# Generated by the protocol buffer compiler.  DO NOT EDIT!
# source: google/cloud/aiplatform/v1/model_deployment_monitoring_job.proto

require 'google/protobuf'

require 'google/api/field_behavior_pb'
require 'google/api/resource_pb'
require 'google/cloud/aiplatform/v1/encryption_spec_pb'
require 'google/cloud/aiplatform/v1/feature_monitoring_stats_pb'
require 'google/cloud/aiplatform/v1/io_pb'
require 'google/cloud/aiplatform/v1/job_state_pb'
require 'google/cloud/aiplatform/v1/model_monitoring_pb'
require 'google/protobuf/duration_pb'
require 'google/protobuf/struct_pb'
require 'google/protobuf/timestamp_pb'
require 'google/rpc/status_pb'

Google::Protobuf::DescriptorPool.generated_pool.build do
  add_file("google/cloud/aiplatform/v1/model_deployment_monitoring_job.proto", :syntax => :proto3) do
    add_message "google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob" do
      optional :name, :string, 1
      optional :display_name, :string, 2
      optional :endpoint, :string, 3
      optional :state, :enum, 4, "google.cloud.aiplatform.v1.JobState"
      optional :schedule_state, :enum, 5, "google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState"
      optional :latest_monitoring_pipeline_metadata, :message, 25, "google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata"
      repeated :model_deployment_monitoring_objective_configs, :message, 6, "google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig"
      optional :model_deployment_monitoring_schedule_config, :message, 7, "google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig"
      optional :logging_sampling_strategy, :message, 8, "google.cloud.aiplatform.v1.SamplingStrategy"
      optional :model_monitoring_alert_config, :message, 15, "google.cloud.aiplatform.v1.ModelMonitoringAlertConfig"
      optional :predict_instance_schema_uri, :string, 9
      optional :sample_predict_instance, :message, 19, "google.protobuf.Value"
      optional :analysis_instance_schema_uri, :string, 16
      repeated :bigquery_tables, :message, 10, "google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable"
      optional :log_ttl, :message, 17, "google.protobuf.Duration"
      map :labels, :string, :string, 11
      optional :create_time, :message, 12, "google.protobuf.Timestamp"
      optional :update_time, :message, 13, "google.protobuf.Timestamp"
      optional :next_schedule_time, :message, 14, "google.protobuf.Timestamp"
      optional :stats_anomalies_base_directory, :message, 20, "google.cloud.aiplatform.v1.GcsDestination"
      optional :encryption_spec, :message, 21, "google.cloud.aiplatform.v1.EncryptionSpec"
      optional :enable_monitoring_pipeline_logs, :bool, 22
      optional :error, :message, 23, "google.rpc.Status"
    end
    add_message "google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata" do
      optional :run_time, :message, 1, "google.protobuf.Timestamp"
      optional :status, :message, 2, "google.rpc.Status"
    end
    add_enum "google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState" do
      value :MONITORING_SCHEDULE_STATE_UNSPECIFIED, 0
      value :PENDING, 1
      value :OFFLINE, 2
      value :RUNNING, 3
    end
    add_message "google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable" do
      optional :log_source, :enum, 1, "google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogSource"
      optional :log_type, :enum, 2, "google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogType"
      optional :bigquery_table_path, :string, 3
    end
    add_enum "google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogSource" do
      value :LOG_SOURCE_UNSPECIFIED, 0
      value :TRAINING, 1
      value :SERVING, 2
    end
    add_enum "google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogType" do
      value :LOG_TYPE_UNSPECIFIED, 0
      value :PREDICT, 1
      value :EXPLAIN, 2
    end
    add_message "google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig" do
      optional :deployed_model_id, :string, 1
      optional :objective_config, :message, 2, "google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig"
    end
    add_message "google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig" do
      optional :monitor_interval, :message, 1, "google.protobuf.Duration"
      optional :monitor_window, :message, 2, "google.protobuf.Duration"
    end
    add_message "google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies" do
      optional :objective, :enum, 1, "google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType"
      optional :deployed_model_id, :string, 2
      optional :anomaly_count, :int32, 3
      repeated :feature_stats, :message, 4, "google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies"
    end
    add_message "google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies" do
      optional :feature_display_name, :string, 1
      optional :threshold, :message, 3, "google.cloud.aiplatform.v1.ThresholdConfig"
      optional :training_stats, :message, 4, "google.cloud.aiplatform.v1.FeatureStatsAnomaly"
      repeated :prediction_stats, :message, 5, "google.cloud.aiplatform.v1.FeatureStatsAnomaly"
    end
    add_enum "google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType" do
      value :MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED, 0
      value :RAW_FEATURE_SKEW, 1
      value :RAW_FEATURE_DRIFT, 2
      value :FEATURE_ATTRIBUTION_SKEW, 3
      value :FEATURE_ATTRIBUTION_DRIFT, 4
    end
  end
end

module Google
  module Cloud
    module AIPlatform
      module V1
        ModelDeploymentMonitoringJob = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob").msgclass
        ModelDeploymentMonitoringJob::LatestMonitoringPipelineMetadata = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata").msgclass
        ModelDeploymentMonitoringJob::MonitoringScheduleState = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState").enummodule
        ModelDeploymentMonitoringBigQueryTable = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable").msgclass
        ModelDeploymentMonitoringBigQueryTable::LogSource = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogSource").enummodule
        ModelDeploymentMonitoringBigQueryTable::LogType = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable.LogType").enummodule
        ModelDeploymentMonitoringObjectiveConfig = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig").msgclass
        ModelDeploymentMonitoringScheduleConfig = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig").msgclass
        ModelMonitoringStatsAnomalies = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies").msgclass
        ModelMonitoringStatsAnomalies::FeatureHistoricStatsAnomalies = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies").msgclass
        ModelDeploymentMonitoringObjectiveType = ::Google::Protobuf::DescriptorPool.generated_pool.lookup("google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType").enummodule
      end
    end
  end
end