grpc1.45.2/third_party/googleapis/google/cloud/automl/v1beta1/io.proto

// Copyright 2020 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

syntax = "proto3";

package google.cloud.automl.v1beta1;

import "google/api/annotations.proto";

option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1beta1";
option php_namespace = "Google\\Cloud\\AutoMl\\V1beta1";
option ruby_package = "Google::Cloud::AutoML::V1beta1";

// 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 (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:
//
//  *  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. For images which are known to not contain any
//           bounding boxes, they should be labelled explictly as
//           "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the
//           BOUNDING_BOX.
//         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,,,,,,,,,
//           TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,
//
//  *  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 (that is, JSON Lines) file which
//           either imports text in-line or as documents. Any given
//           .JSONL file must be 100MB or smaller.
//           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, for example, if you look
//           for animals and text contains "dolphin" that is not labeled, then
//           "dolphin" is assumed to not be an animal. Any given text snippet
//           content must be 10KB or smaller, and also be UTF-8 NFC encoded
//           (ASCII already is).
//           The document .JSONL file contains, per line, a proto that wraps a
//           Document proto. The Document proto must have either document_text
//           or input_config set. In document_text case, the Document proto may
//           also contain the spatial information of the document, including
//           layout, document dimension and page number. In input_config case,
//           only PDF documents are supported now, and each document may be up
//           to 2MB large. Currently, annotations on documents cannot be
//           specified at import.
//         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).:
//           {
//             "document": {
//               "document_text": {"content": "dog cat"}
//               "layout": [
//                 {
//                   "text_segment": {
//                     "start_offset": 0,
//                     "end_offset": 3,
//                   },
//                   "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,
//                 },
//                 {
//                   "text_segment": {
//                     "start_offset": 4,
//                     "end_offset": 7,
//                   },
//                   "page_number": 1,
//                   "bounding_poly": {
//                     "normalized_vertices": [
//                       {"x": 0.4, "y": 0.1},
//                       {"x": 0.4, "y": 0.3},
//                       {"x": 0.8, "y": 0.3},
//                       {"x": 0.8, "y": 0.1},
//                     ],
//                   },
//                   "text_segment_type": TOKEN,
//                 }
//
//               ],
//               "document_dimensions": {
//                 "width": 8.27,
//                 "height": 11.69,
//                 "unit": INCH,
//               }
//               "page_count": 1,
//             },
//             "annotations": [
//               {
//                 "display_name": "animal",
//                 "text_extraction": {"text_segment": {"start_offset": 0,
//                 "end_offset": 3}}
//               },
//               {
//                 "display_name": "animal",
//                 "text_extraction": {"text_segment": {"start_offset": 4,
//                 "end_offset": 7}}
//               }
//             ],
//           }\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 is
//           treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
//           must lead to a .txt file with UTF-8 encoding, for example,
//           "gs://folder/content.txt", and the content in it is extracted
//           as a text snippet. In TEXT_SNIPPET case, the column content
//           excluding quotes is 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.
//         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, that is, prefixed by
//           "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated
//           as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
//           must lead to a .txt file with UTF-8 encoding, for example,
//           "gs://folder/content.txt", and the content in it is extracted
//           as a text snippet. In TEXT_SNIPPET case, the column content itself
//           is treated as to be imported text snippet. In both cases, the
//           text snippet must be up to 500 characters long.
//         Sample rows:
//           TRAIN,"@freewrytin this is way too good for your product",2
//           TRAIN,"I need this product so bad",3
//           TEST,"Thank you for this product.",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 is 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, is listed in
//  Operation.metadata.partial_failures.
//
message InputConfig {
  // The source of the input.
  oneof source {
    // The Google Cloud Storage location for the input content.
    // In ImportData, the gcs_source points to a csv with structure described in
    // the comment.
    GcsSource gcs_source = 1;

    // The BigQuery location for the input content.
    BigQuerySource bigquery_source = 3;
  }

  // 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".
  map<string, string> params = 2;
}

// 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 Image Classification:
//         CSV file(s) with each line having just a single column:
//           GCS_FILE_PATH
//           which leads to image of up to 30MB in size. Supported
//           extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in
//           the Batch predict output.
//         Three sample rows:
//           gs://folder/image1.jpeg
//           gs://folder/image2.gif
//           gs://folder/image3.png
//
//  *  For Image Object Detection:
//         CSV file(s) with each line having just a single column:
//           GCS_FILE_PATH
//           which leads to image of up to 30MB in size. Supported
//           extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in
//           the Batch predict output.
//         Three sample rows:
//           gs://folder/image1.jpeg
//           gs://folder/image2.gif
//           gs://folder/image3.png
//  *  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 Classification:
//         CSV file(s) with each line having just a single column:
//           GCS_FILE_PATH | TEXT_SNIPPET
//         Any given text file can have size upto 128kB.
//         Any given text snippet content must have 60,000 characters or less.
//         Three sample rows:
//           gs://folder/text1.txt
//           "Some text content to predict"
//           gs://folder/text3.pdf
//         Supported file extensions: .txt, .pdf
//
//  *  For Text Sentiment:
//         CSV file(s) with each line having just a single column:
//           GCS_FILE_PATH | TEXT_SNIPPET
//         Any given text file can have size upto 128kB.
//         Any given text snippet content must have 500 characters or less.
//         Three sample rows:
//           gs://folder/text1.txt
//           "Some text content to predict"
//           gs://folder/text3.pdf
//         Supported file extensions: .txt, .pdf
//
//  * 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.
//           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. For FORECASTING
//
// [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
//           all columns having
//
// [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType]
//           type will be ignored.
//           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"}]}
//         BigQuery case:
//           An 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.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]:
//           all columns having
//
// [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType]
//           type will be ignored.
//
//  Definitions:
//  GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi".
//  TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
//                 double quotes ("")
//  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.
message BatchPredictInputConfig {
  // Required. The source of the input.
  oneof source {
    // The Google Cloud Storage location for the input content.
    GcsSource gcs_source = 1;

    // The BigQuery location for the input content.
    BigQuerySource bigquery_source = 2;
  }
}

// Input configuration of a [Document][google.cloud.automl.v1beta1.Document].
message DocumentInputConfig {
  // The Google Cloud Storage location of the document file. Only a single path
  // should be given.
  // Max supported size: 512MB.
  // Supported extensions: .PDF.
  GcsSource gcs_source = 1;
}

// *  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.
message OutputConfig {
  // Required. The destination of the output.
  oneof destination {
    // The Google Cloud Storage location where the output is to be written to.
    // For Image Object Detection, Text Extraction, Video Classification and
    // Tables, in the given directory a new directory will be created with name:
    // export_data-<dataset-display-name>-<timestamp-of-export-call> where
    // timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export
    // output will be written into that directory.
    GcsDestination gcs_destination = 1;

    // The BigQuery location where the output is to be written to.
    BigQueryDestination bigquery_destination = 2;
  }
}

// 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 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 snippet or input text file and a list of
//         zero or more AnnotationPayload protos (called annotations), which
//         have classification detail populated. A single text snippet or file
//         will be listed only once with all its annotations, and its
//         annotations will never be split across files.
//
//         If prediction for any text snippet or file 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 text snippet or input text 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 snippet or input text file and a list of
//         zero or more AnnotationPayload protos (called annotations), which
//         have text_sentiment detail populated. A single text snippet or file
//         will be listed only once with all its annotations, and its
//         annotations will never be split across files.
//
//         If prediction for any text snippet or file 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 text snippet or input text 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.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`.
message BatchPredictOutputConfig {
  // Required. The destination of the output.
  oneof destination {
    // The Google Cloud Storage location of the directory where the output is to
    // be written to.
    GcsDestination gcs_destination = 1;

    // The BigQuery location where the output is to be written to.
    BigQueryDestination bigquery_destination = 2;
  }
}

// Output configuration for ModelExport Action.
message ModelExportOutputConfig {
  // Required. The destination of the output.
  oneof destination {
    // The Google Cloud Storage location where the model is to be written to.
    // This location may only be set for the following model formats:
    //   "tflite", "edgetpu_tflite", "tf_saved_model", "tf_js", "core_ml".
    //
    //  Under the directory given as the destination a new one with name
    //  "model-export-<model-display-name>-<timestamp-of-export-call>",
    //  where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format,
    //  will be created. Inside the model and any of its supporting files
    //  will be written.
    GcsDestination gcs_destination = 1;

    // The GCR location where model image is to be pushed to. This location
    // may only be set for the following model formats:
    //   "docker".
    //
    // The model image will be created under the given URI.
    GcrDestination gcr_destination = 3;
  }

  // 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".
  //
  // *  For Video Classification cloud,
  //      "tf_saved_model".
  //
  // *  For Video Object Tracking cloud,
  //      "tf_saved_model".
  //
  // *  For Video Object Tracking mobile-versatile-1:
  //      "tflite", "edgetpu_tflite", "tf_saved_model", "docker".
  //
  // *  For Video Object Tracking mobile-coral-versatile-1:
  //      "tflite", "edgetpu_tflite", "docker".
  //
  // *  For Video Object Tracking mobile-coral-low-latency-1:
  //      "tflite", "edgetpu_tflite", "docker".
  //
  // *  For Video Object Tracking mobile-jetson-versatile-1:
  //      "tf_saved_model", "docker".
  //
  // *  For Tables:
  //      "docker".
  //
  // 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.
  string model_format = 4;

  // 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".
  map<string, string> params = 2;
}

// 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].
message ExportEvaluatedExamplesOutputConfig {
  // Required. The destination of the output.
  oneof destination {
    // The BigQuery location where the output is to be written to.
    BigQueryDestination bigquery_destination = 2;
  }
}

// The Google Cloud Storage location for the input content.
message GcsSource {
  // 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
  repeated string input_uris = 1;
}

// The BigQuery location for the input content.
message BigQuerySource {
  // Required. BigQuery URI to a table, up to 2000 characters long.
  // Accepted forms:
  // *  BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId
  string input_uri = 1;
}

// The Google Cloud Storage location where the output is to be written to.
message GcsDestination {
  // 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.
  string output_uri_prefix = 1;
}

// The BigQuery location for the output content.
message BigQueryDestination {
  // Required. BigQuery URI to a project, up to 2000 characters long.
  // Accepted forms:
  // *  BigQuery path e.g. bq://projectId
  string output_uri = 1;
}

// The GCR location where the image must be pushed to.
message GcrDestination {
  // 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.
  string output_uri = 1;
}