cloud.google.com/go/aiplatform@v1.106.0/apiv1beta1/aiplatformpb/model_monitoring.pb.go

// Copyright 2025 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// 	protoc-gen-go v1.35.2
// 	protoc        v4.25.7
// source: google/cloud/aiplatform/v1beta1/model_monitoring.proto

package aiplatformpb

import (
	_ "google.golang.org/genproto/googleapis/api/annotations"
	protoreflect "google.golang.org/protobuf/reflect/protoreflect"
	protoimpl "google.golang.org/protobuf/runtime/protoimpl"
	reflect "reflect"
	sync "sync"
)

const (
	// Verify that this generated code is sufficiently up-to-date.
	_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
	// Verify that runtime/protoimpl is sufficiently up-to-date.
	_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)

// The storage format of the predictions generated BatchPrediction job.
type ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat int32

const (
	// Should not be set.
	ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PREDICTION_FORMAT_UNSPECIFIED ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat = 0
	// Predictions are in JSONL files.
	ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_JSONL ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat = 2
	// Predictions are in BigQuery.
	ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_BIGQUERY ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat = 3
)

// Enum value maps for ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat.
var (
	ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat_name = map[int32]string{
		0: "PREDICTION_FORMAT_UNSPECIFIED",
		2: "JSONL",
		3: "BIGQUERY",
	}
	ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat_value = map[string]int32{
		"PREDICTION_FORMAT_UNSPECIFIED": 0,
		"JSONL":                         2,
		"BIGQUERY":                      3,
	}
)

func (x ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) Enum() *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat {
	p := new(ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat)
	*p = x
	return p
}

func (x ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) String() string {
	return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}

func (ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) Descriptor() protoreflect.EnumDescriptor {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_enumTypes[0].Descriptor()
}

func (ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) Type() protoreflect.EnumType {
	return &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_enumTypes[0]
}

func (x ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) Number() protoreflect.EnumNumber {
	return protoreflect.EnumNumber(x)
}

// Deprecated: Use ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat.Descriptor instead.
func (ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat) EnumDescriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{1, 3, 0, 0}
}

// The model monitoring configuration used for Batch Prediction Job.
type ModelMonitoringConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Model monitoring objective config.
	ObjectiveConfigs []*ModelMonitoringObjectiveConfig `protobuf:"bytes,3,rep,name=objective_configs,json=objectiveConfigs,proto3" json:"objective_configs,omitempty"`
	// Model monitoring alert config.
	AlertConfig *ModelMonitoringAlertConfig `protobuf:"bytes,2,opt,name=alert_config,json=alertConfig,proto3" json:"alert_config,omitempty"`
	// YAML schema file uri in Cloud Storage describing the format of a single
	// instance that you want Tensorflow Data Validation (TFDV) to analyze.
	//
	// If there are any data type differences between predict instance and TFDV
	// instance, this field can be used to override the schema.
	// For models trained with Vertex AI, this field must be set as all the
	// fields in predict instance formatted as string.
	AnalysisInstanceSchemaUri string `protobuf:"bytes,4,opt,name=analysis_instance_schema_uri,json=analysisInstanceSchemaUri,proto3" json:"analysis_instance_schema_uri,omitempty"`
	// A Google Cloud Storage location for batch prediction model monitoring to
	// dump statistics and anomalies.
	// If not provided, a folder will be created in customer project to hold
	// statistics and anomalies.
	StatsAnomaliesBaseDirectory *GcsDestination `protobuf:"bytes,5,opt,name=stats_anomalies_base_directory,json=statsAnomaliesBaseDirectory,proto3" json:"stats_anomalies_base_directory,omitempty"`
}

func (x *ModelMonitoringConfig) Reset() {
	*x = ModelMonitoringConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[0]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringConfig) ProtoMessage() {}

func (x *ModelMonitoringConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[0]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{0}
}

func (x *ModelMonitoringConfig) GetObjectiveConfigs() []*ModelMonitoringObjectiveConfig {
	if x != nil {
		return x.ObjectiveConfigs
	}
	return nil
}

func (x *ModelMonitoringConfig) GetAlertConfig() *ModelMonitoringAlertConfig {
	if x != nil {
		return x.AlertConfig
	}
	return nil
}

func (x *ModelMonitoringConfig) GetAnalysisInstanceSchemaUri() string {
	if x != nil {
		return x.AnalysisInstanceSchemaUri
	}
	return ""
}

func (x *ModelMonitoringConfig) GetStatsAnomaliesBaseDirectory() *GcsDestination {
	if x != nil {
		return x.StatsAnomaliesBaseDirectory
	}
	return nil
}

// The objective configuration for model monitoring, including the information
// needed to detect anomalies for one particular model.
type ModelMonitoringObjectiveConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Training dataset for models. This field has to be set only if
	// TrainingPredictionSkewDetectionConfig is specified.
	TrainingDataset *ModelMonitoringObjectiveConfig_TrainingDataset `protobuf:"bytes,1,opt,name=training_dataset,json=trainingDataset,proto3" json:"training_dataset,omitempty"`
	// The config for skew between training data and prediction data.
	TrainingPredictionSkewDetectionConfig *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig `protobuf:"bytes,2,opt,name=training_prediction_skew_detection_config,json=trainingPredictionSkewDetectionConfig,proto3" json:"training_prediction_skew_detection_config,omitempty"`
	// The config for drift of prediction data.
	PredictionDriftDetectionConfig *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig `protobuf:"bytes,3,opt,name=prediction_drift_detection_config,json=predictionDriftDetectionConfig,proto3" json:"prediction_drift_detection_config,omitempty"`
	// The config for integrating with Vertex Explainable AI.
	ExplanationConfig *ModelMonitoringObjectiveConfig_ExplanationConfig `protobuf:"bytes,5,opt,name=explanation_config,json=explanationConfig,proto3" json:"explanation_config,omitempty"`
}

func (x *ModelMonitoringObjectiveConfig) Reset() {
	*x = ModelMonitoringObjectiveConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[1]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringObjectiveConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringObjectiveConfig) ProtoMessage() {}

func (x *ModelMonitoringObjectiveConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[1]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringObjectiveConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{1}
}

func (x *ModelMonitoringObjectiveConfig) GetTrainingDataset() *ModelMonitoringObjectiveConfig_TrainingDataset {
	if x != nil {
		return x.TrainingDataset
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig) GetTrainingPredictionSkewDetectionConfig() *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig {
	if x != nil {
		return x.TrainingPredictionSkewDetectionConfig
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig) GetPredictionDriftDetectionConfig() *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig {
	if x != nil {
		return x.PredictionDriftDetectionConfig
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig) GetExplanationConfig() *ModelMonitoringObjectiveConfig_ExplanationConfig {
	if x != nil {
		return x.ExplanationConfig
	}
	return nil
}

// The alert config for model monitoring.
type ModelMonitoringAlertConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Types that are assignable to Alert:
	//
	//	*ModelMonitoringAlertConfig_EmailAlertConfig_
	Alert isModelMonitoringAlertConfig_Alert `protobuf_oneof:"alert"`
	// Dump the anomalies to Cloud Logging. The anomalies will be put to json
	// payload encoded from proto
	// [google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry][].
	// This can be further sinked to Pub/Sub or any other services supported
	// by Cloud Logging.
	EnableLogging bool `protobuf:"varint,2,opt,name=enable_logging,json=enableLogging,proto3" json:"enable_logging,omitempty"`
	// Resource names of the NotificationChannels to send alert.
	// Must be of the format
	// `projects/<project_id_or_number>/notificationChannels/<channel_id>`
	NotificationChannels []string `protobuf:"bytes,3,rep,name=notification_channels,json=notificationChannels,proto3" json:"notification_channels,omitempty"`
}

func (x *ModelMonitoringAlertConfig) Reset() {
	*x = ModelMonitoringAlertConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[2]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringAlertConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringAlertConfig) ProtoMessage() {}

func (x *ModelMonitoringAlertConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[2]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringAlertConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringAlertConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{2}
}

func (m *ModelMonitoringAlertConfig) GetAlert() isModelMonitoringAlertConfig_Alert {
	if m != nil {
		return m.Alert
	}
	return nil
}

func (x *ModelMonitoringAlertConfig) GetEmailAlertConfig() *ModelMonitoringAlertConfig_EmailAlertConfig {
	if x, ok := x.GetAlert().(*ModelMonitoringAlertConfig_EmailAlertConfig_); ok {
		return x.EmailAlertConfig
	}
	return nil
}

func (x *ModelMonitoringAlertConfig) GetEnableLogging() bool {
	if x != nil {
		return x.EnableLogging
	}
	return false
}

func (x *ModelMonitoringAlertConfig) GetNotificationChannels() []string {
	if x != nil {
		return x.NotificationChannels
	}
	return nil
}

type isModelMonitoringAlertConfig_Alert interface {
	isModelMonitoringAlertConfig_Alert()
}

type ModelMonitoringAlertConfig_EmailAlertConfig_ struct {
	// Email alert config.
	EmailAlertConfig *ModelMonitoringAlertConfig_EmailAlertConfig `protobuf:"bytes,1,opt,name=email_alert_config,json=emailAlertConfig,proto3,oneof"`
}

func (*ModelMonitoringAlertConfig_EmailAlertConfig_) isModelMonitoringAlertConfig_Alert() {}

// The config for feature monitoring threshold.
type ThresholdConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Types that are assignable to Threshold:
	//
	//	*ThresholdConfig_Value
	Threshold isThresholdConfig_Threshold `protobuf_oneof:"threshold"`
}

func (x *ThresholdConfig) Reset() {
	*x = ThresholdConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[3]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ThresholdConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ThresholdConfig) ProtoMessage() {}

func (x *ThresholdConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[3]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ThresholdConfig.ProtoReflect.Descriptor instead.
func (*ThresholdConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{3}
}

func (m *ThresholdConfig) GetThreshold() isThresholdConfig_Threshold {
	if m != nil {
		return m.Threshold
	}
	return nil
}

func (x *ThresholdConfig) GetValue() float64 {
	if x, ok := x.GetThreshold().(*ThresholdConfig_Value); ok {
		return x.Value
	}
	return 0
}

type isThresholdConfig_Threshold interface {
	isThresholdConfig_Threshold()
}

type ThresholdConfig_Value struct {
	// Specify a threshold value that can trigger the alert.
	// If this threshold config is for feature distribution distance:
	//  1. For categorical feature, the distribution distance is calculated by
	//     L-inifinity norm.
	//  2. For numerical feature, the distribution distance is calculated by
	//     Jensen–Shannon divergence.
	//
	// Each feature must have a non-zero threshold if they need to be monitored.
	// Otherwise no alert will be triggered for that feature.
	Value float64 `protobuf:"fixed64,1,opt,name=value,proto3,oneof"`
}

func (*ThresholdConfig_Value) isThresholdConfig_Threshold() {}

// Sampling Strategy for logging, can be for both training and prediction
// dataset.
type SamplingStrategy struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Random sample config. Will support more sampling strategies later.
	RandomSampleConfig *SamplingStrategy_RandomSampleConfig `protobuf:"bytes,1,opt,name=random_sample_config,json=randomSampleConfig,proto3" json:"random_sample_config,omitempty"`
}

func (x *SamplingStrategy) Reset() {
	*x = SamplingStrategy{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[4]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *SamplingStrategy) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*SamplingStrategy) ProtoMessage() {}

func (x *SamplingStrategy) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[4]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use SamplingStrategy.ProtoReflect.Descriptor instead.
func (*SamplingStrategy) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{4}
}

func (x *SamplingStrategy) GetRandomSampleConfig() *SamplingStrategy_RandomSampleConfig {
	if x != nil {
		return x.RandomSampleConfig
	}
	return nil
}

// Training Dataset information.
type ModelMonitoringObjectiveConfig_TrainingDataset struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Types that are assignable to DataSource:
	//
	//	*ModelMonitoringObjectiveConfig_TrainingDataset_Dataset
	//	*ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource
	//	*ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource
	DataSource isModelMonitoringObjectiveConfig_TrainingDataset_DataSource `protobuf_oneof:"data_source"`
	// Data format of the dataset, only applicable if the input is from
	// Google Cloud Storage.
	// The possible formats are:
	//
	// "tf-record"
	// The source file is a TFRecord file.
	//
	// "csv"
	// The source file is a CSV file.
	// "jsonl"
	// The source file is a JSONL file.
	DataFormat string `protobuf:"bytes,2,opt,name=data_format,json=dataFormat,proto3" json:"data_format,omitempty"`
	// The target field name the model is to predict.
	// This field will be excluded when doing Predict and (or) Explain for the
	// training data.
	TargetField string `protobuf:"bytes,6,opt,name=target_field,json=targetField,proto3" json:"target_field,omitempty"`
	// Strategy to sample data from Training Dataset.
	// If not set, we process the whole dataset.
	LoggingSamplingStrategy *SamplingStrategy `protobuf:"bytes,7,opt,name=logging_sampling_strategy,json=loggingSamplingStrategy,proto3" json:"logging_sampling_strategy,omitempty"`
}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) Reset() {
	*x = ModelMonitoringObjectiveConfig_TrainingDataset{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[5]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringObjectiveConfig_TrainingDataset) ProtoMessage() {}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[5]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringObjectiveConfig_TrainingDataset.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_TrainingDataset) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{1, 0}
}

func (m *ModelMonitoringObjectiveConfig_TrainingDataset) GetDataSource() isModelMonitoringObjectiveConfig_TrainingDataset_DataSource {
	if m != nil {
		return m.DataSource
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetDataset() string {
	if x, ok := x.GetDataSource().(*ModelMonitoringObjectiveConfig_TrainingDataset_Dataset); ok {
		return x.Dataset
	}
	return ""
}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetGcsSource() *GcsSource {
	if x, ok := x.GetDataSource().(*ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource); ok {
		return x.GcsSource
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetBigquerySource() *BigQuerySource {
	if x, ok := x.GetDataSource().(*ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource); ok {
		return x.BigquerySource
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetDataFormat() string {
	if x != nil {
		return x.DataFormat
	}
	return ""
}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetTargetField() string {
	if x != nil {
		return x.TargetField
	}
	return ""
}

func (x *ModelMonitoringObjectiveConfig_TrainingDataset) GetLoggingSamplingStrategy() *SamplingStrategy {
	if x != nil {
		return x.LoggingSamplingStrategy
	}
	return nil
}

type isModelMonitoringObjectiveConfig_TrainingDataset_DataSource interface {
	isModelMonitoringObjectiveConfig_TrainingDataset_DataSource()
}

type ModelMonitoringObjectiveConfig_TrainingDataset_Dataset struct {
	// The resource name of the Dataset used to train this Model.
	Dataset string `protobuf:"bytes,3,opt,name=dataset,proto3,oneof"`
}

type ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource struct {
	// The Google Cloud Storage uri of the unmanaged Dataset used to train
	// this Model.
	GcsSource *GcsSource `protobuf:"bytes,4,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

type ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource struct {
	// The BigQuery table of the unmanaged Dataset used to train this
	// Model.
	BigquerySource *BigQuerySource `protobuf:"bytes,5,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}

func (*ModelMonitoringObjectiveConfig_TrainingDataset_Dataset) isModelMonitoringObjectiveConfig_TrainingDataset_DataSource() {
}

func (*ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource) isModelMonitoringObjectiveConfig_TrainingDataset_DataSource() {
}

func (*ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource) isModelMonitoringObjectiveConfig_TrainingDataset_DataSource() {
}

// The config for Training & Prediction data skew detection. It specifies the
// training dataset sources and the skew detection parameters.
type ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Key is the feature name and value is the threshold. If a feature needs to
	// be monitored for skew, a value threshold must be configured for that
	// feature. The threshold here is against feature distribution distance
	// between the training and prediction feature.
	SkewThresholds map[string]*ThresholdConfig `protobuf:"bytes,1,rep,name=skew_thresholds,json=skewThresholds,proto3" json:"skew_thresholds,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
	// Key is the feature name and value is the threshold. The threshold here is
	// against attribution score distance between the training and prediction
	// feature.
	AttributionScoreSkewThresholds map[string]*ThresholdConfig `protobuf:"bytes,2,rep,name=attribution_score_skew_thresholds,json=attributionScoreSkewThresholds,proto3" json:"attribution_score_skew_thresholds,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
	// Skew anomaly detection threshold used by all features.
	// When the per-feature thresholds are not set, this field can be used to
	// specify a threshold for all features.
	DefaultSkewThreshold *ThresholdConfig `protobuf:"bytes,6,opt,name=default_skew_threshold,json=defaultSkewThreshold,proto3" json:"default_skew_threshold,omitempty"`
}

func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) Reset() {
	*x = ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[6]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) ProtoMessage() {}

func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[6]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{1, 1}
}

func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) GetSkewThresholds() map[string]*ThresholdConfig {
	if x != nil {
		return x.SkewThresholds
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) GetAttributionScoreSkewThresholds() map[string]*ThresholdConfig {
	if x != nil {
		return x.AttributionScoreSkewThresholds
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig) GetDefaultSkewThreshold() *ThresholdConfig {
	if x != nil {
		return x.DefaultSkewThreshold
	}
	return nil
}

// The config for Prediction data drift detection.
type ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Key is the feature name and value is the threshold. If a feature needs to
	// be monitored for drift, a value threshold must be configured for that
	// feature. The threshold here is against feature distribution distance
	// between different time windws.
	DriftThresholds map[string]*ThresholdConfig `protobuf:"bytes,1,rep,name=drift_thresholds,json=driftThresholds,proto3" json:"drift_thresholds,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
	// Key is the feature name and value is the threshold. The threshold here is
	// against attribution score distance between different time windows.
	AttributionScoreDriftThresholds map[string]*ThresholdConfig `protobuf:"bytes,2,rep,name=attribution_score_drift_thresholds,json=attributionScoreDriftThresholds,proto3" json:"attribution_score_drift_thresholds,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
	// Drift anomaly detection threshold used by all features.
	// When the per-feature thresholds are not set, this field can be used to
	// specify a threshold for all features.
	DefaultDriftThreshold *ThresholdConfig `protobuf:"bytes,5,opt,name=default_drift_threshold,json=defaultDriftThreshold,proto3" json:"default_drift_threshold,omitempty"`
}

func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) Reset() {
	*x = ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[7]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) ProtoMessage() {}

func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[7]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{1, 2}
}

func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) GetDriftThresholds() map[string]*ThresholdConfig {
	if x != nil {
		return x.DriftThresholds
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) GetAttributionScoreDriftThresholds() map[string]*ThresholdConfig {
	if x != nil {
		return x.AttributionScoreDriftThresholds
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig) GetDefaultDriftThreshold() *ThresholdConfig {
	if x != nil {
		return x.DefaultDriftThreshold
	}
	return nil
}

// The config for integrating with Vertex Explainable AI. Only applicable if
// the Model has explanation_spec populated.
type ModelMonitoringObjectiveConfig_ExplanationConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// If want to analyze the Vertex Explainable AI feature attribute scores or
	// not. If set to true, Vertex AI will log the feature attributions from
	// explain response and do the skew/drift detection for them.
	EnableFeatureAttributes bool `protobuf:"varint,1,opt,name=enable_feature_attributes,json=enableFeatureAttributes,proto3" json:"enable_feature_attributes,omitempty"`
	// Predictions generated by the BatchPredictionJob using baseline dataset.
	ExplanationBaseline *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline `protobuf:"bytes,2,opt,name=explanation_baseline,json=explanationBaseline,proto3" json:"explanation_baseline,omitempty"`
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) Reset() {
	*x = ModelMonitoringObjectiveConfig_ExplanationConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[8]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringObjectiveConfig_ExplanationConfig) ProtoMessage() {}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[8]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringObjectiveConfig_ExplanationConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_ExplanationConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{1, 3}
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) GetEnableFeatureAttributes() bool {
	if x != nil {
		return x.EnableFeatureAttributes
	}
	return false
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig) GetExplanationBaseline() *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline {
	if x != nil {
		return x.ExplanationBaseline
	}
	return nil
}

// Output from
// [BatchPredictionJob][google.cloud.aiplatform.v1beta1.BatchPredictionJob]
// for Model Monitoring baseline dataset, which can be used to generate
// baseline attribution scores.
type ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The configuration specifying of BatchExplain job output. This can be
	// used to generate the baseline of feature attribution scores.
	//
	// Types that are assignable to Destination:
	//
	//	*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs
	//	*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery
	Destination isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination `protobuf_oneof:"destination"`
	// The storage format of the predictions generated BatchPrediction job.
	PredictionFormat ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat `protobuf:"varint,1,opt,name=prediction_format,json=predictionFormat,proto3,enum=google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat" json:"prediction_format,omitempty"`
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) Reset() {
	*x = ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[13]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) ProtoMessage() {}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[13]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline.ProtoReflect.Descriptor instead.
func (*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{1, 3, 0}
}

func (m *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) GetDestination() isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination {
	if m != nil {
		return m.Destination
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) GetGcs() *GcsDestination {
	if x, ok := x.GetDestination().(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs); ok {
		return x.Gcs
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) GetBigquery() *BigQueryDestination {
	if x, ok := x.GetDestination().(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery); ok {
		return x.Bigquery
	}
	return nil
}

func (x *ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline) GetPredictionFormat() ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat {
	if x != nil {
		return x.PredictionFormat
	}
	return ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PREDICTION_FORMAT_UNSPECIFIED
}

type isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination interface {
	isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination()
}

type ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs struct {
	// Cloud Storage location for BatchExplain output.
	Gcs *GcsDestination `protobuf:"bytes,2,opt,name=gcs,proto3,oneof"`
}

type ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery struct {
	// BigQuery location for BatchExplain output.
	Bigquery *BigQueryDestination `protobuf:"bytes,3,opt,name=bigquery,proto3,oneof"`
}

func (*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs) isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination() {
}

func (*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery) isModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Destination() {
}

// The config for email alert.
type ModelMonitoringAlertConfig_EmailAlertConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The email addresses to send the alert.
	UserEmails []string `protobuf:"bytes,1,rep,name=user_emails,json=userEmails,proto3" json:"user_emails,omitempty"`
}

func (x *ModelMonitoringAlertConfig_EmailAlertConfig) Reset() {
	*x = ModelMonitoringAlertConfig_EmailAlertConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[14]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelMonitoringAlertConfig_EmailAlertConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelMonitoringAlertConfig_EmailAlertConfig) ProtoMessage() {}

func (x *ModelMonitoringAlertConfig_EmailAlertConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[14]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelMonitoringAlertConfig_EmailAlertConfig.ProtoReflect.Descriptor instead.
func (*ModelMonitoringAlertConfig_EmailAlertConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{2, 0}
}

func (x *ModelMonitoringAlertConfig_EmailAlertConfig) GetUserEmails() []string {
	if x != nil {
		return x.UserEmails
	}
	return nil
}

// Requests are randomly selected.
type SamplingStrategy_RandomSampleConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Sample rate (0, 1]
	SampleRate float64 `protobuf:"fixed64,1,opt,name=sample_rate,json=sampleRate,proto3" json:"sample_rate,omitempty"`
}

func (x *SamplingStrategy_RandomSampleConfig) Reset() {
	*x = SamplingStrategy_RandomSampleConfig{}
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[15]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *SamplingStrategy_RandomSampleConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*SamplingStrategy_RandomSampleConfig) ProtoMessage() {}

func (x *SamplingStrategy_RandomSampleConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[15]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use SamplingStrategy_RandomSampleConfig.ProtoReflect.Descriptor instead.
func (*SamplingStrategy_RandomSampleConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP(), []int{4, 0}
}

func (x *SamplingStrategy_RandomSampleConfig) GetSampleRate() float64 {
	if x != nil {
		return x.SampleRate
	}
	return 0
}

var File_google_cloud_aiplatform_v1beta1_model_monitoring_proto protoreflect.FileDescriptor

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}

var (
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescOnce sync.Once
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescData = file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDesc
)

func file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescGZIP() []byte {
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescOnce.Do(func() {
		file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescData)
	})
	return file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDescData
}

var file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_enumTypes = make([]protoimpl.EnumInfo, 1)
var file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes = make([]protoimpl.MessageInfo, 16)
var file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_goTypes = []any{
	(ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_PredictionFormat)(0), // 0: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.PredictionFormat
	(*ModelMonitoringConfig)(nil),                                                // 1: google.cloud.aiplatform.v1beta1.ModelMonitoringConfig
	(*ModelMonitoringObjectiveConfig)(nil),                                       // 2: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig
	(*ModelMonitoringAlertConfig)(nil),                                           // 3: google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig
	(*ThresholdConfig)(nil),                                                      // 4: google.cloud.aiplatform.v1beta1.ThresholdConfig
	(*SamplingStrategy)(nil),                                                     // 5: google.cloud.aiplatform.v1beta1.SamplingStrategy
	(*ModelMonitoringObjectiveConfig_TrainingDataset)(nil),                       // 6: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingDataset
	(*ModelMonitoringObjectiveConfig_TrainingPredictionSkewDetectionConfig)(nil), // 7: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
	(*ModelMonitoringObjectiveConfig_PredictionDriftDetectionConfig)(nil),        // 8: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig
	(*ModelMonitoringObjectiveConfig_ExplanationConfig)(nil),                     // 9: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig
	nil, // 10: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.SkewThresholdsEntry
	nil, // 11: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.AttributionScoreSkewThresholdsEntry
	nil, // 12: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.DriftThresholdsEntry
	nil, // 13: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.AttributionScoreDriftThresholdsEntry
	(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline)(nil), // 14: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline
	(*ModelMonitoringAlertConfig_EmailAlertConfig)(nil),                          // 15: google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig.EmailAlertConfig
	(*SamplingStrategy_RandomSampleConfig)(nil),                                  // 16: google.cloud.aiplatform.v1beta1.SamplingStrategy.RandomSampleConfig
	(*GcsDestination)(nil),      // 17: google.cloud.aiplatform.v1beta1.GcsDestination
	(*GcsSource)(nil),           // 18: google.cloud.aiplatform.v1beta1.GcsSource
	(*BigQuerySource)(nil),      // 19: google.cloud.aiplatform.v1beta1.BigQuerySource
	(*BigQueryDestination)(nil), // 20: google.cloud.aiplatform.v1beta1.BigQueryDestination
}
var file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_depIdxs = []int32{
	2,  // 0: google.cloud.aiplatform.v1beta1.ModelMonitoringConfig.objective_configs:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig
	3,  // 1: google.cloud.aiplatform.v1beta1.ModelMonitoringConfig.alert_config:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig
	17, // 2: google.cloud.aiplatform.v1beta1.ModelMonitoringConfig.stats_anomalies_base_directory:type_name -> google.cloud.aiplatform.v1beta1.GcsDestination
	6,  // 3: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.training_dataset:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingDataset
	7,  // 4: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.training_prediction_skew_detection_config:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
	8,  // 5: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.prediction_drift_detection_config:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig
	9,  // 6: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.explanation_config:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig
	15, // 7: google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig.email_alert_config:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringAlertConfig.EmailAlertConfig
	16, // 8: google.cloud.aiplatform.v1beta1.SamplingStrategy.random_sample_config:type_name -> google.cloud.aiplatform.v1beta1.SamplingStrategy.RandomSampleConfig
	18, // 9: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingDataset.gcs_source:type_name -> google.cloud.aiplatform.v1beta1.GcsSource
	19, // 10: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingDataset.bigquery_source:type_name -> google.cloud.aiplatform.v1beta1.BigQuerySource
	5,  // 11: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingDataset.logging_sampling_strategy:type_name -> google.cloud.aiplatform.v1beta1.SamplingStrategy
	10, // 12: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.skew_thresholds:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.SkewThresholdsEntry
	11, // 13: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.attribution_score_skew_thresholds:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.AttributionScoreSkewThresholdsEntry
	4,  // 14: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.default_skew_threshold:type_name -> google.cloud.aiplatform.v1beta1.ThresholdConfig
	12, // 15: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.drift_thresholds:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.DriftThresholdsEntry
	13, // 16: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.attribution_score_drift_thresholds:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.AttributionScoreDriftThresholdsEntry
	4,  // 17: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.default_drift_threshold:type_name -> google.cloud.aiplatform.v1beta1.ThresholdConfig
	14, // 18: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.explanation_baseline:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline
	4,  // 19: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.SkewThresholdsEntry.value:type_name -> google.cloud.aiplatform.v1beta1.ThresholdConfig
	4,  // 20: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.AttributionScoreSkewThresholdsEntry.value:type_name -> google.cloud.aiplatform.v1beta1.ThresholdConfig
	4,  // 21: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.DriftThresholdsEntry.value:type_name -> google.cloud.aiplatform.v1beta1.ThresholdConfig
	4,  // 22: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig.AttributionScoreDriftThresholdsEntry.value:type_name -> google.cloud.aiplatform.v1beta1.ThresholdConfig
	17, // 23: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.gcs:type_name -> google.cloud.aiplatform.v1beta1.GcsDestination
	20, // 24: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.bigquery:type_name -> google.cloud.aiplatform.v1beta1.BigQueryDestination
	0,  // 25: google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.prediction_format:type_name -> google.cloud.aiplatform.v1beta1.ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.PredictionFormat
	26, // [26:26] is the sub-list for method output_type
	26, // [26:26] is the sub-list for method input_type
	26, // [26:26] is the sub-list for extension type_name
	26, // [26:26] is the sub-list for extension extendee
	0,  // [0:26] is the sub-list for field type_name
}

func init() { file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_init() }
func file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_init() {
	if File_google_cloud_aiplatform_v1beta1_model_monitoring_proto != nil {
		return
	}
	file_google_cloud_aiplatform_v1beta1_io_proto_init()
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[2].OneofWrappers = []any{
		(*ModelMonitoringAlertConfig_EmailAlertConfig_)(nil),
	}
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[3].OneofWrappers = []any{
		(*ThresholdConfig_Value)(nil),
	}
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[5].OneofWrappers = []any{
		(*ModelMonitoringObjectiveConfig_TrainingDataset_Dataset)(nil),
		(*ModelMonitoringObjectiveConfig_TrainingDataset_GcsSource)(nil),
		(*ModelMonitoringObjectiveConfig_TrainingDataset_BigquerySource)(nil),
	}
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes[13].OneofWrappers = []any{
		(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Gcs)(nil),
		(*ModelMonitoringObjectiveConfig_ExplanationConfig_ExplanationBaseline_Bigquery)(nil),
	}
	type x struct{}
	out := protoimpl.TypeBuilder{
		File: protoimpl.DescBuilder{
			GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
			RawDescriptor: file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDesc,
			NumEnums:      1,
			NumMessages:   16,
			NumExtensions: 0,
			NumServices:   0,
		},
		GoTypes:           file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_goTypes,
		DependencyIndexes: file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_depIdxs,
		EnumInfos:         file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_enumTypes,
		MessageInfos:      file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_msgTypes,
	}.Build()
	File_google_cloud_aiplatform_v1beta1_model_monitoring_proto = out.File
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_rawDesc = nil
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_goTypes = nil
	file_google_cloud_aiplatform_v1beta1_model_monitoring_proto_depIdxs = nil
}