automl/v1/io.pb.go

// Code generated by protoc-gen-go. DO NOT EDIT.
// source: google/cloud/automl/v1/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 [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.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.v1.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 (that is, 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:
//
// <h4>AutoML Vision</h4>
//
//
// <div class="ds-selector-tabs"><section><h5>Classification</h5>
//
// See [Preparing your training
// data](https://cloud.google.com/vision/automl/docs/prepare) for more
// information.
//
// CSV file(s) with each line in format:
//
//     ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
//
// *   `ML_USE` - Identifies the data set that the current row (file) applies
// to.
//     This value can be one of the following:
//     * `TRAIN` - Rows in this file are used to train the model.
//     * `TEST` - Rows in this file are used to test the model during training.
//     * `UNASSIGNED` - Rows in this file are not categorized. They are
//        Automatically divided into train and test data. 80% for training and
//        20% for testing.
//
// *   `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to
//      30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP,
//      .TIFF, .ICO.
//
// *   `LABEL` - A label that identifies the object in the image.
//
// For the `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
//
//
// </section><section><h5>Object Detection</h5>
// See [Preparing your training
// data](https://cloud.google.com/vision/automl/object-detection/docs/prepare)
// for more information.
//
// A CSV file(s) with each line in format:
//
//     ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)
//
// *   `ML_USE` - Identifies the data set that the current row (file) applies
// to.
//     This value can be one of the following:
//     * `TRAIN` - Rows in this file are used to train the model.
//     * `TEST` - Rows in this file are used to test the model during training.
//     * `UNASSIGNED` - Rows in this file are not categorized. They are
//        Automatically divided into train and test data. 80% for training and
//        20% for testing.
//
// *  `GCS_FILE_PATH` - The Google Cloud Storage location of an image of up to
//     30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image
//     is assumed to be exhaustively labeled.
//
// *  `LABEL` - A label that identifies the object in the image specified by the
//    `BOUNDING_BOX`.
//
// *  `BOUNDING BOX` - The vertices of an object in the example image.
//    The minimum allowed `BOUNDING_BOX` edge length is 0.01, and no more than
//    500 `BOUNDING_BOX` instances per image are allowed (one `BOUNDING_BOX`
//    per line). If an image has no looked for objects 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,,,,,,,,,
//   </section>
// </div>
//
//
// <h4>AutoML Video Intelligence</h4>
//
//
// <div class="ds-selector-tabs"><section><h5>Classification</h5>
//
// See [Preparing your training
// data](https://cloud.google.com/video-intelligence/automl/docs/prepare) for
// more information.
//
// CSV file(s) with each line in format:
//
//     ML_USE,GCS_FILE_PATH
//
// For `ML_USE`, do not use `VALIDATE`.
//
// `GCS_FILE_PATH` is the path to another .csv file that describes training
// example for a 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 the end time must be after the start time. 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 should 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,,,
//
//
//
// </section><section><h5>Object Tracking</h5>
//
// See [Preparing your training
// data](/video-intelligence/automl/object-tracking/docs/prepare) for more
// information.
//
// CSV file(s) with each line in format:
//
//     ML_USE,GCS_FILE_PATH
//
// For `ML_USE`, do not use `VALIDATE`.
//
// `GCS_FILE_PATH` is the path to another .csv file that describes training
// example for a given `ML_USE`, using 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_ID`s 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,,,,,,,,,,,
//   </section>
// </div>
//
//
// <h4>AutoML Natural Language</h4>
//
//
// <div class="ds-selector-tabs"><section><h5>Entity Extraction</h5>
//
// See [Preparing your training
// data](/natural-language/automl/entity-analysis/docs/prepare) for more
// information.
//
// One or more CSV file(s) with each line in the following format:
//
//     ML_USE,GCS_FILE_PATH
//
// *   `ML_USE` - Identifies the data set that the current row (file) applies
// to.
//     This value can be one of the following:
//     * `TRAIN` - Rows in this file are used to train the model.
//     * `TEST` - Rows in this file are used to test the model during training.
//     * `UNASSIGNED` - Rows in this file are not categorized. They are
//        Automatically divided into train and test data. 80% for training and
//        20% for testing..
//
// *   `GCS_FILE_PATH` - a Identifies JSON Lines (.JSONL) file stored in
//      Google Cloud Storage that contains in-line text in-line as documents
//      for model training.
//
// After the training data set has been determined from the `TRAIN` and
// `UNASSIGNED` CSV files, the training data is divided into train and
// validation data sets. 70% for training and 30% for validation.
//
// For example:
//
//     TRAIN,gs://folder/file1.jsonl
//     VALIDATE,gs://folder/file2.jsonl
//     TEST,gs://folder/file3.jsonl
//
// **In-line JSONL files**
//
// In-line .JSONL files contain, per line, a JSON document that wraps a
// [`text_snippet`][google.cloud.automl.v1.TextSnippet] field followed by
// one or more [`annotations`][google.cloud.automl.v1.AnnotationPayload]
// fields, which have `display_name` and `text_extraction` fields to describe
// the entity from the text snippet. Multiple JSON documents can be separated
// using line breaks (\n).
//
// The supplied text must be annotated exhaustively. For example, if you
// include the text "horse", but do not label it as "animal",
// then "horse" is 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 is accepted as it is
// UTF-8 NFC encoded.
//
// For example:
//
//     {
//       "text_snippet": {
//         "content": "dog car cat"
//       },
//       "annotations": [
//          {
//            "display_name": "animal",
//            "text_extraction": {
//              "text_segment": {"start_offset": 0, "end_offset": 2}
//           }
//          },
//          {
//           "display_name": "vehicle",
//            "text_extraction": {
//              "text_segment": {"start_offset": 4, "end_offset": 6}
//            }
//          },
//          {
//            "display_name": "animal",
//            "text_extraction": {
//              "text_segment": {"start_offset": 8, "end_offset": 10}
//            }
//          }
//      ]
//     }\n
//     {
//        "text_snippet": {
//          "content": "This dog is good."
//        },
//        "annotations": [
//           {
//             "display_name": "animal",
//             "text_extraction": {
//               "text_segment": {"start_offset": 5, "end_offset": 7}
//             }
//           }
//        ]
//     }
//
// **JSONL files that reference documents**
//
// .JSONL files contain, per line, a JSON document that wraps a
// `input_config` that contains the path to a source document.
// Multiple JSON documents can be separated using line breaks (\n).
//
// Supported document extensions: .PDF, .TIF, .TIFF
//
// For example:
//
//     {
//       "document": {
//         "input_config": {
//           "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
//           }
//         }
//       }
//     }\n
//     {
//       "document": {
//         "input_config": {
//           "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ]
//           }
//         }
//       }
//     }
//
// **In-line JSONL files with document layout information**
//
// **Note:** You can only annotate documents using the UI. The format described
// below applies to annotated documents exported using the UI or `exportData`.
//
// In-line .JSONL files for documents contain, per line, a JSON document
// that wraps a `document` field that provides the textual content of the
// document and the layout information.
//
// For example:
//
//     {
//       "document": {
//               "document_text": {
//                 "content": "dog car cat"
//               }
//               "layout": [
//                 {
//                   "text_segment": {
//                     "start_offset": 0,
//                     "end_offset": 11,
//                    },
//                    "page_number": 1,
//                    "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},
//                       ],
//                     },
//                     "text_segment_type": TOKEN,
//                 }
//               ],
//               "document_dimensions": {
//                 "width": 8.27,
//                 "height": 11.69,
//                 "unit": INCH,
//               }
//               "page_count": 3,
//             },
//             "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}
//                 }
//               },
//             ],
//
//
//
//
// </section><section><h5>Classification</h5>
//
// See [Preparing your training
// data](https://cloud.google.com/natural-language/automl/docs/prepare) for more
// information.
//
// One or more CSV file(s) with each line in the following format:
//
//     ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...
//
// *   `ML_USE` - Identifies the data set that the current row (file) applies
// to.
//     This value can be one of the following:
//     * `TRAIN` - Rows in this file are used to train the model.
//     * `TEST` - Rows in this file are used to test the model during training.
//     * `UNASSIGNED` - Rows in this file are not categorized. They are
//        Automatically divided into train and test data. 80% for training and
//        20% for testing.
//
// *   `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a pattern. If
//     the column content is a valid Google Cloud Storage file path, that is,
//     prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, if
//     the content is enclosed in double quotes (""), it is treated as a
//     `TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a
//     file with supported extension and UTF-8 encoding, for example,
//     "gs://folder/content.txt" AutoML imports the file content
//     as a text snippet. For `TEXT_SNIPPET`, AutoML imports the column content
//     excluding quotes. In both cases, size of the content must be 10MB or
//     less in size. For zip files, the size of each file inside the zip must be
//     10MB or less in size.
//
//     For the `MULTICLASS` classification type, at most one `LABEL` is allowed.
//
//     The `ML_USE` and `LABEL` columns are optional.
//     Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP
//
// A maximum of 100 unique labels are allowed per CSV row.
//
// Sample rows:
//
//     TRAIN,"They have bad food and very rude",RudeService,BadFood
//     gs://folder/content.txt,SlowService
//     TEST,gs://folder/document.pdf
//     VALIDATE,gs://folder/text_files.zip,BadFood
//
//
//
// </section><section><h5>Sentiment Analysis</h5>
//
// See [Preparing your training
// data](https://cloud.google.com/natural-language/automl/docs/prepare) for more
// information.
//
// CSV file(s) with each line in format:
//
//     ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT
//
// *   `ML_USE` - Identifies the data set that the current row (file) applies
// to.
//     This value can be one of the following:
//     * `TRAIN` - Rows in this file are used to train the model.
//     * `TEST` - Rows in this file are used to test the model during training.
//     * `UNASSIGNED` - Rows in this file are not categorized. They are
//        Automatically divided into train and test data. 80% for training and
//        20% for testing.
//
// *   `TEXT_SNIPPET` and `GCS_FILE_PATH` are distinguished by a pattern. If
//     the column content is a valid  Google Cloud Storage file path, that is,
//     prefixed by "gs://", it is treated as a `GCS_FILE_PATH`. Otherwise, if
//     the content is enclosed in double quotes (""), it is treated as a
//     `TEXT_SNIPPET`. For `GCS_FILE_PATH`, the path must lead to a
//     file with supported extension and UTF-8 encoding, for example,
//     "gs://folder/content.txt" AutoML imports the file content
//     as a text snippet. For `TEXT_SNIPPET`, AutoML imports the column content
//     excluding quotes. In both cases, size of the content must be 128kB or
//     less in size. For zip files, the size of each file inside the zip must be
//     128kB or less in size.
//
//     The `ML_USE` and `SENTIMENT` columns are optional.
//     Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP
//
// *  `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
//     large.
//
// Sample rows:
//
//     TRAIN,"@freewrytin this is way too good for your product",2
//     gs://folder/content.txt,3
//     TEST,gs://folder/document.pdf
//     VALIDATE,gs://folder/text_files.zip,2
//   </section>
// </div>
//
//
//
// <h4>AutoML Tables</h4><div class="ui-datasection-main"><section
// class="selected">
//
// See [Preparing your training
// data](https://cloud.google.com/automl-tables/docs/prepare) for more
// information.
//
// You can use either
// [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or
// [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source].
// All input is concatenated into a
// single
//
// [primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id]
//
// **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:
// <pre>
// "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"}]}
// </pre>
// **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.
//
//   </section>
// </div>
//
//
// **Input field 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`
// : The path to a file on Google Cloud Storage. For example,
//   "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
//   n 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`
// : The content of a text snippet, UTF-8 encoded, enclosed within
//   double quotes ("").
//
// `DOCUMENT`
// : A field that provides the textual content with document and the layout
//   information.
//
//
//  **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, is 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
	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.
	//
	// <h4>AutoML Tables</h4>
	//
	// `schema_inference_version`
	// : (integer) This value must be supplied.
	//   The version of the
	//   algorithm to use for the initial inference of the
	//   column data types of the imported table. 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_929966d18309cd53, []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"`
}

func (*InputConfig_GcsSource) 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) 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),
	}
}

// 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.v1.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:
//
// <h4>AutoML Vision</h4>
// <div class="ds-selector-tabs"><section><h5>Classification</h5>
//
// One or more CSV files where each line is a single column:
//
//     GCS_FILE_PATH
//
// The Google Cloud Storage location of an image of up to
// 30MB in size. Supported extensions: .JPEG, .GIF, .PNG.
// This path is treated as the ID in the batch predict output.
//
// Sample rows:
//
//     gs://folder/image1.jpeg
//     gs://folder/image2.gif
//     gs://folder/image3.png
//
// </section><section><h5>Object Detection</h5>
//
// One or more CSV files where each line is a single column:
//
//     GCS_FILE_PATH
//
// The Google Cloud Storage location of an image of up to
// 30MB in size. Supported extensions: .JPEG, .GIF, .PNG.
// This path is treated as the ID in the batch predict output.
//
// Sample rows:
//
//     gs://folder/image1.jpeg
//     gs://folder/image2.gif
//     gs://folder/image3.png
//   </section>
// </div>
//
// <h4>AutoML Video Intelligence</h4>
// <div class="ds-selector-tabs"><section><h5>Classification</h5>
//
// One or more CSV files where each line is a single column:
//
//     GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
//
// `GCS_FILE_PATH` is the Google Cloud Storage location of video up to 50GB in
// size and up to 3h in duration duration.
// Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
//
// `TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the
// length of the video, and the end time must be after the start time.
//
// Sample rows:
//
//     gs://folder/video1.mp4,10,40
//     gs://folder/video1.mp4,20,60
//     gs://folder/vid2.mov,0,inf
//
// </section><section><h5>Object Tracking</h5>
//
// One or more CSV files where each line is a single column:
//
//     GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
//
// `GCS_FILE_PATH` is the Google Cloud Storage location of video up to 50GB in
// size and up to 3h in duration duration.
// Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
//
// `TIME_SEGMENT_START` and `TIME_SEGMENT_END` must be within the
// length of the video, and the end time must be after the start time.
//
// Sample rows:
//
//     gs://folder/video1.mp4,10,40
//     gs://folder/video1.mp4,20,60
//     gs://folder/vid2.mov,0,inf
//   </section>
// </div>
//
// <h4>AutoML Natural Language</h4>
// <div class="ds-selector-tabs"><section><h5>Classification</h5>
//
// One or more CSV files where each line is a single column:
//
//     GCS_FILE_PATH
//
// `GCS_FILE_PATH` is the Google Cloud Storage location of a text file.
// Supported file extensions: .TXT, .PDF, .TIF, .TIFF
//
// Text files can be no larger than 10MB in size.
//
// Sample rows:
//
//     gs://folder/text1.txt
//     gs://folder/text2.pdf
//     gs://folder/text3.tif
//
// </section><section><h5>Sentiment Analysis</h5>
// One or more CSV files where each line is a single column:
//
//     GCS_FILE_PATH
//
// `GCS_FILE_PATH` is the Google Cloud Storage location of a text file.
// Supported file extensions: .TXT, .PDF, .TIF, .TIFF
//
// Text files can be no larger than 128kB in size.
//
// Sample rows:
//
//     gs://folder/text1.txt
//     gs://folder/text2.pdf
//     gs://folder/text3.tif
//
// </section><section><h5>Entity Extraction</h5>
//
// One or more JSONL (JSON Lines) files that either provide inline text or
// documents. You can only use one format, either inline text or documents,
// for a single call to [AutoMl.BatchPredict].
//
// Each JSONL file contains a 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.
//
// Each document JSONL file contains, per line, a proto that wraps a Document
// proto with `input_config` set. Each document cannot exceed 2MB in size.
//
// Supported document extensions: .PDF, .TIF, .TIFF
//
// Each JSONL file must not exceed 100MB in size, and no more than 20
// JSONL files may be passed.
//
// Sample inline JSONL file (Shown with artificial line
// breaks. Actual line breaks are 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": "Extended sample content",
//          "mime_type": "text/plain"
//        }
//      }
//
// Sample document JSONL file (Shown with artificial line
// breaks. Actual line breaks are 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.tif" ]
//            }
//          }
//        }
//      }
//   </section>
// </div>
//
// <h4>AutoML Tables</h4><div class="ui-datasection-main"><section
// class="selected">
//
// See [Preparing your training
// data](https://cloud.google.com/automl-tables/docs/predict-batch) for more
// information.
//
// You can use either
// [gcs_source][google.cloud.automl.v1.BatchPredictInputConfig.gcs_source]
// or
// [bigquery_source][BatchPredictInputConfig.bigquery_source].
//
// **For gcs_source:**
//
// 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.
//
// The column names must contain the model's
//
// [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs]
// [display_name-s][google.cloud.automl.v1.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.
//
//
// Sample rows from a CSV file:
// <pre>
// "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"}]}
// </pre>
// **For bigquery_source:**
//
// The URI of a BigQuery table. The user data size of the BigQuery
// table must be 100GB or smaller.
//
// The column names must contain the model's
//
// [input_feature_column_specs'][google.cloud.automl.v1.TablesModelMetadata.input_feature_column_specs]
// [display_name-s][google.cloud.automl.v1.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.
//   </section>
// </div>
//
// **Input field definitions:**
//
// `GCS_FILE_PATH`
// : The path to a file on Google Cloud Storage. For example,
//   "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
//   n 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 {
	// The source of the input.
	//
	// Types that are valid to be assigned to Source:
	//	*BatchPredictInputConfig_GcsSource
	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_929966d18309cd53, []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"`
}

func (*BatchPredictInputConfig_GcsSource) 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
}

// XXX_OneofWrappers is for the internal use of the proto package.
func (*BatchPredictInputConfig) XXX_OneofWrappers() []interface{} {
	return []interface{}{
		(*BatchPredictInputConfig_GcsSource)(nil),
	}
}

// Input configuration of a [Document][google.cloud.automl.v1.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_929966d18309cd53, []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 Google Cloud
//         Storage or BigQuery.
//         Google Cloud Storage case:
//
// [gcs_destination][google.cloud.automl.v1p1beta.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.v1p1beta.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 {
	// The destination of the output.
	//
	// Types that are valid to be assigned to Destination:
	//	*OutputConfig_GcsDestination
	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_929966d18309cd53, []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"`
}

func (*OutputConfig_GcsDestination) 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
}

// XXX_OneofWrappers is for the internal use of the proto package.
func (*OutputConfig) XXX_OneofWrappers() []interface{} {
	return []interface{}{
		(*OutputConfig_GcsDestination)(nil),
	}
}

// Output configuration for BatchPredict Action.
//
// As destination the
//
// [gcs_destination][google.cloud.automl.v1.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 is 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 Image Classification:
//         In the created directory files `image_classification_1.jsonl`,
//         `image_classification_2.jsonl`,...,`image_classification_N.jsonl`
//         will be created, where N may be 1, and depends on the
//         total number of the successfully predicted images and annotations.
//         A single image will be listed only once with all its annotations,
//         and its annotations will never be split across files.
//         Each .JSONL file will contain, per line, a JSON representation of a
//         proto that wraps image's "ID" : "<id_value>" followed by a list of
//         zero or more AnnotationPayload protos (called annotations), which
//         have classification detail populated.
//         If prediction for any image failed (partially or completely), then an
//         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 the same "ID" : "<id_value>" 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`fields.
//
//  *  For Image Object Detection:
//         In the created directory files `image_object_detection_1.jsonl`,
//         `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl`
//         will be created, where N may be 1, and depends on the
//         total number of the successfully predicted images and annotations.
//         Each .JSONL file will contain, per line, a JSON representation of a
//         proto that wraps image's "ID" : "<id_value>" followed by a list of
//         zero or more AnnotationPayload protos (called annotations), which
//         have image_object_detection detail populated. A single image will
//         be listed only once with all its annotations, and its annotations
//         will never be split across files.
//         If prediction for any image 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 the same "ID" : "<id_value>" 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`fields.
//  *  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 Classification:
//         In the created directory files `text_classification_1.jsonl`,
//         `text_classification_2.jsonl`,...,`text_classification_N.jsonl`
//         will be created, where N may be 1, and depends on the
//         total number of inputs and annotations found.
//
//         Each .JSONL file will contain, per line, a JSON representation of a
//         proto that wraps input text file (or document) in
//         the text snippet (or document) proto and a list of
//         zero or more AnnotationPayload protos (called annotations), which
//         have classification detail populated. A single text file (or
//         document) will be listed only once with all its annotations, and its
//         annotations will never be split across files.
//
//         If prediction for any input file (or document) 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 input file followed by exactly one
//
// [`google.rpc.Status`](https:
// //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//         containing only `code` and `message`.
//
//  *  For Text Sentiment:
//         In the created directory files `text_sentiment_1.jsonl`,
//         `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl`
//         will be created, where N may be 1, and depends on the
//         total number of inputs and annotations found.
//
//         Each .JSONL file will contain, per line, a JSON representation of a
//         proto that wraps input text file (or document) in
//         the text snippet (or document) proto and a list of
//         zero or more AnnotationPayload protos (called annotations), which
//         have text_sentiment detail populated. A single text file (or
//         document) will be listed only once with all its annotations, and its
//         annotations will never be split across files.
//
//         If prediction for any input file (or document) 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 input file followed by exactly one
//
// [`google.rpc.Status`](https:
// //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//         containing only `code` and `message`.
//
//   *  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" (if specified), followed by input text snippet,
//           and 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.v1p1beta.BatchPredictOutputConfig.gcs_destination]
//         or
//
// [bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination]
//         is set (either is allowed).
//         Google Cloud Storage 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.v1p1beta.TablesModelMetadata.prediction_type]:
//             Each .csv file will contain a header, listing all columns'
//
// [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name]
//             given on input followed by M target column names in the format of
//
// "<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1p1beta.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.v1p1beta.TablesAnnotation.score].
//           For REGRESSION and FORECASTING
//
// [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]:
//             Each .csv file will contain a header, listing all columns'
//             [display_name-s][google.cloud.automl.v1p1beta.display_name]
//             given on input followed by the predicted target column with name
//             in the format of
//
// "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1p1beta.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.v1p1beta.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.v1p1beta.ColumnSpec.display_name]
//           followed by the target column with name in the format of
//
// "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1p1beta.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.v1p1beta.AnnotationPayload],
//           represented as STRUCT-s, containing
//           [TablesAnnotation][google.cloud.automl.v1p1beta.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.v1p1beta.TablesModelMetadata.target_column_spec]
//
// [display_name][google.cloud.automl.v1p1beta.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 {
	// The destination of the output.
	//
	// Types that are valid to be assigned to Destination:
	//	*BatchPredictOutputConfig_GcsDestination
	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_929966d18309cd53, []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"`
}

func (*BatchPredictOutputConfig_GcsDestination) 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
}

// XXX_OneofWrappers is for the internal use of the proto package.
func (*BatchPredictOutputConfig) XXX_OneofWrappers() []interface{} {
	return []interface{}{
		(*BatchPredictOutputConfig_GcsDestination)(nil),
	}
}

// Output configuration for ModelExport Action.
type ModelExportOutputConfig struct {
	// The destination of the output.
	//
	// Types that are valid to be assigned to Destination:
	//	*ModelExportOutputConfig_GcsDestination
	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", "tf_js",
	//      "docker".
	//
	// *  For Image Classification mobile-core-ml-low-latency-1,
	//        mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1:
	//      "core_ml" (default).
	//
	// *  For Image Object Detection mobile-low-latency-1, mobile-versatile-1,
	//        mobile-high-accuracy-1:
	//      "tflite", "tf_saved_model", "tf_js".
	// 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.
	// * tf_js - A [TensorFlow.js](https://www.tensorflow.org/js) model that can
	//           be used in the browser and in Node.js using JavaScript.
	// * 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_929966d18309cd53, []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"`
}

func (*ModelExportOutputConfig_GcsDestination) 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) 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),
	}
}

// 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_929966d18309cd53, []int{6}
}

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 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_929966d18309cd53, []int{7}
}

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 ""
}

func init() {
	proto.RegisterType((*InputConfig)(nil), "google.cloud.automl.v1.InputConfig")
	proto.RegisterMapType((map[string]string)(nil), "google.cloud.automl.v1.InputConfig.ParamsEntry")
	proto.RegisterType((*BatchPredictInputConfig)(nil), "google.cloud.automl.v1.BatchPredictInputConfig")
	proto.RegisterType((*DocumentInputConfig)(nil), "google.cloud.automl.v1.DocumentInputConfig")
	proto.RegisterType((*OutputConfig)(nil), "google.cloud.automl.v1.OutputConfig")
	proto.RegisterType((*BatchPredictOutputConfig)(nil), "google.cloud.automl.v1.BatchPredictOutputConfig")
	proto.RegisterType((*ModelExportOutputConfig)(nil), "google.cloud.automl.v1.ModelExportOutputConfig")
	proto.RegisterMapType((map[string]string)(nil), "google.cloud.automl.v1.ModelExportOutputConfig.ParamsEntry")
	proto.RegisterType((*GcsSource)(nil), "google.cloud.automl.v1.GcsSource")
	proto.RegisterType((*GcsDestination)(nil), "google.cloud.automl.v1.GcsDestination")
}

func init() {
	proto.RegisterFile("google/cloud/automl/v1/io.proto", fileDescriptor_929966d18309cd53)
}

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