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// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/automl/v1beta1/io.proto
package automl
import (
fmt "fmt"
math "math"
proto "github.com/golang/protobuf/proto"
_ "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
// Input configuration for ImportData Action.
//
// The format of input depends on dataset_metadata the Dataset into which
// the import is happening has. As input source the
// [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source]
// is expected, unless specified otherwise. Additionally any input .CSV file
// by itself must be 100MB or smaller, unless specified otherwise.
// If an "example" file (i.e. image, video etc.) with identical content
// (even if it had different GCS_FILE_PATH) is mentioned multiple times, then
// its label, bounding boxes etc. are appended. The same file should be always
// provided with the same ML_USE and GCS_FILE_PATH, if it is not then
// these values are nondeterministically selected from the given ones.
//
// The formats are represented in EBNF with commas being literal and with
// non-terminal symbols defined near the end of this comment. The formats are:
//
// * For Image Classification:
// CSV file(s) with each line in format:
// ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
// GCS_FILE_PATH leads to image of up to 30MB in size. Supported
// extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO
// For MULTICLASS classification type, at most one LABEL is allowed
// per image. If an image has not yet been labeled, then it should be
// mentioned just once with no LABEL.
// Some sample rows:
// TRAIN,gs://folder/image1.jpg,daisy
// TEST,gs://folder/image2.jpg,dandelion,tulip,rose
// UNASSIGNED,gs://folder/image3.jpg,daisy
// UNASSIGNED,gs://folder/image4.jpg
//
// * For Image Object Detection:
// CSV file(s) with each line in format:
// ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,)
// GCS_FILE_PATH leads to image of up to 30MB in size. Supported
// extensions: .JPEG, .GIF, .PNG.
// Each image is assumed to be exhaustively labeled. The minimum
// allowed BOUNDING_BOX edge length is 0.01, and no more than 500
// BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined
// per line). If an image has not yet been labeled, then it should be
// mentioned just once with no LABEL and the ",,,,,,," in place of the
// BOUNDING_BOX.
// Four sample rows:
// TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,,
// TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,,
// UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3
// TEST,gs://folder/im3.png,,,,,,,,,
//
// * For Video Classification:
// CSV file(s) with each line in format:
// ML_USE,GCS_FILE_PATH
// where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH
// should lead to another .csv file which describes examples that have
// given ML_USE, using the following row format:
// GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,)
// Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
// to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
// TIME_SEGMENT_START and TIME_SEGMENT_END must be within the
// length of the video, and end has to be after the start. Any segment
// of a video which has one or more labels on it, is considered a
// hard negative for all other labels. Any segment with no labels on
// it is considered to be unknown. If a whole video is unknown, then
// it shuold be mentioned just once with ",," in place of LABEL,
// TIME_SEGMENT_START,TIME_SEGMENT_END.
// Sample top level CSV file:
// TRAIN,gs://folder/train_videos.csv
// TEST,gs://folder/test_videos.csv
// UNASSIGNED,gs://folder/other_videos.csv
// Sample rows of a CSV file for a particular ML_USE:
// gs://folder/video1.avi,car,120,180.000021
// gs://folder/video1.avi,bike,150,180.000021
// gs://folder/vid2.avi,car,0,60.5
// gs://folder/vid3.avi,,,
//
// * For Video Object Tracking:
// CSV file(s) with each line in format:
// ML_USE,GCS_FILE_PATH
// where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH
// should lead to another .csv file which describes examples that have
// given ML_USE, using one of the following row format:
// GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX
// or
// GCS_FILE_PATH,,,,,,,,,,
// Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
// to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
// Providing INSTANCE_IDs can help to obtain a better model. When
// a specific labeled entity leaves the video frame, and shows up
// afterwards it is not required, albeit preferable, that the same
// INSTANCE_ID is given to it.
// TIMESTAMP must be within the length of the video, the
// BOUNDING_BOX is assumed to be drawn on the closest video's frame
// to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected
// to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per
// frame are allowed. If a whole video is unknown, then it should be
// mentioned just once with ",,,,,,,,,," in place of LABEL,
// [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX.
// Sample top level CSV file:
// TRAIN,gs://folder/train_videos.csv
// TEST,gs://folder/test_videos.csv
// UNASSIGNED,gs://folder/other_videos.csv
// Seven sample rows of a CSV file for a particular ML_USE:
// gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
// gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
// gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
// gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
// gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,,
// gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,,
// gs://folder/video2.avi,,,,,,,,,,,
// * For Text Extraction:
// CSV file(s) with each line in format:
// ML_USE,GCS_FILE_PATH
// GCS_FILE_PATH leads to a .JSONL (i.e. JSON Lines) file which either
// imports text in-line or as documents.
// The in-line .JSONL file contains, per line, a proto that wraps a
// TextSnippet proto (in json representation) followed by one or
// more AnnotationPayload protos (called annotations), which have
// display_name and text_extraction detail populated.
// The given text is expected to be annotated exhaustively, e.g. if
// you look for animals and text contains "dolphin" that is not
// labeled, then "dolphin" will be assumed to not be an animal. Any
// given text snippet content must have 30,000 characters or less,
// and also be UTF-8 NFC encoded (ASCII already is). The document .JSONL file contains, per line, a proto that wraps a
// Document proto with input_config set. Only PDF documents are
// supported now, and each document may be up to 2MB large.
// Currently annotations on documents cannot be specified at import.
// Any given .JSONL file must be 100MB or smaller.
// Three sample CSV rows:
// TRAIN,gs://folder/file1.jsonl
// VALIDATE,gs://folder/file2.jsonl
// TEST,gs://folder/file3.jsonl
// Sample in-line JSON Lines file for entity extraction (presented here
// with artificial line breaks, but the only actual line break is
// denoted by \n).:
// {
// "text_snippet": {
// "content": "dog car cat"
// } "annotations": [
// {
// "display_name": "animal",
// "text_extraction": {
// "text_segment": {"start_offset": 0, "end_offset": 3}
// }
// },
// {
// "display_name": "vehicle",
// "text_extraction": {
// "text_segment": {"start_offset": 4, "end_offset": 7}
// }
// },
// {
// "display_name": "animal",
// "text_extraction": {
// "text_segment": {"start_offset": 8, "end_offset": 11}
// }
// },
// ],
// }\n
// {
// "text_snippet": {
// "content": "This dog is good."
// },
// "annotations": [
// {
// "display_name": "animal",
// "text_extraction": {
// "text_segment": {"start_offset": 5, "end_offset": 8}
// }
// }
// ]
// }
// Sample document JSON Lines file (presented here with artificial line
// breaks, but the only actual line break is denoted by \n).:
// {
// "document": {
// "input_config": {
// "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
// }
// }
// }
// }\n
// {
// "document": {
// "input_config": {
// "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
// }
// }
// }
// }
//
// * For Text Classification:
// CSV file(s) with each line in format:
// ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...
// TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
// the column content is a valid gcs file path, i.e. prefixed by
// "gs://", it will be treated as a GCS_FILE_PATH, else if the content
// is enclosed within double quotes (""), it will
// be treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
// must lead to a .txt file with UTF-8 encoding, e.g.
// "gs://folder/content.txt", and the content in it will be extracted
// as a text snippet. In TEXT_SNIPPET case, the column content
// excluding quotes will be treated as to be imported text snippet. In
// both cases, the text snippet/file size must be within 128kB.
// Maximum 100 unique labels are allowed per CSV row.
// Four sample rows:
// TRAIN,"They have bad food and very rude",RudeService,BadFood
// TRAIN,gs://folder/content.txt,SlowService
// TEST,"Typically always bad service there.",RudeService
// VALIDATE,"Stomach ache to go.",BadFood
//
// * For Text Sentiment:
// CSV file(s) with each line in format:
// ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT
// TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
// the column content is a valid gcs file path, i.e. prefixed by
// "gs://", it will be treated as a GCS_FILE_PATH, otherwise it will
// be treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
// must lead to a .txt file with UTF-8 encoding, e.g.
// "gs://folder/content.txt", and the content in it will be extracted
// as a text snippet. In TEXT_SNIPPET case, the column content itself
// will be treated as to be imported text snippet. In both cases, the
// text snippet must be up to 500 characters long.
// Four sample rows:
// TRAIN,"@freewrytin God is way too good for Claritin",2
// TRAIN,"I need Claritin so bad",3
// TEST,"Thank god for Claritin.",4
// VALIDATE,gs://folder/content.txt,2
//
// * For Tables:
// Either
// [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or
//
// [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]
// can be used. All inputs will be concatenated into a single
//
// [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name]
// For gcs_source:
// CSV file(s), where the first row of the first file is the header,
// containing unique column names. If the first row of a subsequent
// file is the same as the header, then it is also treated as a
// header. All other rows contain values for the corresponding
// columns.
// Each .CSV file by itself must be 10GB or smaller, and their total
// size must be 100GB or smaller.
// First three sample rows of a CSV file:
// "Id","First Name","Last Name","Dob","Addresses"
//
// "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
//
// "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
// For bigquery_source:
// An URI of a BigQuery table. The user data size of the BigQuery
// table must be 100GB or smaller.
// An imported table must have between 2 and 1,000 columns, inclusive,
// and between 1000 and 100,000,000 rows, inclusive. There are at most 5
// import data running in parallel.
//
// Definitions:
// ML_USE = "TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED"
// Describes how the given example (file) should be used for model
// training. "UNASSIGNED" can be used when user has no preference.
// GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/image1.png".
// LABEL = A display name of an object on an image, video etc., e.g. "dog".
// Must be up to 32 characters long and can consist only of ASCII
// Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
// For each label an AnnotationSpec is created which display_name
// becomes the label; AnnotationSpecs are given back in predictions.
// INSTANCE_ID = A positive integer that identifies a specific instance of a
// labeled entity on an example. Used e.g. to track two cars on
// a video while being able to tell apart which one is which.
// BOUNDING_BOX = VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,
// A rectangle parallel to the frame of the example (image,
// video). If 4 vertices are given they are connected by edges
// in the order provided, if 2 are given they are recognized
// as diagonally opposite vertices of the rectangle.
// VERTEX = COORDINATE,COORDINATE
// First coordinate is horizontal (x), the second is vertical (y).
// COORDINATE = A float in 0 to 1 range, relative to total length of
// image or video in given dimension. For fractions the
// leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
// Point 0,0 is in top left.
// TIME_SEGMENT_START = TIME_OFFSET
// Expresses a beginning, inclusive, of a time segment
// within an example that has a time dimension
// (e.g. video).
// TIME_SEGMENT_END = TIME_OFFSET
// Expresses an end, exclusive, of a time segment within
// an example that has a time dimension (e.g. video).
// TIME_OFFSET = A number of seconds as measured from the start of an
// example (e.g. video). Fractions are allowed, up to a
// microsecond precision. "inf" is allowed, and it means the end
// of the example.
// TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
// double quotes ("").
// SENTIMENT = An integer between 0 and
// Dataset.text_sentiment_dataset_metadata.sentiment_max
// (inclusive). Describes the ordinal of the sentiment - higher
// value means a more positive sentiment. All the values are
// completely relative, i.e. neither 0 needs to mean a negative or
// neutral sentiment nor sentiment_max needs to mean a positive one
// - it is just required that 0 is the least positive sentiment
// in the data, and sentiment_max is the most positive one.
// The SENTIMENT shouldn't be confused with "score" or "magnitude"
// from the previous Natural Language Sentiment Analysis API.
// All SENTIMENT values between 0 and sentiment_max must be
// represented in the imported data. On prediction the same 0 to
// sentiment_max range will be used. The difference between
// neighboring sentiment values needs not to be uniform, e.g. 1 and
// 2 may be similar whereas the difference between 2 and 3 may be
// huge.
//
// Errors:
// If any of the provided CSV files can't be parsed or if more than certain
// percent of CSV rows cannot be processed then the operation fails and
// nothing is imported. Regardless of overall success or failure the per-row
// failures, up to a certain count cap, will be listed in
// Operation.metadata.partial_failures.
//
type InputConfig struct {
// The source of the input.
//
// Types that are valid to be assigned to Source:
// *InputConfig_GcsSource
// *InputConfig_BigquerySource
Source isInputConfig_Source `protobuf_oneof:"source"`
// Additional domain-specific parameters describing the semantic of the
// imported data, any string must be up to 25000
// characters long.
//
// * For Tables:
// `schema_inference_version` - (integer) Required. The version of the
// algorithm that should be used for the initial inference of the
// schema (columns' DataTypes) of the table the data is being imported
// into. Allowed values: "1".
Params map[string]string `protobuf:"bytes,2,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *InputConfig) Reset() { *m = InputConfig{} }
func (m *InputConfig) String() string { return proto.CompactTextString(m) }
func (*InputConfig) ProtoMessage() {}
func (*InputConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{0}
}
func (m *InputConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_InputConfig.Unmarshal(m, b)
}
func (m *InputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_InputConfig.Marshal(b, m, deterministic)
}
func (m *InputConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_InputConfig.Merge(m, src)
}
func (m *InputConfig) XXX_Size() int {
return xxx_messageInfo_InputConfig.Size(m)
}
func (m *InputConfig) XXX_DiscardUnknown() {
xxx_messageInfo_InputConfig.DiscardUnknown(m)
}
var xxx_messageInfo_InputConfig proto.InternalMessageInfo
type isInputConfig_Source interface {
isInputConfig_Source()
}
type InputConfig_GcsSource struct {
GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}
type InputConfig_BigquerySource struct {
BigquerySource *BigQuerySource `protobuf:"bytes,3,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}
func (*InputConfig_GcsSource) isInputConfig_Source() {}
func (*InputConfig_BigquerySource) isInputConfig_Source() {}
func (m *InputConfig) GetSource() isInputConfig_Source {
if m != nil {
return m.Source
}
return nil
}
func (m *InputConfig) GetGcsSource() *GcsSource {
if x, ok := m.GetSource().(*InputConfig_GcsSource); ok {
return x.GcsSource
}
return nil
}
func (m *InputConfig) GetBigquerySource() *BigQuerySource {
if x, ok := m.GetSource().(*InputConfig_BigquerySource); ok {
return x.BigquerySource
}
return nil
}
func (m *InputConfig) GetParams() map[string]string {
if m != nil {
return m.Params
}
return nil
}
// XXX_OneofWrappers is for the internal use of the proto package.
func (*InputConfig) XXX_OneofWrappers() []interface{} {
return []interface{}{
(*InputConfig_GcsSource)(nil),
(*InputConfig_BigquerySource)(nil),
}
}
// Input configuration for BatchPredict Action.
//
// The format of input depends on the ML problem of the model used for
// prediction. As input source the
// [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source]
// is expected, unless specified otherwise.
//
// The formats are represented in EBNF with commas being literal and with
// non-terminal symbols defined near the end of this comment. The formats
// are:
// * For Video Classification:
// CSV file(s) with each line in format:
// GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
// GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h
// duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
// TIME_SEGMENT_START and TIME_SEGMENT_END must be within the
// length of the video, and end has to be after the start.
// Three sample rows:
// gs://folder/video1.mp4,10,40
// gs://folder/video1.mp4,20,60
// gs://folder/vid2.mov,0,inf
//
// * For Video Object Tracking:
// CSV file(s) with each line in format:
// GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
// GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h
// duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
// TIME_SEGMENT_START and TIME_SEGMENT_END must be within the
// length of the video, and end has to be after the start.
// Three sample rows:
// gs://folder/video1.mp4,10,240
// gs://folder/video1.mp4,300,360
// gs://folder/vid2.mov,0,inf
// * For Text Extraction
// .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or
// as documents (for a single BatchPredict call only one of the these
// formats may be used).
// The in-line .JSONL file(s) contain per line a proto that
// wraps a temporary user-assigned TextSnippet ID (string up to 2000
// characters long) called "id", a TextSnippet proto (in
// json representation) and zero or more TextFeature protos. Any given
// text snippet content must have 30,000 characters or less, and also
// be UTF-8 NFC encoded (ASCII already is). The IDs provided should be
// unique.
// The document .JSONL file(s) contain, per line, a proto that wraps a
// Document proto with input_config set. Only PDF documents are
// supported now, and each document must be up to 2MB large.
// Any given .JSONL file must be 100MB or smaller, and no more than 20
// files may be given.
// Sample in-line JSON Lines file (presented here with artificial line
// breaks, but the only actual line break is denoted by \n):
// {
// "id": "my_first_id",
// "text_snippet": { "content": "dog car cat"},
// "text_features": [
// {
// "text_segment": {"start_offset": 4, "end_offset": 6},
// "structural_type": PARAGRAPH,
// "bounding_poly": {
// "normalized_vertices": [
// {"x": 0.1, "y": 0.1},
// {"x": 0.1, "y": 0.3},
// {"x": 0.3, "y": 0.3},
// {"x": 0.3, "y": 0.1},
// ]
// },
// }
// ],
// }\n
// {
// "id": "2",
// "text_snippet": {
// "content": "An elaborate content",
// "mime_type": "text/plain"
// }
// }
// Sample document JSON Lines file (presented here with artificial line
// breaks, but the only actual line break is denoted by \n).:
// {
// "document": {
// "input_config": {
// "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
// }
// }
// }
// }\n
// {
// "document": {
// "input_config": {
// "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
// }
// }
// }
// }
//
// * For Tables:
// Either
// [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or
//
// [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source].
// GCS case:
// CSV file(s), each by itself 10GB or smaller and total size must be
// 100GB or smaller, where first file must have a header containing
// column names. If the first row of a subsequent file is the same as
// the header, then it is also treated as a header. All other rows
// contain values for the corresponding columns. For all
// CLASSIFICATION and REGRESSION
//
// [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
// The column names must contain the model's
//
// [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
//
// [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
// (order doesn't matter). The columns corresponding to the model's
// input feature column specs must contain values compatible with
// the column spec's data types. Prediction on all the rows, i.e.
// the CSV lines, will be attempted. First three sample rows of a
// CSV file:
// "First Name","Last Name","Dob","Addresses"
//
// "John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
//
// "Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
// For FORECASTING
//
// [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
// The column names must contain the union of the model's
//
// [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
//
// [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
// and
//
// [target_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]
// (order doesn't matter), with values compatible with these column
// specs data types, except as specified below.
// The input rows must contain not only the to-be-predicted rows
// but also the historical data rows, even if they would be
// identical as the ones on which the model has been trained.
// The historical rows must have non-NULL target column
// values. The to-be-predicted rows must have NULL values in the
// target column and all columns having
//
// [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType.KEY]
// type, regardless if these columns are
// [nullable][google.cloud.automl.v1beta1.DataType.nullable].
// Prediction only on the to-be-predicted rows will be attempted.
// First four sample rows of a CSV file:
//
// "Year","City","OlympicsThatYear","Population","WaterUsedGigaGallons"
// "2000","NYC","true","8008278","452.7"
// "2001","NYC","false","8024963","432.2"
// "2002","NYC","true","",""
// BigQuery case:
// An URI of a BigQuery table. The user data size of the BigQuery
// table must be 100GB or smaller.
// For all CLASSIFICATION and REGRESSION
//
// [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
// The column names must contain the model's
//
// [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
//
// [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
// (order doesn't matter). The columns corresponding to the model's
// input feature column specs must contain values compatible with
// the column spec's data types. Prediction on all the rows of the
// table will be attempted.
// For FORECASTING
//
// [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
// The column names must contain the union of the model's
//
// [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
//
// [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
// and
//
// [target_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]
// (order doesn't matter), with values compatible with these column
// specs data types, except as specified below.
// The table's rows must contain not only the to-be-predicted rows
// but also the historical data rows, even if they would be
// identical as the ones on which the model has been trained.
// The historical rows must have non-NULL target column values.
// The to-be-predicted rows must have NULL values in the
// target column and all columns having
//
// [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType.KEY]
// type, regardless if these columns are
// [nullable][google.cloud.automl.v1beta1.DataType.nullable].
// Prediction only on the to-be-predicted rows will be attempted.
//
// Definitions:
// GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi".
// TIME_SEGMENT_START = TIME_OFFSET
// Expresses a beginning, inclusive, of a time segment
// within an
// example that has a time dimension (e.g. video).
// TIME_SEGMENT_END = TIME_OFFSET
// Expresses an end, exclusive, of a time segment within
// an example that has a time dimension (e.g. video).
// TIME_OFFSET = A number of seconds as measured from the start of an
// example (e.g. video). Fractions are allowed, up to a
// microsecond precision. "inf" is allowed and it means the end
// of the example.
//
// Errors:
// If any of the provided CSV files can't be parsed or if more than certain
// percent of CSV rows cannot be processed then the operation fails and
// prediction does not happen. Regardless of overall success or failure the
// per-row failures, up to a certain count cap, will be listed in
// Operation.metadata.partial_failures.
type BatchPredictInputConfig struct {
// Required. The source of the input.
//
// Types that are valid to be assigned to Source:
// *BatchPredictInputConfig_GcsSource
// *BatchPredictInputConfig_BigquerySource
Source isBatchPredictInputConfig_Source `protobuf_oneof:"source"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *BatchPredictInputConfig) Reset() { *m = BatchPredictInputConfig{} }
func (m *BatchPredictInputConfig) String() string { return proto.CompactTextString(m) }
func (*BatchPredictInputConfig) ProtoMessage() {}
func (*BatchPredictInputConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{1}
}
func (m *BatchPredictInputConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_BatchPredictInputConfig.Unmarshal(m, b)
}
func (m *BatchPredictInputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_BatchPredictInputConfig.Marshal(b, m, deterministic)
}
func (m *BatchPredictInputConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_BatchPredictInputConfig.Merge(m, src)
}
func (m *BatchPredictInputConfig) XXX_Size() int {
return xxx_messageInfo_BatchPredictInputConfig.Size(m)
}
func (m *BatchPredictInputConfig) XXX_DiscardUnknown() {
xxx_messageInfo_BatchPredictInputConfig.DiscardUnknown(m)
}
var xxx_messageInfo_BatchPredictInputConfig proto.InternalMessageInfo
type isBatchPredictInputConfig_Source interface {
isBatchPredictInputConfig_Source()
}
type BatchPredictInputConfig_GcsSource struct {
GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}
type BatchPredictInputConfig_BigquerySource struct {
BigquerySource *BigQuerySource `protobuf:"bytes,2,opt,name=bigquery_source,json=bigquerySource,proto3,oneof"`
}
func (*BatchPredictInputConfig_GcsSource) isBatchPredictInputConfig_Source() {}
func (*BatchPredictInputConfig_BigquerySource) isBatchPredictInputConfig_Source() {}
func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source {
if m != nil {
return m.Source
}
return nil
}
func (m *BatchPredictInputConfig) GetGcsSource() *GcsSource {
if x, ok := m.GetSource().(*BatchPredictInputConfig_GcsSource); ok {
return x.GcsSource
}
return nil
}
func (m *BatchPredictInputConfig) GetBigquerySource() *BigQuerySource {
if x, ok := m.GetSource().(*BatchPredictInputConfig_BigquerySource); ok {
return x.BigquerySource
}
return nil
}
// XXX_OneofWrappers is for the internal use of the proto package.
func (*BatchPredictInputConfig) XXX_OneofWrappers() []interface{} {
return []interface{}{
(*BatchPredictInputConfig_GcsSource)(nil),
(*BatchPredictInputConfig_BigquerySource)(nil),
}
}
// Input configuration of a [Document][google.cloud.automl.v1beta1.Document].
type DocumentInputConfig struct {
// The Google Cloud Storage location of the document file. Only a single path
// should be given.
// Max supported size: 512MB.
// Supported extensions: .PDF.
GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3" json:"gcs_source,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *DocumentInputConfig) Reset() { *m = DocumentInputConfig{} }
func (m *DocumentInputConfig) String() string { return proto.CompactTextString(m) }
func (*DocumentInputConfig) ProtoMessage() {}
func (*DocumentInputConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{2}
}
func (m *DocumentInputConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_DocumentInputConfig.Unmarshal(m, b)
}
func (m *DocumentInputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_DocumentInputConfig.Marshal(b, m, deterministic)
}
func (m *DocumentInputConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_DocumentInputConfig.Merge(m, src)
}
func (m *DocumentInputConfig) XXX_Size() int {
return xxx_messageInfo_DocumentInputConfig.Size(m)
}
func (m *DocumentInputConfig) XXX_DiscardUnknown() {
xxx_messageInfo_DocumentInputConfig.DiscardUnknown(m)
}
var xxx_messageInfo_DocumentInputConfig proto.InternalMessageInfo
func (m *DocumentInputConfig) GetGcsSource() *GcsSource {
if m != nil {
return m.GcsSource
}
return nil
}
// * For Translation:
// CSV file `translation.csv`, with each line in format:
// ML_USE,GCS_FILE_PATH
// GCS_FILE_PATH leads to a .TSV file which describes examples that have
// given ML_USE, using the following row format per line:
// TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target
// language)
//
// * For Tables:
// Output depends on whether the dataset was imported from GCS or
// BigQuery.
// GCS case:
//
// [gcs_destination][google.cloud.automl.v1beta1.OutputConfig.gcs_destination]
// must be set. Exported are CSV file(s) `tables_1.csv`,
// `tables_2.csv`,...,`tables_N.csv` with each having as header line
// the table's column names, and all other lines contain values for
// the header columns.
// BigQuery case:
//
// [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination]
// pointing to a BigQuery project must be set. In the given project a
// new dataset will be created with name
//
// `export_data_<automl-dataset-display-name>_<timestamp-of-export-call>`
// where <automl-dataset-display-name> will be made
// BigQuery-dataset-name compatible (e.g. most special characters will
// become underscores), and timestamp will be in
// YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that
// dataset a new table called `primary_table` will be created, and
// filled with precisely the same data as this obtained on import.
type OutputConfig struct {
// Required. The destination of the output.
//
// Types that are valid to be assigned to Destination:
// *OutputConfig_GcsDestination
// *OutputConfig_BigqueryDestination
Destination isOutputConfig_Destination `protobuf_oneof:"destination"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *OutputConfig) Reset() { *m = OutputConfig{} }
func (m *OutputConfig) String() string { return proto.CompactTextString(m) }
func (*OutputConfig) ProtoMessage() {}
func (*OutputConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{3}
}
func (m *OutputConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_OutputConfig.Unmarshal(m, b)
}
func (m *OutputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_OutputConfig.Marshal(b, m, deterministic)
}
func (m *OutputConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_OutputConfig.Merge(m, src)
}
func (m *OutputConfig) XXX_Size() int {
return xxx_messageInfo_OutputConfig.Size(m)
}
func (m *OutputConfig) XXX_DiscardUnknown() {
xxx_messageInfo_OutputConfig.DiscardUnknown(m)
}
var xxx_messageInfo_OutputConfig proto.InternalMessageInfo
type isOutputConfig_Destination interface {
isOutputConfig_Destination()
}
type OutputConfig_GcsDestination struct {
GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}
type OutputConfig_BigqueryDestination struct {
BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}
func (*OutputConfig_GcsDestination) isOutputConfig_Destination() {}
func (*OutputConfig_BigqueryDestination) isOutputConfig_Destination() {}
func (m *OutputConfig) GetDestination() isOutputConfig_Destination {
if m != nil {
return m.Destination
}
return nil
}
func (m *OutputConfig) GetGcsDestination() *GcsDestination {
if x, ok := m.GetDestination().(*OutputConfig_GcsDestination); ok {
return x.GcsDestination
}
return nil
}
func (m *OutputConfig) GetBigqueryDestination() *BigQueryDestination {
if x, ok := m.GetDestination().(*OutputConfig_BigqueryDestination); ok {
return x.BigqueryDestination
}
return nil
}
// XXX_OneofWrappers is for the internal use of the proto package.
func (*OutputConfig) XXX_OneofWrappers() []interface{} {
return []interface{}{
(*OutputConfig_GcsDestination)(nil),
(*OutputConfig_BigqueryDestination)(nil),
}
}
// Output configuration for BatchPredict Action.
//
// As destination the
//
// [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination]
// must be set unless specified otherwise for a domain. If gcs_destination is
// set then in the given directory a new directory will be created. Its name
// will be
// "prediction-<model-display-name>-<timestamp-of-prediction-call>",
// where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents
// of it depends on the ML problem the predictions are made for.
// * For Video Classification:
// In the created directory a video_classification.csv file, and a .JSON
// file per each video classification requested in the input (i.e. each
// line in given CSV(s)), will be created.
//
// The format of video_classification.csv is:
//
// GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
// where:
// GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
// the prediction input lines (i.e. video_classification.csv has
// precisely the same number of lines as the prediction input had.)
// JSON_FILE_NAME = Name of .JSON file in the output directory, which
// contains prediction responses for the video time segment.
// STATUS = "OK" if prediction completed successfully, or an error code
// with message otherwise. If STATUS is not "OK" then the .JSON file
// for that line may not exist or be empty.
//
// Each .JSON file, assuming STATUS is "OK", will contain a list of
// AnnotationPayload protos in JSON format, which are the predictions
// for the video time segment the file is assigned to in the
// video_classification.csv. All AnnotationPayload protos will have
// video_classification field set, and will be sorted by
// video_classification.type field (note that the returned types are
// governed by `classifaction_types` parameter in
// [PredictService.BatchPredictRequest.params][]).
//
// * For Video Object Tracking:
// In the created directory a video_object_tracking.csv file will be
// created, and multiple files video_object_trackinng_1.json,
// video_object_trackinng_2.json,..., video_object_trackinng_N.json,
// where N is the number of requests in the input (i.e. the number of
// lines in given CSV(s)).
//
// The format of video_object_tracking.csv is:
//
// GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
// where:
// GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
// the prediction input lines (i.e. video_object_tracking.csv has
// precisely the same number of lines as the prediction input had.)
// JSON_FILE_NAME = Name of .JSON file in the output directory, which
// contains prediction responses for the video time segment.
// STATUS = "OK" if prediction completed successfully, or an error
// code with message otherwise. If STATUS is not "OK" then the .JSON
// file for that line may not exist or be empty.
//
// Each .JSON file, assuming STATUS is "OK", will contain a list of
// AnnotationPayload protos in JSON format, which are the predictions
// for each frame of the video time segment the file is assigned to in
// video_object_tracking.csv. All AnnotationPayload protos will have
// video_object_tracking field set.
// * For Text Extraction:
// In the created directory files `text_extraction_1.jsonl`,
// `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl`
// will be created, where N may be 1, and depends on the
// total number of inputs and annotations found.
// The contents of these .JSONL file(s) depend on whether the input
// used inline text, or documents.
// If input was inline, then each .JSONL file will contain, per line,
// a JSON representation of a proto that wraps given in request text
// snippet's "id" : "<id_value>" followed by a list of zero or more
// AnnotationPayload protos (called annotations), which have
// text_extraction detail populated. A single text snippet will be
// listed only once with all its annotations, and its annotations will
// never be split across files.
// If input used documents, then each .JSONL file will contain, per
// line, a JSON representation of a proto that wraps given in request
// document proto, followed by its OCR-ed representation in the form
// of a text snippet, finally followed by a list of zero or more
// AnnotationPayload protos (called annotations), which have
// text_extraction detail populated and refer, via their indices, to
// the OCR-ed text snippet. A single document (and its text snippet)
// will be listed only once with all its annotations, and its
// annotations will never be split across files.
// If prediction for any text snippet failed (partially or completely),
// then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
// `errors_N.jsonl` files will be created (N depends on total number of
// failed predictions). These files will have a JSON representation of a
// proto that wraps either the "id" : "<id_value>" (in case of inline)
// or the document proto (in case of document) but here followed by
// exactly one
//
// [`google.rpc.Status`](https:
// //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
// containing only `code` and `message`.
//
// * For Tables:
// Output depends on whether
//
// [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination]
// or
//
// [bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination]
// is set (either is allowed).
// GCS case:
// In the created directory files `tables_1.csv`, `tables_2.csv`,...,
// `tables_N.csv` will be created, where N may be 1, and depends on
// the total number of the successfully predicted rows.
// For all CLASSIFICATION
//
// [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
// Each .csv file will contain a header, listing all columns'
//
// [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
// given on input followed by M target column names in the format of
//
// "<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>_<target
// value>_score" where M is the number of distinct target values,
// i.e. number of distinct values in the target column of the table
// used to train the model. Subsequent lines will contain the
// respective values of successfully predicted rows, with the last,
// i.e. the target, columns having the corresponding prediction
// [scores][google.cloud.automl.v1beta1.TablesAnnotation.score].
// For REGRESSION and FORECASTING
//
// [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
// Each .csv file will contain a header, listing all columns'
// [display_name-s][google.cloud.automl.v1beta1.display_name] given
// on input followed by the predicted target column with name in the
// format of
//
// "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>"
// Subsequent lines will contain the respective values of
// successfully predicted rows, with the last, i.e. the target,
// column having the predicted target value.
// If prediction for any rows failed, then an additional
// `errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be
// created (N depends on total number of failed rows). These files
// will have analogous format as `tables_*.csv`, but always with a
// single target column having
//
// [`google.rpc.Status`](https:
// //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
// represented as a JSON string, and containing only `code` and
// `message`.
// BigQuery case:
//
// [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination]
// pointing to a BigQuery project must be set. In the given project a
// new dataset will be created with name
// `prediction_<model-display-name>_<timestamp-of-prediction-call>`
// where <model-display-name> will be made
// BigQuery-dataset-name compatible (e.g. most special characters will
// become underscores), and timestamp will be in
// YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset
// two tables will be created, `predictions`, and `errors`.
// The `predictions` table's column names will be the input columns'
//
// [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
// followed by the target column with name in the format of
//
// "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>"
// The input feature columns will contain the respective values of
// successfully predicted rows, with the target column having an
// ARRAY of
//
// [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload],
// represented as STRUCT-s, containing
// [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].
// The `errors` table contains rows for which the prediction has
// failed, it has analogous input columns while the target column name
// is in the format of
//
// "errors_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>",
// and as a value has
//
// [`google.rpc.Status`](https:
// //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
// represented as a STRUCT, and containing only `code` and `message`.
type BatchPredictOutputConfig struct {
// Required. The destination of the output.
//
// Types that are valid to be assigned to Destination:
// *BatchPredictOutputConfig_GcsDestination
// *BatchPredictOutputConfig_BigqueryDestination
Destination isBatchPredictOutputConfig_Destination `protobuf_oneof:"destination"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *BatchPredictOutputConfig) Reset() { *m = BatchPredictOutputConfig{} }
func (m *BatchPredictOutputConfig) String() string { return proto.CompactTextString(m) }
func (*BatchPredictOutputConfig) ProtoMessage() {}
func (*BatchPredictOutputConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{4}
}
func (m *BatchPredictOutputConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_BatchPredictOutputConfig.Unmarshal(m, b)
}
func (m *BatchPredictOutputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_BatchPredictOutputConfig.Marshal(b, m, deterministic)
}
func (m *BatchPredictOutputConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_BatchPredictOutputConfig.Merge(m, src)
}
func (m *BatchPredictOutputConfig) XXX_Size() int {
return xxx_messageInfo_BatchPredictOutputConfig.Size(m)
}
func (m *BatchPredictOutputConfig) XXX_DiscardUnknown() {
xxx_messageInfo_BatchPredictOutputConfig.DiscardUnknown(m)
}
var xxx_messageInfo_BatchPredictOutputConfig proto.InternalMessageInfo
type isBatchPredictOutputConfig_Destination interface {
isBatchPredictOutputConfig_Destination()
}
type BatchPredictOutputConfig_GcsDestination struct {
GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}
type BatchPredictOutputConfig_BigqueryDestination struct {
BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}
func (*BatchPredictOutputConfig_GcsDestination) isBatchPredictOutputConfig_Destination() {}
func (*BatchPredictOutputConfig_BigqueryDestination) isBatchPredictOutputConfig_Destination() {}
func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination {
if m != nil {
return m.Destination
}
return nil
}
func (m *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination {
if x, ok := m.GetDestination().(*BatchPredictOutputConfig_GcsDestination); ok {
return x.GcsDestination
}
return nil
}
func (m *BatchPredictOutputConfig) GetBigqueryDestination() *BigQueryDestination {
if x, ok := m.GetDestination().(*BatchPredictOutputConfig_BigqueryDestination); ok {
return x.BigqueryDestination
}
return nil
}
// XXX_OneofWrappers is for the internal use of the proto package.
func (*BatchPredictOutputConfig) XXX_OneofWrappers() []interface{} {
return []interface{}{
(*BatchPredictOutputConfig_GcsDestination)(nil),
(*BatchPredictOutputConfig_BigqueryDestination)(nil),
}
}
// Output configuration for ModelExport Action.
type ModelExportOutputConfig struct {
// Required. The destination of the output.
//
// Types that are valid to be assigned to Destination:
// *ModelExportOutputConfig_GcsDestination
// *ModelExportOutputConfig_GcrDestination
Destination isModelExportOutputConfig_Destination `protobuf_oneof:"destination"`
// The format in which the model must be exported. The available, and default,
// formats depend on the problem and model type (if given problem and type
// combination doesn't have a format listed, it means its models are not
// exportable):
//
// * For Image Classification mobile-low-latency-1, mobile-versatile-1,
// mobile-high-accuracy-1:
// "tflite" (default), "edgetpu_tflite", "tf_saved_model", "docker".
//
// * For Image Classification mobile-core-ml-low-latency-1,
// mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1:
// "core_ml" (default).
// Formats description:
//
// * tflite - Used for Android mobile devices.
// * edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/)
// devices.
// * tf_saved_model - A tensorflow model in SavedModel format.
// * docker - Used for Docker containers. Use the params field to customize
// the container. The container is verified to work correctly on
// ubuntu 16.04 operating system. See more at
// [containers quickstart](https://cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
// * core_ml - Used for iOS mobile devices.
ModelFormat string `protobuf:"bytes,4,opt,name=model_format,json=modelFormat,proto3" json:"model_format,omitempty"`
// Additional model-type and format specific parameters describing the
// requirements for the to be exported model files, any string must be up to
// 25000 characters long.
//
// * For `docker` format:
// `cpu_architecture` - (string) "x86_64" (default).
// `gpu_architecture` - (string) "none" (default), "nvidia".
Params map[string]string `protobuf:"bytes,2,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *ModelExportOutputConfig) Reset() { *m = ModelExportOutputConfig{} }
func (m *ModelExportOutputConfig) String() string { return proto.CompactTextString(m) }
func (*ModelExportOutputConfig) ProtoMessage() {}
func (*ModelExportOutputConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{5}
}
func (m *ModelExportOutputConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_ModelExportOutputConfig.Unmarshal(m, b)
}
func (m *ModelExportOutputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_ModelExportOutputConfig.Marshal(b, m, deterministic)
}
func (m *ModelExportOutputConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_ModelExportOutputConfig.Merge(m, src)
}
func (m *ModelExportOutputConfig) XXX_Size() int {
return xxx_messageInfo_ModelExportOutputConfig.Size(m)
}
func (m *ModelExportOutputConfig) XXX_DiscardUnknown() {
xxx_messageInfo_ModelExportOutputConfig.DiscardUnknown(m)
}
var xxx_messageInfo_ModelExportOutputConfig proto.InternalMessageInfo
type isModelExportOutputConfig_Destination interface {
isModelExportOutputConfig_Destination()
}
type ModelExportOutputConfig_GcsDestination struct {
GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}
type ModelExportOutputConfig_GcrDestination struct {
GcrDestination *GcrDestination `protobuf:"bytes,3,opt,name=gcr_destination,json=gcrDestination,proto3,oneof"`
}
func (*ModelExportOutputConfig_GcsDestination) isModelExportOutputConfig_Destination() {}
func (*ModelExportOutputConfig_GcrDestination) isModelExportOutputConfig_Destination() {}
func (m *ModelExportOutputConfig) GetDestination() isModelExportOutputConfig_Destination {
if m != nil {
return m.Destination
}
return nil
}
func (m *ModelExportOutputConfig) GetGcsDestination() *GcsDestination {
if x, ok := m.GetDestination().(*ModelExportOutputConfig_GcsDestination); ok {
return x.GcsDestination
}
return nil
}
func (m *ModelExportOutputConfig) GetGcrDestination() *GcrDestination {
if x, ok := m.GetDestination().(*ModelExportOutputConfig_GcrDestination); ok {
return x.GcrDestination
}
return nil
}
func (m *ModelExportOutputConfig) GetModelFormat() string {
if m != nil {
return m.ModelFormat
}
return ""
}
func (m *ModelExportOutputConfig) GetParams() map[string]string {
if m != nil {
return m.Params
}
return nil
}
// XXX_OneofWrappers is for the internal use of the proto package.
func (*ModelExportOutputConfig) XXX_OneofWrappers() []interface{} {
return []interface{}{
(*ModelExportOutputConfig_GcsDestination)(nil),
(*ModelExportOutputConfig_GcrDestination)(nil),
}
}
// Output configuration for ExportEvaluatedExamples Action. Note that this call
// is available only for 30 days since the moment the model was evaluated.
// The output depends on the domain, as follows (note that only examples from
// the TEST set are exported):
//
// * For Tables:
//
// [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination]
// pointing to a BigQuery project must be set. In the given project a
// new dataset will be created with name
//
// `export_evaluated_examples_<model-display-name>_<timestamp-of-export-call>`
// where <model-display-name> will be made BigQuery-dataset-name
// compatible (e.g. most special characters will become underscores),
// and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601"
// format. In the dataset an `evaluated_examples` table will be
// created. It will have all the same columns as the
//
// [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id]
// of the
// [dataset][google.cloud.automl.v1beta1.Model.dataset_id] from which
// the model was created, as they were at the moment of model's
// evaluation (this includes the target column with its ground
// truth), followed by a column called "predicted_<target_column>". That
// last column will contain the model's prediction result for each
// respective row, given as ARRAY of
// [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload],
// represented as STRUCT-s, containing
// [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].
type ExportEvaluatedExamplesOutputConfig struct {
// Required. The destination of the output.
//
// Types that are valid to be assigned to Destination:
// *ExportEvaluatedExamplesOutputConfig_BigqueryDestination
Destination isExportEvaluatedExamplesOutputConfig_Destination `protobuf_oneof:"destination"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *ExportEvaluatedExamplesOutputConfig) Reset() { *m = ExportEvaluatedExamplesOutputConfig{} }
func (m *ExportEvaluatedExamplesOutputConfig) String() string { return proto.CompactTextString(m) }
func (*ExportEvaluatedExamplesOutputConfig) ProtoMessage() {}
func (*ExportEvaluatedExamplesOutputConfig) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{6}
}
func (m *ExportEvaluatedExamplesOutputConfig) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_ExportEvaluatedExamplesOutputConfig.Unmarshal(m, b)
}
func (m *ExportEvaluatedExamplesOutputConfig) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_ExportEvaluatedExamplesOutputConfig.Marshal(b, m, deterministic)
}
func (m *ExportEvaluatedExamplesOutputConfig) XXX_Merge(src proto.Message) {
xxx_messageInfo_ExportEvaluatedExamplesOutputConfig.Merge(m, src)
}
func (m *ExportEvaluatedExamplesOutputConfig) XXX_Size() int {
return xxx_messageInfo_ExportEvaluatedExamplesOutputConfig.Size(m)
}
func (m *ExportEvaluatedExamplesOutputConfig) XXX_DiscardUnknown() {
xxx_messageInfo_ExportEvaluatedExamplesOutputConfig.DiscardUnknown(m)
}
var xxx_messageInfo_ExportEvaluatedExamplesOutputConfig proto.InternalMessageInfo
type isExportEvaluatedExamplesOutputConfig_Destination interface {
isExportEvaluatedExamplesOutputConfig_Destination()
}
type ExportEvaluatedExamplesOutputConfig_BigqueryDestination struct {
BigqueryDestination *BigQueryDestination `protobuf:"bytes,2,opt,name=bigquery_destination,json=bigqueryDestination,proto3,oneof"`
}
func (*ExportEvaluatedExamplesOutputConfig_BigqueryDestination) isExportEvaluatedExamplesOutputConfig_Destination() {
}
func (m *ExportEvaluatedExamplesOutputConfig) GetDestination() isExportEvaluatedExamplesOutputConfig_Destination {
if m != nil {
return m.Destination
}
return nil
}
func (m *ExportEvaluatedExamplesOutputConfig) GetBigqueryDestination() *BigQueryDestination {
if x, ok := m.GetDestination().(*ExportEvaluatedExamplesOutputConfig_BigqueryDestination); ok {
return x.BigqueryDestination
}
return nil
}
// XXX_OneofWrappers is for the internal use of the proto package.
func (*ExportEvaluatedExamplesOutputConfig) XXX_OneofWrappers() []interface{} {
return []interface{}{
(*ExportEvaluatedExamplesOutputConfig_BigqueryDestination)(nil),
}
}
// The Google Cloud Storage location for the input content.
type GcsSource struct {
// Required. Google Cloud Storage URIs to input files, up to 2000 characters
// long. Accepted forms:
// * Full object path, e.g. gs://bucket/directory/object.csv
InputUris []string `protobuf:"bytes,1,rep,name=input_uris,json=inputUris,proto3" json:"input_uris,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *GcsSource) Reset() { *m = GcsSource{} }
func (m *GcsSource) String() string { return proto.CompactTextString(m) }
func (*GcsSource) ProtoMessage() {}
func (*GcsSource) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{7}
}
func (m *GcsSource) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_GcsSource.Unmarshal(m, b)
}
func (m *GcsSource) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_GcsSource.Marshal(b, m, deterministic)
}
func (m *GcsSource) XXX_Merge(src proto.Message) {
xxx_messageInfo_GcsSource.Merge(m, src)
}
func (m *GcsSource) XXX_Size() int {
return xxx_messageInfo_GcsSource.Size(m)
}
func (m *GcsSource) XXX_DiscardUnknown() {
xxx_messageInfo_GcsSource.DiscardUnknown(m)
}
var xxx_messageInfo_GcsSource proto.InternalMessageInfo
func (m *GcsSource) GetInputUris() []string {
if m != nil {
return m.InputUris
}
return nil
}
// The BigQuery location for the input content.
type BigQuerySource struct {
// Required. BigQuery URI to a table, up to 2000 characters long.
// Accepted forms:
// * BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId
InputUri string `protobuf:"bytes,1,opt,name=input_uri,json=inputUri,proto3" json:"input_uri,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *BigQuerySource) Reset() { *m = BigQuerySource{} }
func (m *BigQuerySource) String() string { return proto.CompactTextString(m) }
func (*BigQuerySource) ProtoMessage() {}
func (*BigQuerySource) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{8}
}
func (m *BigQuerySource) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_BigQuerySource.Unmarshal(m, b)
}
func (m *BigQuerySource) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_BigQuerySource.Marshal(b, m, deterministic)
}
func (m *BigQuerySource) XXX_Merge(src proto.Message) {
xxx_messageInfo_BigQuerySource.Merge(m, src)
}
func (m *BigQuerySource) XXX_Size() int {
return xxx_messageInfo_BigQuerySource.Size(m)
}
func (m *BigQuerySource) XXX_DiscardUnknown() {
xxx_messageInfo_BigQuerySource.DiscardUnknown(m)
}
var xxx_messageInfo_BigQuerySource proto.InternalMessageInfo
func (m *BigQuerySource) GetInputUri() string {
if m != nil {
return m.InputUri
}
return ""
}
// The Google Cloud Storage location where the output is to be written to.
type GcsDestination struct {
// Required. Google Cloud Storage URI to output directory, up to 2000
// characters long.
// Accepted forms:
// * Prefix path: gs://bucket/directory
// The requesting user must have write permission to the bucket.
// The directory is created if it doesn't exist.
OutputUriPrefix string `protobuf:"bytes,1,opt,name=output_uri_prefix,json=outputUriPrefix,proto3" json:"output_uri_prefix,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *GcsDestination) Reset() { *m = GcsDestination{} }
func (m *GcsDestination) String() string { return proto.CompactTextString(m) }
func (*GcsDestination) ProtoMessage() {}
func (*GcsDestination) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{9}
}
func (m *GcsDestination) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_GcsDestination.Unmarshal(m, b)
}
func (m *GcsDestination) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_GcsDestination.Marshal(b, m, deterministic)
}
func (m *GcsDestination) XXX_Merge(src proto.Message) {
xxx_messageInfo_GcsDestination.Merge(m, src)
}
func (m *GcsDestination) XXX_Size() int {
return xxx_messageInfo_GcsDestination.Size(m)
}
func (m *GcsDestination) XXX_DiscardUnknown() {
xxx_messageInfo_GcsDestination.DiscardUnknown(m)
}
var xxx_messageInfo_GcsDestination proto.InternalMessageInfo
func (m *GcsDestination) GetOutputUriPrefix() string {
if m != nil {
return m.OutputUriPrefix
}
return ""
}
// The BigQuery location for the output content.
type BigQueryDestination struct {
// Required. BigQuery URI to a project, up to 2000 characters long.
// Accepted forms:
// * BigQuery path e.g. bq://projectId
OutputUri string `protobuf:"bytes,1,opt,name=output_uri,json=outputUri,proto3" json:"output_uri,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *BigQueryDestination) Reset() { *m = BigQueryDestination{} }
func (m *BigQueryDestination) String() string { return proto.CompactTextString(m) }
func (*BigQueryDestination) ProtoMessage() {}
func (*BigQueryDestination) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{10}
}
func (m *BigQueryDestination) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_BigQueryDestination.Unmarshal(m, b)
}
func (m *BigQueryDestination) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_BigQueryDestination.Marshal(b, m, deterministic)
}
func (m *BigQueryDestination) XXX_Merge(src proto.Message) {
xxx_messageInfo_BigQueryDestination.Merge(m, src)
}
func (m *BigQueryDestination) XXX_Size() int {
return xxx_messageInfo_BigQueryDestination.Size(m)
}
func (m *BigQueryDestination) XXX_DiscardUnknown() {
xxx_messageInfo_BigQueryDestination.DiscardUnknown(m)
}
var xxx_messageInfo_BigQueryDestination proto.InternalMessageInfo
func (m *BigQueryDestination) GetOutputUri() string {
if m != nil {
return m.OutputUri
}
return ""
}
// The GCR location where the image must be pushed to.
type GcrDestination struct {
// Required. Google Contained Registry URI of the new image, up to 2000
// characters long. See
//
// https:
// //cloud.google.com/container-registry/do
// // cs/pushing-and-pulling#pushing_an_image_to_a_registry
// Accepted forms:
// * [HOSTNAME]/[PROJECT-ID]/[IMAGE]
// * [HOSTNAME]/[PROJECT-ID]/[IMAGE]:[TAG]
//
// The requesting user must have permission to push images the project.
OutputUri string `protobuf:"bytes,1,opt,name=output_uri,json=outputUri,proto3" json:"output_uri,omitempty"`
XXX_NoUnkeyedLiteral struct{} `json:"-"`
XXX_unrecognized []byte `json:"-"`
XXX_sizecache int32 `json:"-"`
}
func (m *GcrDestination) Reset() { *m = GcrDestination{} }
func (m *GcrDestination) String() string { return proto.CompactTextString(m) }
func (*GcrDestination) ProtoMessage() {}
func (*GcrDestination) Descriptor() ([]byte, []int) {
return fileDescriptor_6e2d768504aa30d7, []int{11}
}
func (m *GcrDestination) XXX_Unmarshal(b []byte) error {
return xxx_messageInfo_GcrDestination.Unmarshal(m, b)
}
func (m *GcrDestination) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) {
return xxx_messageInfo_GcrDestination.Marshal(b, m, deterministic)
}
func (m *GcrDestination) XXX_Merge(src proto.Message) {
xxx_messageInfo_GcrDestination.Merge(m, src)
}
func (m *GcrDestination) XXX_Size() int {
return xxx_messageInfo_GcrDestination.Size(m)
}
func (m *GcrDestination) XXX_DiscardUnknown() {
xxx_messageInfo_GcrDestination.DiscardUnknown(m)
}
var xxx_messageInfo_GcrDestination proto.InternalMessageInfo
func (m *GcrDestination) GetOutputUri() string {
if m != nil {
return m.OutputUri
}
return ""
}
func init() {
proto.RegisterType((*InputConfig)(nil), "google.cloud.automl.v1beta1.InputConfig")
proto.RegisterMapType((map[string]string)(nil), "google.cloud.automl.v1beta1.InputConfig.ParamsEntry")
proto.RegisterType((*BatchPredictInputConfig)(nil), "google.cloud.automl.v1beta1.BatchPredictInputConfig")
proto.RegisterType((*DocumentInputConfig)(nil), "google.cloud.automl.v1beta1.DocumentInputConfig")
proto.RegisterType((*OutputConfig)(nil), "google.cloud.automl.v1beta1.OutputConfig")
proto.RegisterType((*BatchPredictOutputConfig)(nil), "google.cloud.automl.v1beta1.BatchPredictOutputConfig")
proto.RegisterType((*ModelExportOutputConfig)(nil), "google.cloud.automl.v1beta1.ModelExportOutputConfig")
proto.RegisterMapType((map[string]string)(nil), "google.cloud.automl.v1beta1.ModelExportOutputConfig.ParamsEntry")
proto.RegisterType((*ExportEvaluatedExamplesOutputConfig)(nil), "google.cloud.automl.v1beta1.ExportEvaluatedExamplesOutputConfig")
proto.RegisterType((*GcsSource)(nil), "google.cloud.automl.v1beta1.GcsSource")
proto.RegisterType((*BigQuerySource)(nil), "google.cloud.automl.v1beta1.BigQuerySource")
proto.RegisterType((*GcsDestination)(nil), "google.cloud.automl.v1beta1.GcsDestination")
proto.RegisterType((*BigQueryDestination)(nil), "google.cloud.automl.v1beta1.BigQueryDestination")
proto.RegisterType((*GcrDestination)(nil), "google.cloud.automl.v1beta1.GcrDestination")
}
func init() {
proto.RegisterFile("google/cloud/automl/v1beta1/io.proto", fileDescriptor_6e2d768504aa30d7)
}
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}