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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: proto/clarifai/api/resources.proto
package com.clarifai.grpc.api;
/**
*
* TaskWorkerPartitionedStrategyInfo
*
*
* Protobuf type {@code clarifai.api.TaskWorkerPartitionedStrategyInfo}
*/
public final class TaskWorkerPartitionedStrategyInfo extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:clarifai.api.TaskWorkerPartitionedStrategyInfo)
TaskWorkerPartitionedStrategyInfoOrBuilder {
private static final long serialVersionUID = 0L;
// Use TaskWorkerPartitionedStrategyInfo.newBuilder() to construct.
private TaskWorkerPartitionedStrategyInfo(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private TaskWorkerPartitionedStrategyInfo() {
type_ = 0;
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(
UnusedPrivateParameter unused) {
return new TaskWorkerPartitionedStrategyInfo();
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@java.lang.Override
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return this.unknownFields;
}
private TaskWorkerPartitionedStrategyInfo(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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this();
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public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return com.clarifai.grpc.api.Resources.internal_static_clarifai_api_TaskWorkerPartitionedStrategyInfo_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.clarifai.grpc.api.Resources.internal_static_clarifai_api_TaskWorkerPartitionedStrategyInfo_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.class, com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.Builder.class);
}
/**
* Protobuf enum {@code clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy}
*/
public enum TaskWorkerPartitionedStrategy
implements com.google.protobuf.ProtocolMessageEnum {
/**
* PARTITIONED_WORKER_STRATEGY_NOT_SET = 0;
*/
PARTITIONED_WORKER_STRATEGY_NOT_SET(0),
/**
*
* Each worker will label (approximately) the same number of inputs.
*
*
* EVENLY = 1;
*/
EVENLY(1),
/**
*
* Each worker will have an assigned weight.
* See weights field for more details.
*
*
* WEIGHTED = 2;
*/
WEIGHTED(2),
UNRECOGNIZED(-1),
;
/**
* PARTITIONED_WORKER_STRATEGY_NOT_SET = 0;
*/
public static final int PARTITIONED_WORKER_STRATEGY_NOT_SET_VALUE = 0;
/**
*
* Each worker will label (approximately) the same number of inputs.
*
*
* EVENLY = 1;
*/
public static final int EVENLY_VALUE = 1;
/**
*
* Each worker will have an assigned weight.
* See weights field for more details.
*
*
* WEIGHTED = 2;
*/
public static final int WEIGHTED_VALUE = 2;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @param value The numeric wire value of the corresponding enum entry.
* @return The enum associated with the given numeric wire value.
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static TaskWorkerPartitionedStrategy valueOf(int value) {
return forNumber(value);
}
/**
* @param value The numeric wire value of the corresponding enum entry.
* @return The enum associated with the given numeric wire value.
*/
public static TaskWorkerPartitionedStrategy forNumber(int value) {
switch (value) {
case 0: return PARTITIONED_WORKER_STRATEGY_NOT_SET;
case 1: return EVENLY;
case 2: return WEIGHTED;
default: return null;
}
}
public static com.google.protobuf.Internal.EnumLiteMap
internalGetValueMap() {
return internalValueMap;
}
private static final com.google.protobuf.Internal.EnumLiteMap<
TaskWorkerPartitionedStrategy> internalValueMap =
new com.google.protobuf.Internal.EnumLiteMap() {
public TaskWorkerPartitionedStrategy findValueByNumber(int number) {
return TaskWorkerPartitionedStrategy.forNumber(number);
}
};
public final com.google.protobuf.Descriptors.EnumValueDescriptor
getValueDescriptor() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalStateException(
"Can't get the descriptor of an unrecognized enum value.");
}
return getDescriptor().getValues().get(ordinal());
}
public final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.getDescriptor().getEnumTypes().get(0);
}
private static final TaskWorkerPartitionedStrategy[] VALUES = values();
public static TaskWorkerPartitionedStrategy valueOf(
com.google.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
}
if (desc.getIndex() == -1) {
return UNRECOGNIZED;
}
return VALUES[desc.getIndex()];
}
private final int value;
private TaskWorkerPartitionedStrategy(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy)
}
public static final int TYPE_FIELD_NUMBER = 1;
private int type_;
/**
*
* Define how the partitioning should work.
*
*
* .clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy type = 1;
* @return The enum numeric value on the wire for type.
*/
@java.lang.Override public int getTypeValue() {
return type_;
}
/**
*
* Define how the partitioning should work.
*
*
* .clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy type = 1;
* @return The type.
*/
@java.lang.Override public com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy getType() {
@SuppressWarnings("deprecation")
com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy result = com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy.valueOf(type_);
return result == null ? com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy.UNRECOGNIZED : result;
}
public static final int WORKERS_PER_INPUT_FIELD_NUMBER = 2;
private int workersPerInput_;
/**
*
* How many workers will label each input.
*
*
* int32 workers_per_input = 2;
* @return The workersPerInput.
*/
@java.lang.Override
public int getWorkersPerInput() {
return workersPerInput_;
}
public static final int WEIGHTS_FIELD_NUMBER = 3;
private com.google.protobuf.Struct weights_;
/**
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
*
* .google.protobuf.Struct weights = 3;
* @return Whether the weights field is set.
*/
@java.lang.Override
public boolean hasWeights() {
return weights_ != null;
}
/**
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
*
* Protobuf type {@code clarifai.api.TaskWorkerPartitionedStrategyInfo}
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:clarifai.api.TaskWorkerPartitionedStrategyInfo)
com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfoOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return com.clarifai.grpc.api.Resources.internal_static_clarifai_api_TaskWorkerPartitionedStrategyInfo_descriptor;
}
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internalGetFieldAccessorTable() {
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// Construct using com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.newBuilder()
private Builder() {
maybeForceBuilderInitialization();
}
private Builder(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
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maybeForceBuilderInitialization();
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private void maybeForceBuilderInitialization() {
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super.clear();
type_ = 0;
workersPerInput_ = 0;
if (weightsBuilder_ == null) {
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weights_ = null;
weightsBuilder_ = null;
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getDescriptorForType() {
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@java.lang.Override
public com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo build() {
com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
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@java.lang.Override
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com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo result = new com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo(this);
result.type_ = type_;
result.workersPerInput_ = workersPerInput_;
if (weightsBuilder_ == null) {
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onBuilt();
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@java.lang.Override
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com.google.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
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public Builder mergeFrom(com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo other) {
if (other == com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.getDefaultInstance()) return this;
if (other.type_ != 0) {
setTypeValue(other.getTypeValue());
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com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo parsedMessage = null;
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parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
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private int type_ = 0;
/**
*
* Define how the partitioning should work.
*
*
* .clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy type = 1;
* @return The enum numeric value on the wire for type.
*/
@java.lang.Override public int getTypeValue() {
return type_;
}
/**
*
* Define how the partitioning should work.
*
*
* .clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy type = 1;
* @param value The enum numeric value on the wire for type to set.
* @return This builder for chaining.
*/
public Builder setTypeValue(int value) {
type_ = value;
onChanged();
return this;
}
/**
*
* Define how the partitioning should work.
*
*
* .clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy type = 1;
* @return The type.
*/
@java.lang.Override
public com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy getType() {
@SuppressWarnings("deprecation")
com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy result = com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy.valueOf(type_);
return result == null ? com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy.UNRECOGNIZED : result;
}
/**
*
* Define how the partitioning should work.
*
*
* .clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy type = 1;
* @param value The type to set.
* @return This builder for chaining.
*/
public Builder setType(com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy value) {
if (value == null) {
throw new NullPointerException();
}
type_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Define how the partitioning should work.
*
*
* .clarifai.api.TaskWorkerPartitionedStrategyInfo.TaskWorkerPartitionedStrategy type = 1;
* @return This builder for chaining.
*/
public Builder clearType() {
type_ = 0;
onChanged();
return this;
}
private int workersPerInput_ ;
/**
*
* How many workers will label each input.
*
*
* int32 workers_per_input = 2;
* @return The workersPerInput.
*/
@java.lang.Override
public int getWorkersPerInput() {
return workersPerInput_;
}
/**
*
* How many workers will label each input.
*
*
* int32 workers_per_input = 2;
* @param value The workersPerInput to set.
* @return This builder for chaining.
*/
public Builder setWorkersPerInput(int value) {
workersPerInput_ = value;
onChanged();
return this;
}
/**
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
*
* .google.protobuf.Struct weights = 3;
* @return Whether the weights field is set.
*/
public boolean hasWeights() {
return weightsBuilder_ != null || weights_ != null;
}
/**
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
*
* .google.protobuf.Struct weights = 3;
*/
public Builder setWeights(com.google.protobuf.Struct value) {
if (weightsBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
weights_ = value;
onChanged();
} else {
weightsBuilder_.setMessage(value);
}
return this;
}
/**
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
* In case of weighted partitioning, map user ids to weights.
* Each labeler will be assigned work proportional to its own weight as compared to the sum of total weight.
* EXAMPLE:
* If we have 3 workers, and weights = {1: 30, 2: 30, 3: 40},
* then first worker will have assigned 30% of the work,
* second worker will have assigned 30% of the work,
* and third worker will have assigned 40% of the work.
* You may use weights which add up to 100, but it's not necessary.
* For example, weights {1: 30, 2: 30, 3: 40} are equivalent with {1: 3, 2: 3, 3: 4}
* because they represent the same percentages: {1: 30%, 2: 30%, 3: 40%}.
* NOTE:
* Note that no worker should be assigned a weight percentage greater than 1/workers_per_input.
* It is mathematically impossible to partition the work in such a case.
* Why? Say, we have 3 workers. And workers_per_input = 2, i.e. each input must be labeled by 2 workers.
* Let's assign weights {1: 51%, 2: 25%, 3: 24%}.
* Note that first worker has a weight percentage higher than 1/workers_per_input = 1/2 = 50%.
* If we have 100 inputs, then a total of 100 * workers_per_input = 200 cumulative inputs will be labeled by these 3 workers.
* Worker 1 should label 102 cumulative inputs, while worker 2 and worker 3 will label 98 cumulative inputs together.
* No matter how we assign the 98 cumulative inputs, the 2 workers will be able to label up to 98 actual inputs.
* This means the remaining 2 inputs will be labeled only by worker 1. This contradicts the worker_per_input = 2 requirement.
*
*
* .google.protobuf.Struct weights = 3;
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.protobuf.Struct, com.google.protobuf.Struct.Builder, com.google.protobuf.StructOrBuilder>
getWeightsFieldBuilder() {
if (weightsBuilder_ == null) {
weightsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
com.google.protobuf.Struct, com.google.protobuf.Struct.Builder, com.google.protobuf.StructOrBuilder>(
getWeights(),
getParentForChildren(),
isClean());
weights_ = null;
}
return weightsBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:clarifai.api.TaskWorkerPartitionedStrategyInfo)
}
// @@protoc_insertion_point(class_scope:clarifai.api.TaskWorkerPartitionedStrategyInfo)
private static final com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo();
}
public static com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
@java.lang.Override
public TaskWorkerPartitionedStrategyInfo parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return new TaskWorkerPartitionedStrategyInfo(input, extensionRegistry);
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public com.clarifai.grpc.api.TaskWorkerPartitionedStrategyInfo getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
