } 2977 } else { 2978 if (destinationCase_ == 8) { 2979 destinationCase_ = 0; 2980 destination_ = null; 2981 } 2982 gcsDestinationBuilder_.clear(); 2983 } 2984 return this; 2985 } 2986 /** 2987 * 2988 * 2989 * <pre> 2990 * The Cloud Storage location where the training data is to be 2991 * written to. In the given directory a new directory is created with 2992 * name: 2993 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 2994 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 2995 * All training input data is written into that directory. 2996 * The Vertex AI environment variables representing Cloud Storage 2997 * data URIs are represented in the Cloud Storage wildcard 2998 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 2999 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 3000 * * AIP_TRAINING_DATA_URI = 3001 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 3002 * * AIP_VALIDATION_DATA_URI = 3003 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 3004 * * AIP_TEST_DATA_URI = 3005 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 3006 * </pre> 3007 * 3008 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 3009 */ getGcsDestinationBuilder()3010 public com.google.cloud.aiplatform.v1.GcsDestination.Builder getGcsDestinationBuilder() { 3011 return getGcsDestinationFieldBuilder().getBuilder(); 3012 } 3013 /** 3014 * 3015 * 3016 * <pre> 3017 * The Cloud Storage location where the training data is to be 3018 * written to. In the given directory a new directory is created with 3019 * name: 3020 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 3021 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 3022 * All training input data is written into that directory. 3023 * The Vertex AI environment variables representing Cloud Storage 3024 * data URIs are represented in the Cloud Storage wildcard 3025 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 3026 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 3027 * * AIP_TRAINING_DATA_URI = 3028 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 3029 * * AIP_VALIDATION_DATA_URI = 3030 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 3031 * * AIP_TEST_DATA_URI = 3032 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 3033 * </pre> 3034 * 3035 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 3036 */ 3037 @java.lang.Override getGcsDestinationOrBuilder()3038 public com.google.cloud.aiplatform.v1.GcsDestinationOrBuilder getGcsDestinationOrBuilder() { 3039 if ((destinationCase_ == 8) && (gcsDestinationBuilder_ != null)) { 3040 return gcsDestinationBuilder_.getMessageOrBuilder(); 3041 } else { 3042 if (destinationCase_ == 8) { 3043 return (com.google.cloud.aiplatform.v1.GcsDestination) destination_; 3044 } 3045 return com.google.cloud.aiplatform.v1.GcsDestination.getDefaultInstance(); 3046 } 3047 } 3048 /** 3049 * 3050 * 3051 * <pre> 3052 * The Cloud Storage location where the training data is to be 3053 * written to. In the given directory a new directory is created with 3054 * name: 3055 * `dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call>` 3056 * where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. 3057 * All training input data is written into that directory. 3058 * The Vertex AI environment variables representing Cloud Storage 3059 * data URIs are represented in the Cloud Storage wildcard 3060 * format to support sharded data. e.g.: "gs://.../training-*.jsonl" 3061 * * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data 3062 * * AIP_TRAINING_DATA_URI = 3063 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/training-*.${AIP_DATA_FORMAT}" 3064 * * AIP_VALIDATION_DATA_URI = 3065 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/validation-*.${AIP_DATA_FORMAT}" 3066 * * AIP_TEST_DATA_URI = 3067 * "gcs_destination/dataset-<dataset-id>-<annotation-type>-<time>/test-*.${AIP_DATA_FORMAT}" 3068 * </pre> 3069 * 3070 * <code>.google.cloud.aiplatform.v1.GcsDestination gcs_destination = 8;</code> 3071 */ 3072 private com.google.protobuf.SingleFieldBuilderV3< 3073 com.google.cloud.aiplatform.v1.GcsDestination, 3074 com.google.cloud.aiplatform.v1.GcsDestination.Builder, 3075 com.google.cloud.aiplatform.v1.GcsDestinationOrBuilder> getGcsDestinationFieldBuilder()3076 getGcsDestinationFieldBuilder() { 3077 if (gcsDestinationBuilder_ == null) { 3078 if (!(destinationCase_ == 8)) { 3079 destination_ = com.google.cloud.aiplatform.v1.GcsDestination.getDefaultInstance(); 3080 } 3081 gcsDestinationBuilder_ = 3082 new com.google.protobuf.SingleFieldBuilderV3< 3083 com.google.cloud.aiplatform.v1.GcsDestination, 3084 com.google.cloud.aiplatform.v1.GcsDestination.Builder, 3085 com.google.cloud.aiplatform.v1.GcsDestinationOrBuilder>( 3086 (com.google.cloud.aiplatform.v1.GcsDestination) destination_, 3087 getParentForChildren(), 3088 isClean()); 3089 destination_ = null; 3090 } 3091 destinationCase_ = 8; 3092 onChanged(); 3093 return gcsDestinationBuilder_; 3094 } 3095 3096 private com.google.protobuf.SingleFieldBuilderV3< 3097 com.google.cloud.aiplatform.v1.BigQueryDestination, 3098 com.google.cloud.aiplatform.v1.BigQueryDestination.Builder, 3099 com.google.cloud.aiplatform.v1.BigQueryDestinationOrBuilder> 3100 bigqueryDestinationBuilder_; 3101 /** 3102 * 3103 * 3104 * <pre> 3105 * Only applicable to custom training with tabular Dataset with BigQuery 3106 * source. 3107 * The BigQuery project location where the training data is to be written 3108 * to. In the given project a new dataset is created with name 3109 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3110 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3111 * input data is written into that dataset. In the dataset three 3112 * tables are created, `training`, `validation` and `test`. 3113 * * AIP_DATA_FORMAT = "bigquery". 3114 * * AIP_TRAINING_DATA_URI = 3115 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3116 * * AIP_VALIDATION_DATA_URI = 3117 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3118 * * AIP_TEST_DATA_URI = 3119 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3120 * </pre> 3121 * 3122 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3123 * 3124 * @return Whether the bigqueryDestination field is set. 3125 */ 3126 @java.lang.Override hasBigqueryDestination()3127 public boolean hasBigqueryDestination() { 3128 return destinationCase_ == 10; 3129 } 3130 /** 3131 * 3132 * 3133 * <pre> 3134 * Only applicable to custom training with tabular Dataset with BigQuery 3135 * source. 3136 * The BigQuery project location where the training data is to be written 3137 * to. In the given project a new dataset is created with name 3138 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3139 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3140 * input data is written into that dataset. In the dataset three 3141 * tables are created, `training`, `validation` and `test`. 3142 * * AIP_DATA_FORMAT = "bigquery". 3143 * * AIP_TRAINING_DATA_URI = 3144 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3145 * * AIP_VALIDATION_DATA_URI = 3146 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3147 * * AIP_TEST_DATA_URI = 3148 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3149 * </pre> 3150 * 3151 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3152 * 3153 * @return The bigqueryDestination. 3154 */ 3155 @java.lang.Override getBigqueryDestination()3156 public com.google.cloud.aiplatform.v1.BigQueryDestination getBigqueryDestination() { 3157 if (bigqueryDestinationBuilder_ == null) { 3158 if (destinationCase_ == 10) { 3159 return (com.google.cloud.aiplatform.v1.BigQueryDestination) destination_; 3160 } 3161 return com.google.cloud.aiplatform.v1.BigQueryDestination.getDefaultInstance(); 3162 } else { 3163 if (destinationCase_ == 10) { 3164 return bigqueryDestinationBuilder_.getMessage(); 3165 } 3166 return com.google.cloud.aiplatform.v1.BigQueryDestination.getDefaultInstance(); 3167 } 3168 } 3169 /** 3170 * 3171 * 3172 * <pre> 3173 * Only applicable to custom training with tabular Dataset with BigQuery 3174 * source. 3175 * The BigQuery project location where the training data is to be written 3176 * to. In the given project a new dataset is created with name 3177 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3178 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3179 * input data is written into that dataset. In the dataset three 3180 * tables are created, `training`, `validation` and `test`. 3181 * * AIP_DATA_FORMAT = "bigquery". 3182 * * AIP_TRAINING_DATA_URI = 3183 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3184 * * AIP_VALIDATION_DATA_URI = 3185 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3186 * * AIP_TEST_DATA_URI = 3187 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3188 * </pre> 3189 * 3190 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3191 */ setBigqueryDestination( com.google.cloud.aiplatform.v1.BigQueryDestination value)3192 public Builder setBigqueryDestination( 3193 com.google.cloud.aiplatform.v1.BigQueryDestination value) { 3194 if (bigqueryDestinationBuilder_ == null) { 3195 if (value == null) { 3196 throw new NullPointerException(); 3197 } 3198 destination_ = value; 3199 onChanged(); 3200 } else { 3201 bigqueryDestinationBuilder_.setMessage(value); 3202 } 3203 destinationCase_ = 10; 3204 return this; 3205 } 3206 /** 3207 * 3208 * 3209 * <pre> 3210 * Only applicable to custom training with tabular Dataset with BigQuery 3211 * source. 3212 * The BigQuery project location where the training data is to be written 3213 * to. In the given project a new dataset is created with name 3214 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3215 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3216 * input data is written into that dataset. In the dataset three 3217 * tables are created, `training`, `validation` and `test`. 3218 * * AIP_DATA_FORMAT = "bigquery". 3219 * * AIP_TRAINING_DATA_URI = 3220 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3221 * * AIP_VALIDATION_DATA_URI = 3222 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3223 * * AIP_TEST_DATA_URI = 3224 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3225 * </pre> 3226 * 3227 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3228 */ setBigqueryDestination( com.google.cloud.aiplatform.v1.BigQueryDestination.Builder builderForValue)3229 public Builder setBigqueryDestination( 3230 com.google.cloud.aiplatform.v1.BigQueryDestination.Builder builderForValue) { 3231 if (bigqueryDestinationBuilder_ == null) { 3232 destination_ = builderForValue.build(); 3233 onChanged(); 3234 } else { 3235 bigqueryDestinationBuilder_.setMessage(builderForValue.build()); 3236 } 3237 destinationCase_ = 10; 3238 return this; 3239 } 3240 /** 3241 * 3242 * 3243 * <pre> 3244 * Only applicable to custom training with tabular Dataset with BigQuery 3245 * source. 3246 * The BigQuery project location where the training data is to be written 3247 * to. In the given project a new dataset is created with name 3248 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3249 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3250 * input data is written into that dataset. In the dataset three 3251 * tables are created, `training`, `validation` and `test`. 3252 * * AIP_DATA_FORMAT = "bigquery". 3253 * * AIP_TRAINING_DATA_URI = 3254 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3255 * * AIP_VALIDATION_DATA_URI = 3256 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3257 * * AIP_TEST_DATA_URI = 3258 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3259 * </pre> 3260 * 3261 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3262 */ mergeBigqueryDestination( com.google.cloud.aiplatform.v1.BigQueryDestination value)3263 public Builder mergeBigqueryDestination( 3264 com.google.cloud.aiplatform.v1.BigQueryDestination value) { 3265 if (bigqueryDestinationBuilder_ == null) { 3266 if (destinationCase_ == 10 3267 && destination_ 3268 != com.google.cloud.aiplatform.v1.BigQueryDestination.getDefaultInstance()) { 3269 destination_ = 3270 com.google.cloud.aiplatform.v1.BigQueryDestination.newBuilder( 3271 (com.google.cloud.aiplatform.v1.BigQueryDestination) destination_) 3272 .mergeFrom(value) 3273 .buildPartial(); 3274 } else { 3275 destination_ = value; 3276 } 3277 onChanged(); 3278 } else { 3279 if (destinationCase_ == 10) { 3280 bigqueryDestinationBuilder_.mergeFrom(value); 3281 } else { 3282 bigqueryDestinationBuilder_.setMessage(value); 3283 } 3284 } 3285 destinationCase_ = 10; 3286 return this; 3287 } 3288 /** 3289 * 3290 * 3291 * <pre> 3292 * Only applicable to custom training with tabular Dataset with BigQuery 3293 * source. 3294 * The BigQuery project location where the training data is to be written 3295 * to. In the given project a new dataset is created with name 3296 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3297 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3298 * input data is written into that dataset. In the dataset three 3299 * tables are created, `training`, `validation` and `test`. 3300 * * AIP_DATA_FORMAT = "bigquery". 3301 * * AIP_TRAINING_DATA_URI = 3302 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3303 * * AIP_VALIDATION_DATA_URI = 3304 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3305 * * AIP_TEST_DATA_URI = 3306 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3307 * </pre> 3308 * 3309 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3310 */ clearBigqueryDestination()3311 public Builder clearBigqueryDestination() { 3312 if (bigqueryDestinationBuilder_ == null) { 3313 if (destinationCase_ == 10) { 3314 destinationCase_ = 0; 3315 destination_ = null; 3316 onChanged(); 3317 } 3318 } else { 3319 if (destinationCase_ == 10) { 3320 destinationCase_ = 0; 3321 destination_ = null; 3322 } 3323 bigqueryDestinationBuilder_.clear(); 3324 } 3325 return this; 3326 } 3327 /** 3328 * 3329 * 3330 * <pre> 3331 * Only applicable to custom training with tabular Dataset with BigQuery 3332 * source. 3333 * The BigQuery project location where the training data is to be written 3334 * to. In the given project a new dataset is created with name 3335 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3336 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3337 * input data is written into that dataset. In the dataset three 3338 * tables are created, `training`, `validation` and `test`. 3339 * * AIP_DATA_FORMAT = "bigquery". 3340 * * AIP_TRAINING_DATA_URI = 3341 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3342 * * AIP_VALIDATION_DATA_URI = 3343 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3344 * * AIP_TEST_DATA_URI = 3345 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3346 * </pre> 3347 * 3348 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3349 */ 3350 public com.google.cloud.aiplatform.v1.BigQueryDestination.Builder getBigqueryDestinationBuilder()3351 getBigqueryDestinationBuilder() { 3352 return getBigqueryDestinationFieldBuilder().getBuilder(); 3353 } 3354 /** 3355 * 3356 * 3357 * <pre> 3358 * Only applicable to custom training with tabular Dataset with BigQuery 3359 * source. 3360 * The BigQuery project location where the training data is to be written 3361 * to. In the given project a new dataset is created with name 3362 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3363 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3364 * input data is written into that dataset. In the dataset three 3365 * tables are created, `training`, `validation` and `test`. 3366 * * AIP_DATA_FORMAT = "bigquery". 3367 * * AIP_TRAINING_DATA_URI = 3368 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3369 * * AIP_VALIDATION_DATA_URI = 3370 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3371 * * AIP_TEST_DATA_URI = 3372 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3373 * </pre> 3374 * 3375 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3376 */ 3377 @java.lang.Override 3378 public com.google.cloud.aiplatform.v1.BigQueryDestinationOrBuilder getBigqueryDestinationOrBuilder()3379 getBigqueryDestinationOrBuilder() { 3380 if ((destinationCase_ == 10) && (bigqueryDestinationBuilder_ != null)) { 3381 return bigqueryDestinationBuilder_.getMessageOrBuilder(); 3382 } else { 3383 if (destinationCase_ == 10) { 3384 return (com.google.cloud.aiplatform.v1.BigQueryDestination) destination_; 3385 } 3386 return com.google.cloud.aiplatform.v1.BigQueryDestination.getDefaultInstance(); 3387 } 3388 } 3389 /** 3390 * 3391 * 3392 * <pre> 3393 * Only applicable to custom training with tabular Dataset with BigQuery 3394 * source. 3395 * The BigQuery project location where the training data is to be written 3396 * to. In the given project a new dataset is created with name 3397 * `dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 3398 * where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training 3399 * input data is written into that dataset. In the dataset three 3400 * tables are created, `training`, `validation` and `test`. 3401 * * AIP_DATA_FORMAT = "bigquery". 3402 * * AIP_TRAINING_DATA_URI = 3403 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.training" 3404 * * AIP_VALIDATION_DATA_URI = 3405 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.validation" 3406 * * AIP_TEST_DATA_URI = 3407 * "bigquery_destination.dataset_<dataset-id>_<annotation-type>_<time>.test" 3408 * </pre> 3409 * 3410 * <code>.google.cloud.aiplatform.v1.BigQueryDestination bigquery_destination = 10;</code> 3411 */ 3412 private com.google.protobuf.SingleFieldBuilderV3< 3413 com.google.cloud.aiplatform.v1.BigQueryDestination, 3414 com.google.cloud.aiplatform.v1.BigQueryDestination.Builder, 3415 com.google.cloud.aiplatform.v1.BigQueryDestinationOrBuilder> getBigqueryDestinationFieldBuilder()3416 getBigqueryDestinationFieldBuilder() { 3417 if (bigqueryDestinationBuilder_ == null) { 3418 if (!(destinationCase_ == 10)) { 3419 destination_ = com.google.cloud.aiplatform.v1.BigQueryDestination.getDefaultInstance(); 3420 } 3421 bigqueryDestinationBuilder_ = 3422 new com.google.protobuf.SingleFieldBuilderV3< 3423 com.google.cloud.aiplatform.v1.BigQueryDestination, 3424 com.google.cloud.aiplatform.v1.BigQueryDestination.Builder, 3425 com.google.cloud.aiplatform.v1.BigQueryDestinationOrBuilder>( 3426 (com.google.cloud.aiplatform.v1.BigQueryDestination) destination_, 3427 getParentForChildren(), 3428 isClean()); 3429 destination_ = null; 3430 } 3431 destinationCase_ = 10; 3432 onChanged(); 3433 return bigqueryDestinationBuilder_; 3434 } 3435 3436 private java.lang.Object datasetId_ = ""; 3437 /** 3438 * 3439 * 3440 * <pre> 3441 * Required. The ID of the Dataset in the same Project and Location which data 3442 * will be used to train the Model. The Dataset must use schema compatible 3443 * with Model being trained, and what is compatible should be described in the 3444 * used TrainingPipeline's [training_task_definition] 3445 * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. 3446 * For tabular Datasets, all their data is exported to training, to pick 3447 * and choose from. 3448 * </pre> 3449 * 3450 * <code>string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];</code> 3451 * 3452 * @return The datasetId. 3453 */ getDatasetId()3454 public java.lang.String getDatasetId() { 3455 java.lang.Object ref = datasetId_; 3456 if (!(ref instanceof java.lang.String)) { 3457 com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref; 3458 java.lang.String s = bs.toStringUtf8(); 3459 datasetId_ = s; 3460 return s; 3461 } else { 3462 return (java.lang.String) ref; 3463 } 3464 } 3465 /** 3466 * 3467 * 3468 * <pre> 3469 * Required. The ID of the Dataset in the same Project and Location which data 3470 * will be used to train the Model. The Dataset must use schema compatible 3471 * with Model being trained, and what is compatible should be described in the 3472 * used TrainingPipeline's [training_task_definition] 3473 * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. 3474 * For tabular Datasets, all their data is exported to training, to pick 3475 * and choose from. 3476 * </pre> 3477 * 3478 * <code>string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];</code> 3479 * 3480 * @return The bytes for datasetId. 3481 */ getDatasetIdBytes()3482 public com.google.protobuf.ByteString getDatasetIdBytes() { 3483 java.lang.Object ref = datasetId_; 3484 if (ref instanceof String) { 3485 com.google.protobuf.ByteString b = 3486 com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref); 3487 datasetId_ = b; 3488 return b; 3489 } else { 3490 return (com.google.protobuf.ByteString) ref; 3491 } 3492 } 3493 /** 3494 * 3495 * 3496 * <pre> 3497 * Required. The ID of the Dataset in the same Project and Location which data 3498 * will be used to train the Model. The Dataset must use schema compatible 3499 * with Model being trained, and what is compatible should be described in the 3500 * used TrainingPipeline's [training_task_definition] 3501 * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. 3502 * For tabular Datasets, all their data is exported to training, to pick 3503 * and choose from. 3504 * </pre> 3505 * 3506 * <code>string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];</code> 3507 * 3508 * @param value The datasetId to set. 3509 * @return This builder for chaining. 3510 */ setDatasetId(java.lang.String value)3511 public Builder setDatasetId(java.lang.String value) { 3512 if (value == null) { 3513 throw new NullPointerException(); 3514 } 3515 datasetId_ = value; 3516 bitField0_ |= 0x00000080; 3517 onChanged(); 3518 return this; 3519 } 3520 /** 3521 * 3522 * 3523 * <pre> 3524 * Required. The ID of the Dataset in the same Project and Location which data 3525 * will be used to train the Model. The Dataset must use schema compatible 3526 * with Model being trained, and what is compatible should be described in the 3527 * used TrainingPipeline's [training_task_definition] 3528 * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. 3529 * For tabular Datasets, all their data is exported to training, to pick 3530 * and choose from. 3531 * </pre> 3532 * 3533 * <code>string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];</code> 3534 * 3535 * @return This builder for chaining. 3536 */ clearDatasetId()3537 public Builder clearDatasetId() { 3538 datasetId_ = getDefaultInstance().getDatasetId(); 3539 bitField0_ = (bitField0_ & ~0x00000080); 3540 onChanged(); 3541 return this; 3542 } 3543 /** 3544 * 3545 * 3546 * <pre> 3547 * Required. The ID of the Dataset in the same Project and Location which data 3548 * will be used to train the Model. The Dataset must use schema compatible 3549 * with Model being trained, and what is compatible should be described in the 3550 * used TrainingPipeline's [training_task_definition] 3551 * [google.cloud.aiplatform.v1.TrainingPipeline.training_task_definition]. 3552 * For tabular Datasets, all their data is exported to training, to pick 3553 * and choose from. 3554 * </pre> 3555 * 3556 * <code>string dataset_id = 1 [(.google.api.field_behavior) = REQUIRED];</code> 3557 * 3558 * @param value The bytes for datasetId to set. 3559 * @return This builder for chaining. 3560 */ setDatasetIdBytes(com.google.protobuf.ByteString value)3561 public Builder setDatasetIdBytes(com.google.protobuf.ByteString value) { 3562 if (value == null) { 3563 throw new NullPointerException(); 3564 } 3565 checkByteStringIsUtf8(value); 3566 datasetId_ = value; 3567 bitField0_ |= 0x00000080; 3568 onChanged(); 3569 return this; 3570 } 3571 3572 private java.lang.Object annotationsFilter_ = ""; 3573 /** 3574 * 3575 * 3576 * <pre> 3577 * Applicable only to Datasets that have DataItems and Annotations. 3578 * A filter on Annotations of the Dataset. Only Annotations that both 3579 * match this filter and belong to DataItems not ignored by the split method 3580 * are used in respectively training, validation or test role, depending on 3581 * the role of the DataItem they are on (for the auto-assigned that role is 3582 * decided by Vertex AI). A filter with same syntax as the one used in 3583 * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] 3584 * may be used, but note here it filters across all Annotations of the 3585 * Dataset, and not just within a single DataItem. 3586 * </pre> 3587 * 3588 * <code>string annotations_filter = 6;</code> 3589 * 3590 * @return The annotationsFilter. 3591 */ getAnnotationsFilter()3592 public java.lang.String getAnnotationsFilter() { 3593 java.lang.Object ref = annotationsFilter_; 3594 if (!(ref instanceof java.lang.String)) { 3595 com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref; 3596 java.lang.String s = bs.toStringUtf8(); 3597 annotationsFilter_ = s; 3598 return s; 3599 } else { 3600 return (java.lang.String) ref; 3601 } 3602 } 3603 /** 3604 * 3605 * 3606 * <pre> 3607 * Applicable only to Datasets that have DataItems and Annotations. 3608 * A filter on Annotations of the Dataset. Only Annotations that both 3609 * match this filter and belong to DataItems not ignored by the split method 3610 * are used in respectively training, validation or test role, depending on 3611 * the role of the DataItem they are on (for the auto-assigned that role is 3612 * decided by Vertex AI). A filter with same syntax as the one used in 3613 * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] 3614 * may be used, but note here it filters across all Annotations of the 3615 * Dataset, and not just within a single DataItem. 3616 * </pre> 3617 * 3618 * <code>string annotations_filter = 6;</code> 3619 * 3620 * @return The bytes for annotationsFilter. 3621 */ getAnnotationsFilterBytes()3622 public com.google.protobuf.ByteString getAnnotationsFilterBytes() { 3623 java.lang.Object ref = annotationsFilter_; 3624 if (ref instanceof String) { 3625 com.google.protobuf.ByteString b = 3626 com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref); 3627 annotationsFilter_ = b; 3628 return b; 3629 } else { 3630 return (com.google.protobuf.ByteString) ref; 3631 } 3632 } 3633 /** 3634 * 3635 * 3636 * <pre> 3637 * Applicable only to Datasets that have DataItems and Annotations. 3638 * A filter on Annotations of the Dataset. Only Annotations that both 3639 * match this filter and belong to DataItems not ignored by the split method 3640 * are used in respectively training, validation or test role, depending on 3641 * the role of the DataItem they are on (for the auto-assigned that role is 3642 * decided by Vertex AI). A filter with same syntax as the one used in 3643 * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] 3644 * may be used, but note here it filters across all Annotations of the 3645 * Dataset, and not just within a single DataItem. 3646 * </pre> 3647 * 3648 * <code>string annotations_filter = 6;</code> 3649 * 3650 * @param value The annotationsFilter to set. 3651 * @return This builder for chaining. 3652 */ setAnnotationsFilter(java.lang.String value)3653 public Builder setAnnotationsFilter(java.lang.String value) { 3654 if (value == null) { 3655 throw new NullPointerException(); 3656 } 3657 annotationsFilter_ = value; 3658 bitField0_ |= 0x00000100; 3659 onChanged(); 3660 return this; 3661 } 3662 /** 3663 * 3664 * 3665 * <pre> 3666 * Applicable only to Datasets that have DataItems and Annotations. 3667 * A filter on Annotations of the Dataset. Only Annotations that both 3668 * match this filter and belong to DataItems not ignored by the split method 3669 * are used in respectively training, validation or test role, depending on 3670 * the role of the DataItem they are on (for the auto-assigned that role is 3671 * decided by Vertex AI). A filter with same syntax as the one used in 3672 * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] 3673 * may be used, but note here it filters across all Annotations of the 3674 * Dataset, and not just within a single DataItem. 3675 * </pre> 3676 * 3677 * <code>string annotations_filter = 6;</code> 3678 * 3679 * @return This builder for chaining. 3680 */ clearAnnotationsFilter()3681 public Builder clearAnnotationsFilter() { 3682 annotationsFilter_ = getDefaultInstance().getAnnotationsFilter(); 3683 bitField0_ = (bitField0_ & ~0x00000100); 3684 onChanged(); 3685 return this; 3686 } 3687 /** 3688 * 3689 * 3690 * <pre> 3691 * Applicable only to Datasets that have DataItems and Annotations. 3692 * A filter on Annotations of the Dataset. Only Annotations that both 3693 * match this filter and belong to DataItems not ignored by the split method 3694 * are used in respectively training, validation or test role, depending on 3695 * the role of the DataItem they are on (for the auto-assigned that role is 3696 * decided by Vertex AI). A filter with same syntax as the one used in 3697 * [ListAnnotations][google.cloud.aiplatform.v1.DatasetService.ListAnnotations] 3698 * may be used, but note here it filters across all Annotations of the 3699 * Dataset, and not just within a single DataItem. 3700 * </pre> 3701 * 3702 * <code>string annotations_filter = 6;</code> 3703 * 3704 * @param value The bytes for annotationsFilter to set. 3705 * @return This builder for chaining. 3706 */ setAnnotationsFilterBytes(com.google.protobuf.ByteString value)3707 public Builder setAnnotationsFilterBytes(com.google.protobuf.ByteString value) { 3708 if (value == null) { 3709 throw new NullPointerException(); 3710 } 3711 checkByteStringIsUtf8(value); 3712 annotationsFilter_ = value; 3713 bitField0_ |= 0x00000100; 3714 onChanged(); 3715 return this; 3716 } 3717 3718 private java.lang.Object annotationSchemaUri_ = ""; 3719 /** 3720 * 3721 * 3722 * <pre> 3723 * Applicable only to custom training with Datasets that have DataItems and 3724 * Annotations. 3725 * Cloud Storage URI that points to a YAML file describing the annotation 3726 * schema. The schema is defined as an OpenAPI 3.0.2 [Schema 3727 * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). 3728 * The schema files that can be used here are found in 3729 * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the 3730 * chosen schema must be consistent with 3731 * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the 3732 * Dataset specified by 3733 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. 3734 * Only Annotations that both match this schema and belong to DataItems not 3735 * ignored by the split method are used in respectively training, validation 3736 * or test role, depending on the role of the DataItem they are on. 3737 * When used in conjunction with 3738 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 3739 * the Annotations used for training are filtered by both 3740 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] 3741 * and 3742 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. 3743 * </pre> 3744 * 3745 * <code>string annotation_schema_uri = 9;</code> 3746 * 3747 * @return The annotationSchemaUri. 3748 */ getAnnotationSchemaUri()3749 public java.lang.String getAnnotationSchemaUri() { 3750 java.lang.Object ref = annotationSchemaUri_; 3751 if (!(ref instanceof java.lang.String)) { 3752 com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref; 3753 java.lang.String s = bs.toStringUtf8(); 3754 annotationSchemaUri_ = s; 3755 return s; 3756 } else { 3757 return (java.lang.String) ref; 3758 } 3759 } 3760 /** 3761 * 3762 * 3763 * <pre> 3764 * Applicable only to custom training with Datasets that have DataItems and 3765 * Annotations. 3766 * Cloud Storage URI that points to a YAML file describing the annotation 3767 * schema. The schema is defined as an OpenAPI 3.0.2 [Schema 3768 * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). 3769 * The schema files that can be used here are found in 3770 * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the 3771 * chosen schema must be consistent with 3772 * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the 3773 * Dataset specified by 3774 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. 3775 * Only Annotations that both match this schema and belong to DataItems not 3776 * ignored by the split method are used in respectively training, validation 3777 * or test role, depending on the role of the DataItem they are on. 3778 * When used in conjunction with 3779 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 3780 * the Annotations used for training are filtered by both 3781 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] 3782 * and 3783 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. 3784 * </pre> 3785 * 3786 * <code>string annotation_schema_uri = 9;</code> 3787 * 3788 * @return The bytes for annotationSchemaUri. 3789 */ getAnnotationSchemaUriBytes()3790 public com.google.protobuf.ByteString getAnnotationSchemaUriBytes() { 3791 java.lang.Object ref = annotationSchemaUri_; 3792 if (ref instanceof String) { 3793 com.google.protobuf.ByteString b = 3794 com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref); 3795 annotationSchemaUri_ = b; 3796 return b; 3797 } else { 3798 return (com.google.protobuf.ByteString) ref; 3799 } 3800 } 3801 /** 3802 * 3803 * 3804 * <pre> 3805 * Applicable only to custom training with Datasets that have DataItems and 3806 * Annotations. 3807 * Cloud Storage URI that points to a YAML file describing the annotation 3808 * schema. The schema is defined as an OpenAPI 3.0.2 [Schema 3809 * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). 3810 * The schema files that can be used here are found in 3811 * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the 3812 * chosen schema must be consistent with 3813 * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the 3814 * Dataset specified by 3815 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. 3816 * Only Annotations that both match this schema and belong to DataItems not 3817 * ignored by the split method are used in respectively training, validation 3818 * or test role, depending on the role of the DataItem they are on. 3819 * When used in conjunction with 3820 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 3821 * the Annotations used for training are filtered by both 3822 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] 3823 * and 3824 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. 3825 * </pre> 3826 * 3827 * <code>string annotation_schema_uri = 9;</code> 3828 * 3829 * @param value The annotationSchemaUri to set. 3830 * @return This builder for chaining. 3831 */ setAnnotationSchemaUri(java.lang.String value)3832 public Builder setAnnotationSchemaUri(java.lang.String value) { 3833 if (value == null) { 3834 throw new NullPointerException(); 3835 } 3836 annotationSchemaUri_ = value; 3837 bitField0_ |= 0x00000200; 3838 onChanged(); 3839 return this; 3840 } 3841 /** 3842 * 3843 * 3844 * <pre> 3845 * Applicable only to custom training with Datasets that have DataItems and 3846 * Annotations. 3847 * Cloud Storage URI that points to a YAML file describing the annotation 3848 * schema. The schema is defined as an OpenAPI 3.0.2 [Schema 3849 * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). 3850 * The schema files that can be used here are found in 3851 * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the 3852 * chosen schema must be consistent with 3853 * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the 3854 * Dataset specified by 3855 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. 3856 * Only Annotations that both match this schema and belong to DataItems not 3857 * ignored by the split method are used in respectively training, validation 3858 * or test role, depending on the role of the DataItem they are on. 3859 * When used in conjunction with 3860 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 3861 * the Annotations used for training are filtered by both 3862 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] 3863 * and 3864 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. 3865 * </pre> 3866 * 3867 * <code>string annotation_schema_uri = 9;</code> 3868 * 3869 * @return This builder for chaining. 3870 */ clearAnnotationSchemaUri()3871 public Builder clearAnnotationSchemaUri() { 3872 annotationSchemaUri_ = getDefaultInstance().getAnnotationSchemaUri(); 3873 bitField0_ = (bitField0_ & ~0x00000200); 3874 onChanged(); 3875 return this; 3876 } 3877 /** 3878 * 3879 * 3880 * <pre> 3881 * Applicable only to custom training with Datasets that have DataItems and 3882 * Annotations. 3883 * Cloud Storage URI that points to a YAML file describing the annotation 3884 * schema. The schema is defined as an OpenAPI 3.0.2 [Schema 3885 * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). 3886 * The schema files that can be used here are found in 3887 * gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the 3888 * chosen schema must be consistent with 3889 * [metadata][google.cloud.aiplatform.v1.Dataset.metadata_schema_uri] of the 3890 * Dataset specified by 3891 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id]. 3892 * Only Annotations that both match this schema and belong to DataItems not 3893 * ignored by the split method are used in respectively training, validation 3894 * or test role, depending on the role of the DataItem they are on. 3895 * When used in conjunction with 3896 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 3897 * the Annotations used for training are filtered by both 3898 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter] 3899 * and 3900 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri]. 3901 * </pre> 3902 * 3903 * <code>string annotation_schema_uri = 9;</code> 3904 * 3905 * @param value The bytes for annotationSchemaUri to set. 3906 * @return This builder for chaining. 3907 */ setAnnotationSchemaUriBytes(com.google.protobuf.ByteString value)3908 public Builder setAnnotationSchemaUriBytes(com.google.protobuf.ByteString value) { 3909 if (value == null) { 3910 throw new NullPointerException(); 3911 } 3912 checkByteStringIsUtf8(value); 3913 annotationSchemaUri_ = value; 3914 bitField0_ |= 0x00000200; 3915 onChanged(); 3916 return this; 3917 } 3918 3919 private java.lang.Object savedQueryId_ = ""; 3920 /** 3921 * 3922 * 3923 * <pre> 3924 * Only applicable to Datasets that have SavedQueries. 3925 * The ID of a SavedQuery (annotation set) under the Dataset specified by 3926 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used 3927 * for filtering Annotations for training. 3928 * Only Annotations that are associated with this SavedQuery are used in 3929 * respectively training. When used in conjunction with 3930 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 3931 * the Annotations used for training are filtered by both 3932 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 3933 * and 3934 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. 3935 * Only one of 3936 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 3937 * and 3938 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] 3939 * should be specified as both of them represent the same thing: problem type. 3940 * </pre> 3941 * 3942 * <code>string saved_query_id = 7;</code> 3943 * 3944 * @return The savedQueryId. 3945 */ getSavedQueryId()3946 public java.lang.String getSavedQueryId() { 3947 java.lang.Object ref = savedQueryId_; 3948 if (!(ref instanceof java.lang.String)) { 3949 com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref; 3950 java.lang.String s = bs.toStringUtf8(); 3951 savedQueryId_ = s; 3952 return s; 3953 } else { 3954 return (java.lang.String) ref; 3955 } 3956 } 3957 /** 3958 * 3959 * 3960 * <pre> 3961 * Only applicable to Datasets that have SavedQueries. 3962 * The ID of a SavedQuery (annotation set) under the Dataset specified by 3963 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used 3964 * for filtering Annotations for training. 3965 * Only Annotations that are associated with this SavedQuery are used in 3966 * respectively training. When used in conjunction with 3967 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 3968 * the Annotations used for training are filtered by both 3969 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 3970 * and 3971 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. 3972 * Only one of 3973 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 3974 * and 3975 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] 3976 * should be specified as both of them represent the same thing: problem type. 3977 * </pre> 3978 * 3979 * <code>string saved_query_id = 7;</code> 3980 * 3981 * @return The bytes for savedQueryId. 3982 */ getSavedQueryIdBytes()3983 public com.google.protobuf.ByteString getSavedQueryIdBytes() { 3984 java.lang.Object ref = savedQueryId_; 3985 if (ref instanceof String) { 3986 com.google.protobuf.ByteString b = 3987 com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref); 3988 savedQueryId_ = b; 3989 return b; 3990 } else { 3991 return (com.google.protobuf.ByteString) ref; 3992 } 3993 } 3994 /** 3995 * 3996 * 3997 * <pre> 3998 * Only applicable to Datasets that have SavedQueries. 3999 * The ID of a SavedQuery (annotation set) under the Dataset specified by 4000 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used 4001 * for filtering Annotations for training. 4002 * Only Annotations that are associated with this SavedQuery are used in 4003 * respectively training. When used in conjunction with 4004 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 4005 * the Annotations used for training are filtered by both 4006 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 4007 * and 4008 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. 4009 * Only one of 4010 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 4011 * and 4012 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] 4013 * should be specified as both of them represent the same thing: problem type. 4014 * </pre> 4015 * 4016 * <code>string saved_query_id = 7;</code> 4017 * 4018 * @param value The savedQueryId to set. 4019 * @return This builder for chaining. 4020 */ setSavedQueryId(java.lang.String value)4021 public Builder setSavedQueryId(java.lang.String value) { 4022 if (value == null) { 4023 throw new NullPointerException(); 4024 } 4025 savedQueryId_ = value; 4026 bitField0_ |= 0x00000400; 4027 onChanged(); 4028 return this; 4029 } 4030 /** 4031 * 4032 * 4033 * <pre> 4034 * Only applicable to Datasets that have SavedQueries. 4035 * The ID of a SavedQuery (annotation set) under the Dataset specified by 4036 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used 4037 * for filtering Annotations for training. 4038 * Only Annotations that are associated with this SavedQuery are used in 4039 * respectively training. When used in conjunction with 4040 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 4041 * the Annotations used for training are filtered by both 4042 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 4043 * and 4044 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. 4045 * Only one of 4046 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 4047 * and 4048 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] 4049 * should be specified as both of them represent the same thing: problem type. 4050 * </pre> 4051 * 4052 * <code>string saved_query_id = 7;</code> 4053 * 4054 * @return This builder for chaining. 4055 */ clearSavedQueryId()4056 public Builder clearSavedQueryId() { 4057 savedQueryId_ = getDefaultInstance().getSavedQueryId(); 4058 bitField0_ = (bitField0_ & ~0x00000400); 4059 onChanged(); 4060 return this; 4061 } 4062 /** 4063 * 4064 * 4065 * <pre> 4066 * Only applicable to Datasets that have SavedQueries. 4067 * The ID of a SavedQuery (annotation set) under the Dataset specified by 4068 * [dataset_id][google.cloud.aiplatform.v1.InputDataConfig.dataset_id] used 4069 * for filtering Annotations for training. 4070 * Only Annotations that are associated with this SavedQuery are used in 4071 * respectively training. When used in conjunction with 4072 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter], 4073 * the Annotations used for training are filtered by both 4074 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 4075 * and 4076 * [annotations_filter][google.cloud.aiplatform.v1.InputDataConfig.annotations_filter]. 4077 * Only one of 4078 * [saved_query_id][google.cloud.aiplatform.v1.InputDataConfig.saved_query_id] 4079 * and 4080 * [annotation_schema_uri][google.cloud.aiplatform.v1.InputDataConfig.annotation_schema_uri] 4081 * should be specified as both of them represent the same thing: problem type. 4082 * </pre> 4083 * 4084 * <code>string saved_query_id = 7;</code> 4085 * 4086 * @param value The bytes for savedQueryId to set. 4087 * @return This builder for chaining. 4088 */ setSavedQueryIdBytes(com.google.protobuf.ByteString value)4089 public Builder setSavedQueryIdBytes(com.google.protobuf.ByteString value) { 4090 if (value == null) { 4091 throw new NullPointerException(); 4092 } 4093 checkByteStringIsUtf8(value); 4094 savedQueryId_ = value; 4095 bitField0_ |= 0x00000400; 4096 onChanged(); 4097 return this; 4098 } 4099 4100 private boolean persistMlUseAssignment_; 4101 /** 4102 * 4103 * 4104 * <pre> 4105 * Whether to persist the ML use assignment to data item system labels. 4106 * </pre> 4107 * 4108 * <code>bool persist_ml_use_assignment = 11;</code> 4109 * 4110 * @return The persistMlUseAssignment. 4111 */ 4112 @java.lang.Override getPersistMlUseAssignment()4113 public boolean getPersistMlUseAssignment() { 4114 return persistMlUseAssignment_; 4115 } 4116 /** 4117 * 4118 * 4119 * <pre> 4120 * Whether to persist the ML use assignment to data item system labels. 4121 * </pre> 4122 * 4123 * <code>bool persist_ml_use_assignment = 11;</code> 4124 * 4125 * @param value The persistMlUseAssignment to set. 4126 * @return This builder for chaining. 4127 */ setPersistMlUseAssignment(boolean value)4128 public Builder setPersistMlUseAssignment(boolean value) { 4129 4130 persistMlUseAssignment_ = value; 4131 bitField0_ |= 0x00000800; 4132 onChanged(); 4133 return this; 4134 } 4135 /** 4136 * 4137 * 4138 * <pre> 4139 * Whether to persist the ML use assignment to data item system labels. 4140 * </pre> 4141 * 4142 * <code>bool persist_ml_use_assignment = 11;</code> 4143 * 4144 * @return This builder for chaining. 4145 */ clearPersistMlUseAssignment()4146 public Builder clearPersistMlUseAssignment() { 4147 bitField0_ = (bitField0_ & ~0x00000800); 4148 persistMlUseAssignment_ = false; 4149 onChanged(); 4150 return this; 4151 } 4152 4153 @java.lang.Override setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields)4154 public final Builder setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields) { 4155 return super.setUnknownFields(unknownFields); 4156 } 4157 4158 @java.lang.Override mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields)4159 public final Builder mergeUnknownFields( 4160 final com.google.protobuf.UnknownFieldSet unknownFields) { 4161 return super.mergeUnknownFields(unknownFields); 4162 } 4163 4164 // @@protoc_insertion_point(builder_scope:google.cloud.aiplatform.v1.InputDataConfig) 4165 } 4166 4167 // @@protoc_insertion_point(class_scope:google.cloud.aiplatform.v1.InputDataConfig) 4168 private static final com.google.cloud.aiplatform.v1.InputDataConfig DEFAULT_INSTANCE; 4169 4170 static { 4171 DEFAULT_INSTANCE = new com.google.cloud.aiplatform.v1.InputDataConfig(); 4172 } 4173 getDefaultInstance()4174 public static com.google.cloud.aiplatform.v1.InputDataConfig getDefaultInstance() { 4175 return DEFAULT_INSTANCE; 4176 } 4177 4178 private static final com.google.protobuf.Parser<InputDataConfig> PARSER = 4179 new com.google.protobuf.AbstractParser<InputDataConfig>() { 4180 @java.lang.Override 4181 public InputDataConfig parsePartialFrom( 4182 com.google.protobuf.CodedInputStream input, 4183 com.google.protobuf.ExtensionRegistryLite extensionRegistry) 4184 throws com.google.protobuf.InvalidProtocolBufferException { 4185 Builder builder = newBuilder(); 4186 try { 4187 builder.mergeFrom(input, extensionRegistry); 4188 } catch (com.google.protobuf.InvalidProtocolBufferException e) { 4189 throw e.setUnfinishedMessage(builder.buildPartial()); 4190 } catch (com.google.protobuf.UninitializedMessageException e) { 4191 throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial()); 4192 } catch (java.io.IOException e) { 4193 throw new com.google.protobuf.InvalidProtocolBufferException(e) 4194 .setUnfinishedMessage(builder.buildPartial()); 4195 } 4196 return builder.buildPartial(); 4197 } 4198 }; 4199 parser()4200 public static com.google.protobuf.Parser<InputDataConfig> parser() { 4201 return PARSER; 4202 } 4203 4204 @java.lang.Override getParserForType()4205 public com.google.protobuf.Parser<InputDataConfig> getParserForType() { 4206 return PARSER; 4207 } 4208 4209 @java.lang.Override getDefaultInstanceForType()4210 public com.google.cloud.aiplatform.v1.InputDataConfig getDefaultInstanceForType() { 4211 return DEFAULT_INSTANCE; 4212 } 4213 } 4214