// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/automl/v1/image.proto

package automl

import (
	fmt "fmt"
	math "math"

	proto "github.com/golang/protobuf/proto"
	_ "github.com/golang/protobuf/ptypes/timestamp"
	_ "google.golang.org/genproto/googleapis/api/annotations"
)

// Reference imports to suppress errors if they are not otherwise used.
var _ = proto.Marshal
var _ = fmt.Errorf
var _ = math.Inf

// This is a compile-time assertion to ensure that this generated file
// is compatible with the proto package it is being compiled against.
// A compilation error at this line likely means your copy of the
// proto package needs to be updated.
const _ = proto.ProtoPackageIsVersion3 // please upgrade the proto package

// Dataset metadata that is specific to image classification.
type ImageClassificationDatasetMetadata struct {
	// Required. Type of the classification problem.
	ClassificationType   ClassificationType `protobuf:"varint,1,opt,name=classification_type,json=classificationType,proto3,enum=google.cloud.automl.v1.ClassificationType" json:"classification_type,omitempty"`
	XXX_NoUnkeyedLiteral struct{}           `json:"-"`
	XXX_unrecognized     []byte             `json:"-"`
	XXX_sizecache        int32              `json:"-"`
}

func (m *ImageClassificationDatasetMetadata) Reset()         { *m = ImageClassificationDatasetMetadata{} }
func (m *ImageClassificationDatasetMetadata) String() string { return proto.CompactTextString(m) }
func (*ImageClassificationDatasetMetadata) ProtoMessage()    {}
func (*ImageClassificationDatasetMetadata) Descriptor() ([]byte, []int) {
	return fileDescriptor_651f5a2c51022614, []int{0}
}

func (m *ImageClassificationDatasetMetadata) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_ImageClassificationDatasetMetadata.Unmarshal(m, b)
}
func (m *ImageClassificationDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_ImageClassificationDatasetMetadata.Marshal(b, m, deterministic)
}
func (m *ImageClassificationDatasetMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_ImageClassificationDatasetMetadata.Merge(m, src)
}
func (m *ImageClassificationDatasetMetadata) XXX_Size() int {
	return xxx_messageInfo_ImageClassificationDatasetMetadata.Size(m)
}
func (m *ImageClassificationDatasetMetadata) XXX_DiscardUnknown() {
	xxx_messageInfo_ImageClassificationDatasetMetadata.DiscardUnknown(m)
}

var xxx_messageInfo_ImageClassificationDatasetMetadata proto.InternalMessageInfo

func (m *ImageClassificationDatasetMetadata) GetClassificationType() ClassificationType {
	if m != nil {
		return m.ClassificationType
	}
	return ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED
}

// Dataset metadata specific to image object detection.
type ImageObjectDetectionDatasetMetadata struct {
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *ImageObjectDetectionDatasetMetadata) Reset()         { *m = ImageObjectDetectionDatasetMetadata{} }
func (m *ImageObjectDetectionDatasetMetadata) String() string { return proto.CompactTextString(m) }
func (*ImageObjectDetectionDatasetMetadata) ProtoMessage()    {}
func (*ImageObjectDetectionDatasetMetadata) Descriptor() ([]byte, []int) {
	return fileDescriptor_651f5a2c51022614, []int{1}
}

func (m *ImageObjectDetectionDatasetMetadata) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_ImageObjectDetectionDatasetMetadata.Unmarshal(m, b)
}
func (m *ImageObjectDetectionDatasetMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_ImageObjectDetectionDatasetMetadata.Marshal(b, m, deterministic)
}
func (m *ImageObjectDetectionDatasetMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_ImageObjectDetectionDatasetMetadata.Merge(m, src)
}
func (m *ImageObjectDetectionDatasetMetadata) XXX_Size() int {
	return xxx_messageInfo_ImageObjectDetectionDatasetMetadata.Size(m)
}
func (m *ImageObjectDetectionDatasetMetadata) XXX_DiscardUnknown() {
	xxx_messageInfo_ImageObjectDetectionDatasetMetadata.DiscardUnknown(m)
}

var xxx_messageInfo_ImageObjectDetectionDatasetMetadata proto.InternalMessageInfo

// Model metadata for image classification.
type ImageClassificationModelMetadata struct {
	// Optional. The ID of the `base` model. If it is specified, the new model
	// will be created based on the `base` model. Otherwise, the new model will be
	// created from scratch. The `base` model must be in the same
	// `project` and `location` as the new model to create, and have the same
	// `model_type`.
	BaseModelId string `protobuf:"bytes,1,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`
	// The train budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour. The actual
	// `train_cost` will be equal or less than this value. If further model
	// training ceases to provide any improvements, it will stop without using
	// full budget and the stop_reason will be `MODEL_CONVERGED`.
	// Note, node_hour  = actual_hour * number_of_nodes_invovled.
	// For model type `cloud`(default), the train budget must be between 8,000
	// and 800,000 milli node hours, inclusive. The default value is 192, 000
	// which represents one day in wall time. For model type
	// `mobile-low-latency-1`, `mobile-versatile-1`, `mobile-high-accuracy-1`,
	// `mobile-core-ml-low-latency-1`, `mobile-core-ml-versatile-1`,
	// `mobile-core-ml-high-accuracy-1`, the train budget must be between 1,000
	// and 100,000 milli node hours, inclusive. The default value is 24, 000 which
	// represents one day in wall time.
	TrainBudgetMilliNodeHours int64 `protobuf:"varint,16,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"`
	// Output only. The actual train cost of creating this model, expressed in
	// milli node hours, i.e. 1,000 value in this field means 1 node hour.
	// Guaranteed to not exceed the train budget.
	TrainCostMilliNodeHours int64 `protobuf:"varint,17,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,omitempty"`
	// Output only. The reason that this create model operation stopped,
	// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
	StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`
	// Optional. Type of the model. The available values are:
	// *   `cloud` - Model to be used via prediction calls to AutoML API.
	//               This is the default value.
	// *   `mobile-low-latency-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel])
	//               and used on a mobile or edge device with TensorFlow
	//               afterwards. Expected to have low latency, but may have lower
	//               prediction quality than other models.
	// *   `mobile-versatile-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel])
	//               and used on a mobile or edge device with TensorFlow
	//               afterwards.
	// *   `mobile-high-accuracy-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel])
	//               and used on a mobile or edge device with TensorFlow
	//               afterwards.  Expected to have a higher latency, but should
	//               also have a higher prediction quality than other models.
	// *   `mobile-core-ml-low-latency-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel])
	//               and used on a mobile device with Core ML afterwards. Expected
	//               to have low latency, but may have lower prediction quality
	//               than other models.
	// *   `mobile-core-ml-versatile-1` - A model that, in addition to providing
	//               prediction via AutoML API, can also be exported (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel])
	//               and used on a mobile device with Core ML afterwards.
	// *   `mobile-core-ml-high-accuracy-1` - A model that, in addition to
	//               providing prediction via AutoML API, can also be exported
	//               (see
	//               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel])
	//               and used on a mobile device with Core ML afterwards. Expected
	//               to have a higher latency, but should also have a higher
	//               prediction quality than other models.
	ModelType string `protobuf:"bytes,7,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`
	// Output only. An approximate number of online prediction QPS that can
	// be supported by this model per each node on which it is deployed.
	NodeQps float64 `protobuf:"fixed64,13,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`
	// Output only. The number of nodes this model is deployed on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the node_qps field.
	NodeCount            int64    `protobuf:"varint,14,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *ImageClassificationModelMetadata) Reset()         { *m = ImageClassificationModelMetadata{} }
func (m *ImageClassificationModelMetadata) String() string { return proto.CompactTextString(m) }
func (*ImageClassificationModelMetadata) ProtoMessage()    {}
func (*ImageClassificationModelMetadata) Descriptor() ([]byte, []int) {
	return fileDescriptor_651f5a2c51022614, []int{2}
}

func (m *ImageClassificationModelMetadata) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_ImageClassificationModelMetadata.Unmarshal(m, b)
}
func (m *ImageClassificationModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_ImageClassificationModelMetadata.Marshal(b, m, deterministic)
}
func (m *ImageClassificationModelMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_ImageClassificationModelMetadata.Merge(m, src)
}
func (m *ImageClassificationModelMetadata) XXX_Size() int {
	return xxx_messageInfo_ImageClassificationModelMetadata.Size(m)
}
func (m *ImageClassificationModelMetadata) XXX_DiscardUnknown() {
	xxx_messageInfo_ImageClassificationModelMetadata.DiscardUnknown(m)
}

var xxx_messageInfo_ImageClassificationModelMetadata proto.InternalMessageInfo

func (m *ImageClassificationModelMetadata) GetBaseModelId() string {
	if m != nil {
		return m.BaseModelId
	}
	return ""
}

func (m *ImageClassificationModelMetadata) GetTrainBudgetMilliNodeHours() int64 {
	if m != nil {
		return m.TrainBudgetMilliNodeHours
	}
	return 0
}

func (m *ImageClassificationModelMetadata) GetTrainCostMilliNodeHours() int64 {
	if m != nil {
		return m.TrainCostMilliNodeHours
	}
	return 0
}

func (m *ImageClassificationModelMetadata) GetStopReason() string {
	if m != nil {
		return m.StopReason
	}
	return ""
}

func (m *ImageClassificationModelMetadata) GetModelType() string {
	if m != nil {
		return m.ModelType
	}
	return ""
}

func (m *ImageClassificationModelMetadata) GetNodeQps() float64 {
	if m != nil {
		return m.NodeQps
	}
	return 0
}

func (m *ImageClassificationModelMetadata) GetNodeCount() int64 {
	if m != nil {
		return m.NodeCount
	}
	return 0
}

// Model metadata specific to image object detection.
type ImageObjectDetectionModelMetadata struct {
	// Optional. Type of the model. The available values are:
	// *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
	//               calls to AutoML API. Expected to have a higher latency, but
	//               should also have a higher prediction quality than other
	//               models.
	// *   `cloud-low-latency-1` -  A model to be used via prediction
	//               calls to AutoML API. Expected to have low latency, but may
	//               have lower prediction quality than other models.
	ModelType string `protobuf:"bytes,1,opt,name=model_type,json=modelType,proto3" json:"model_type,omitempty"`
	// Output only. The number of nodes this model is deployed on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the qps_per_node field.
	NodeCount int64 `protobuf:"varint,3,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	// Output only. An approximate number of online prediction QPS that can
	// be supported by this model per each node on which it is deployed.
	NodeQps float64 `protobuf:"fixed64,4,opt,name=node_qps,json=nodeQps,proto3" json:"node_qps,omitempty"`
	// Output only. The reason that this create model operation stopped,
	// e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
	StopReason string `protobuf:"bytes,5,opt,name=stop_reason,json=stopReason,proto3" json:"stop_reason,omitempty"`
	// The train budget of creating this model, expressed in milli node
	// hours i.e. 1,000 value in this field means 1 node hour. The actual
	// `train_cost` will be equal or less than this value. If further model
	// training ceases to provide any improvements, it will stop without using
	// full budget and the stop_reason will be `MODEL_CONVERGED`.
	// Note, node_hour  = actual_hour * number_of_nodes_invovled.
	// For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
	// the train budget must be between 20,000 and 900,000 milli node hours,
	// inclusive. The default value is 216, 000 which represents one day in
	// wall time.
	// For model type `mobile-low-latency-1`, `mobile-versatile-1`,
	// `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
	// `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
	// budget must be between 1,000 and 100,000 milli node hours, inclusive.
	// The default value is 24, 000 which represents one day in wall time.
	TrainBudgetMilliNodeHours int64 `protobuf:"varint,6,opt,name=train_budget_milli_node_hours,json=trainBudgetMilliNodeHours,proto3" json:"train_budget_milli_node_hours,omitempty"`
	// Output only. The actual train cost of creating this model, expressed in
	// milli node hours, i.e. 1,000 value in this field means 1 node hour.
	// Guaranteed to not exceed the train budget.
	TrainCostMilliNodeHours int64    `protobuf:"varint,7,opt,name=train_cost_milli_node_hours,json=trainCostMilliNodeHours,proto3" json:"train_cost_milli_node_hours,omitempty"`
	XXX_NoUnkeyedLiteral    struct{} `json:"-"`
	XXX_unrecognized        []byte   `json:"-"`
	XXX_sizecache           int32    `json:"-"`
}

func (m *ImageObjectDetectionModelMetadata) Reset()         { *m = ImageObjectDetectionModelMetadata{} }
func (m *ImageObjectDetectionModelMetadata) String() string { return proto.CompactTextString(m) }
func (*ImageObjectDetectionModelMetadata) ProtoMessage()    {}
func (*ImageObjectDetectionModelMetadata) Descriptor() ([]byte, []int) {
	return fileDescriptor_651f5a2c51022614, []int{3}
}

func (m *ImageObjectDetectionModelMetadata) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_ImageObjectDetectionModelMetadata.Unmarshal(m, b)
}
func (m *ImageObjectDetectionModelMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_ImageObjectDetectionModelMetadata.Marshal(b, m, deterministic)
}
func (m *ImageObjectDetectionModelMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_ImageObjectDetectionModelMetadata.Merge(m, src)
}
func (m *ImageObjectDetectionModelMetadata) XXX_Size() int {
	return xxx_messageInfo_ImageObjectDetectionModelMetadata.Size(m)
}
func (m *ImageObjectDetectionModelMetadata) XXX_DiscardUnknown() {
	xxx_messageInfo_ImageObjectDetectionModelMetadata.DiscardUnknown(m)
}

var xxx_messageInfo_ImageObjectDetectionModelMetadata proto.InternalMessageInfo

func (m *ImageObjectDetectionModelMetadata) GetModelType() string {
	if m != nil {
		return m.ModelType
	}
	return ""
}

func (m *ImageObjectDetectionModelMetadata) GetNodeCount() int64 {
	if m != nil {
		return m.NodeCount
	}
	return 0
}

func (m *ImageObjectDetectionModelMetadata) GetNodeQps() float64 {
	if m != nil {
		return m.NodeQps
	}
	return 0
}

func (m *ImageObjectDetectionModelMetadata) GetStopReason() string {
	if m != nil {
		return m.StopReason
	}
	return ""
}

func (m *ImageObjectDetectionModelMetadata) GetTrainBudgetMilliNodeHours() int64 {
	if m != nil {
		return m.TrainBudgetMilliNodeHours
	}
	return 0
}

func (m *ImageObjectDetectionModelMetadata) GetTrainCostMilliNodeHours() int64 {
	if m != nil {
		return m.TrainCostMilliNodeHours
	}
	return 0
}

// Model deployment metadata specific to Image Classification.
type ImageClassificationModelDeploymentMetadata struct {
	// Input only. The number of nodes to deploy the model on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the model's
	//
	// [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps].
	// Must be between 1 and 100, inclusive on both ends.
	NodeCount            int64    `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *ImageClassificationModelDeploymentMetadata) Reset() {
	*m = ImageClassificationModelDeploymentMetadata{}
}
func (m *ImageClassificationModelDeploymentMetadata) String() string {
	return proto.CompactTextString(m)
}
func (*ImageClassificationModelDeploymentMetadata) ProtoMessage() {}
func (*ImageClassificationModelDeploymentMetadata) Descriptor() ([]byte, []int) {
	return fileDescriptor_651f5a2c51022614, []int{4}
}

func (m *ImageClassificationModelDeploymentMetadata) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_ImageClassificationModelDeploymentMetadata.Unmarshal(m, b)
}
func (m *ImageClassificationModelDeploymentMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_ImageClassificationModelDeploymentMetadata.Marshal(b, m, deterministic)
}
func (m *ImageClassificationModelDeploymentMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_ImageClassificationModelDeploymentMetadata.Merge(m, src)
}
func (m *ImageClassificationModelDeploymentMetadata) XXX_Size() int {
	return xxx_messageInfo_ImageClassificationModelDeploymentMetadata.Size(m)
}
func (m *ImageClassificationModelDeploymentMetadata) XXX_DiscardUnknown() {
	xxx_messageInfo_ImageClassificationModelDeploymentMetadata.DiscardUnknown(m)
}

var xxx_messageInfo_ImageClassificationModelDeploymentMetadata proto.InternalMessageInfo

func (m *ImageClassificationModelDeploymentMetadata) GetNodeCount() int64 {
	if m != nil {
		return m.NodeCount
	}
	return 0
}

// Model deployment metadata specific to Image Object Detection.
type ImageObjectDetectionModelDeploymentMetadata struct {
	// Input only. The number of nodes to deploy the model on. A node is an
	// abstraction of a machine resource, which can handle online prediction QPS
	// as given in the model's
	//
	// [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node].
	// Must be between 1 and 100, inclusive on both ends.
	NodeCount            int64    `protobuf:"varint,1,opt,name=node_count,json=nodeCount,proto3" json:"node_count,omitempty"`
	XXX_NoUnkeyedLiteral struct{} `json:"-"`
	XXX_unrecognized     []byte   `json:"-"`
	XXX_sizecache        int32    `json:"-"`
}

func (m *ImageObjectDetectionModelDeploymentMetadata) Reset() {
	*m = ImageObjectDetectionModelDeploymentMetadata{}
}
func (m *ImageObjectDetectionModelDeploymentMetadata) String() string {
	return proto.CompactTextString(m)
}
func (*ImageObjectDetectionModelDeploymentMetadata) ProtoMessage() {}
func (*ImageObjectDetectionModelDeploymentMetadata) Descriptor() ([]byte, []int) {
	return fileDescriptor_651f5a2c51022614, []int{5}
}

func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Unmarshal(b []byte) error {
	return xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.Unmarshal(m, b)
}
func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
	return xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.Marshal(b, m, deterministic)
}
func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Merge(src proto.Message) {
	xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.Merge(m, src)
}
func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_Size() int {
	return xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.Size(m)
}
func (m *ImageObjectDetectionModelDeploymentMetadata) XXX_DiscardUnknown() {
	xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata.DiscardUnknown(m)
}

var xxx_messageInfo_ImageObjectDetectionModelDeploymentMetadata proto.InternalMessageInfo

func (m *ImageObjectDetectionModelDeploymentMetadata) GetNodeCount() int64 {
	if m != nil {
		return m.NodeCount
	}
	return 0
}

func init() {
	proto.RegisterType((*ImageClassificationDatasetMetadata)(nil), "google.cloud.automl.v1.ImageClassificationDatasetMetadata")
	proto.RegisterType((*ImageObjectDetectionDatasetMetadata)(nil), "google.cloud.automl.v1.ImageObjectDetectionDatasetMetadata")
	proto.RegisterType((*ImageClassificationModelMetadata)(nil), "google.cloud.automl.v1.ImageClassificationModelMetadata")
	proto.RegisterType((*ImageObjectDetectionModelMetadata)(nil), "google.cloud.automl.v1.ImageObjectDetectionModelMetadata")
	proto.RegisterType((*ImageClassificationModelDeploymentMetadata)(nil), "google.cloud.automl.v1.ImageClassificationModelDeploymentMetadata")
	proto.RegisterType((*ImageObjectDetectionModelDeploymentMetadata)(nil), "google.cloud.automl.v1.ImageObjectDetectionModelDeploymentMetadata")
}

func init() { proto.RegisterFile("google/cloud/automl/v1/image.proto", fileDescriptor_651f5a2c51022614) }

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