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// Copyright 2021 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.v1;
import "google/api/field_behavior.proto";
option csharp_namespace = "Google.Cloud.AutoML.V1";
option go_package = "cloud.google.com/go/automl/apiv1/automlpb;automlpb";
option java_multiple_files = true;
option java_package = "com.google.cloud.automl.v1";
option php_namespace = "Google\\Cloud\\AutoMl\\V1";
option ruby_package = "Google::Cloud::AutoML::V1";
// 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.
//
message InputConfig {
// The source of the input.
oneof source {
// The Google Cloud Storage location for the input content.
// For [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData], `gcs_source` points to a CSV file with
// a structure described in [InputConfig][google.cloud.automl.v1.InputConfig].
GcsSource gcs_source = 1;
}
// 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".
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.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.
message BatchPredictInputConfig {
// The source of the input.
oneof source {
// Required. The Google Cloud Storage location for the input content.
GcsSource gcs_source = 1 [(google.api.field_behavior) = REQUIRED];
}
}
// Input configuration of a [Document][google.cloud.automl.v1.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 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.
message OutputConfig {
// The destination of the output.
oneof destination {
// Required. 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 [(google.api.field_behavior) = REQUIRED];
}
}
// Output configuration for BatchPredict Action.
//
// As destination the
// [gcs_destination][google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination]