// Copyright 2024 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.

// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// 	protoc-gen-go v1.35.2
// 	protoc        v4.25.3
// source: google/cloud/automl/v1/io.proto

package automlpb

import (
	reflect "reflect"
	sync "sync"

	_ "google.golang.org/genproto/googleapis/api/annotations"
	protoreflect "google.golang.org/protobuf/reflect/protoreflect"
	protoimpl "google.golang.org/protobuf/runtime/protoimpl"
)

const (
	// Verify that this generated code is sufficiently up-to-date.
	_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
	// Verify that runtime/protoimpl is sufficiently up-to-date.
	_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)

// 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:
//
// #### AutoML Vision
//
// ##### Classification
//
// 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
//
// ##### Object Detection
// 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>
//
// #### AutoML Video Intelligence
//
// ##### Classification
//
// 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,,,
//
// ##### Object Tracking
//
// 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,,,,,,,,,,,
//
// #### AutoML Natural Language
//
// ##### Entity Extraction
//
// 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}
//	            }
//	          },
//	        ],
//
// ##### Classification
//
// 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
//
// ##### Sentiment Analysis
//
// 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
//
// #### AutoML Tables
//
// 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.
//
// **Input field definitions:**
//
// `ML_USE`
// : ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED")
//
//	Describes how the given example (file) should be used for model
//	training. "UNASSIGNED" can be used when user has no preference.
//
// `GCS_FILE_PATH`
// : The path to a file on Google Cloud Storage. For example,
//
//	"gs://folder/image1.png".
//
// `LABEL`
// : A display name of an object on an image, video etc., e.g. "dog".
//
//	Must be up to 32 characters long and can consist only of ASCII
//	Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
//	For each label an AnnotationSpec is created which display_name
//	becomes the label; AnnotationSpecs are given back in predictions.
//
// `INSTANCE_ID`
// : A positive integer that identifies a specific instance of a
//
//	labeled entity on an example. Used e.g. to track two cars on
//	a video while being able to tell apart which one is which.
//
// `BOUNDING_BOX`
// : (`VERTEX,VERTEX,VERTEX,VERTEX` | `VERTEX,,,VERTEX,,`)
//
//	A rectangle parallel to the frame of the example (image,
//	video). If 4 vertices are given they are connected by edges
//	in the order provided, if 2 are given they are recognized
//	as diagonally opposite vertices of the rectangle.
//
// `VERTEX`
// : (`COORDINATE,COORDINATE`)
//
//	First coordinate is horizontal (x), the second is vertical (y).
//
// `COORDINATE`
// : A float in 0 to 1 range, relative to total length of
//
//	image or video in given dimension. For fractions the
//	leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
//	Point 0,0 is in top left.
//
// `TIME_SEGMENT_START`
// : (`TIME_OFFSET`)
//
//	Expresses a beginning, inclusive, of a time segment
//	within an example that has a time dimension
//	(e.g. video).
//
// `TIME_SEGMENT_END`
// : (`TIME_OFFSET`)
//
//	Expresses an end, exclusive, of a time segment within
//	n example that has a time dimension (e.g. video).
//
// `TIME_OFFSET`
// : A number of seconds as measured from the start of an
//
//	example (e.g. video). Fractions are allowed, up to a
//	microsecond precision. "inf" is allowed, and it means the end
//	of the example.
//
// `TEXT_SNIPPET`
// : The content of a text snippet, UTF-8 encoded, enclosed within
//
//	double quotes ("").
//
// `DOCUMENT`
// : A field that provides the textual content with document and the layout
//
//	 information.
//
//
//	**Errors:**
//
//	If any of the provided CSV files can't be parsed or if more than certain
//	percent of CSV rows cannot be processed then the operation fails and
//	nothing is imported. Regardless of overall success or failure the per-row
//	failures, up to a certain count cap, is listed in
//	Operation.metadata.partial_failures.
type InputConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The source of the input.
	//
	// Types that are assignable to Source:
	//
	//	*InputConfig_GcsSource
	Source isInputConfig_Source `protobuf_oneof:"source"`
	// Additional domain-specific parameters describing the semantic of the
	// imported data, any string must be up to 25000
	// characters long.
	//
	// #### AutoML Tables
	//
	// `schema_inference_version`
	// : (integer) This value must be supplied.
	//
	//	The version of the
	//	algorithm to use for the initial inference of the
	//	column data types of the imported table. Allowed values: "1".
	Params map[string]string `protobuf:"bytes,2,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
}

func (x *InputConfig) Reset() {
	*x = InputConfig{}
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[0]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *InputConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*InputConfig) ProtoMessage() {}

func (x *InputConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[0]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use InputConfig.ProtoReflect.Descriptor instead.
func (*InputConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{0}
}

func (m *InputConfig) GetSource() isInputConfig_Source {
	if m != nil {
		return m.Source
	}
	return nil
}

func (x *InputConfig) GetGcsSource() *GcsSource {
	if x, ok := x.GetSource().(*InputConfig_GcsSource); ok {
		return x.GcsSource
	}
	return nil
}

func (x *InputConfig) GetParams() map[string]string {
	if x != nil {
		return x.Params
	}
	return nil
}

type isInputConfig_Source interface {
	isInputConfig_Source()
}

type InputConfig_GcsSource struct {
	// 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 *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

func (*InputConfig_GcsSource) isInputConfig_Source() {}

// 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:
//
// #### AutoML Vision
// ##### Classification
//
// 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
//
// ##### Object Detection
//
// 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
//
// #### AutoML Video Intelligence
// ##### Classification
//
// 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
//
// ##### Object Tracking
//
// 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
//
// #### AutoML Natural Language
// ##### Classification
//
// 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
//
// ##### Sentiment Analysis
// 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
//
// ##### Entity Extraction
//
// 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" ]
//	      }
//	    }
//	  }
//	}
//
// #### AutoML Tables
//
// 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.
//
// **Input field definitions:**
//
// `GCS_FILE_PATH`
// : The path to a file on Google Cloud Storage. For example,
//
//	"gs://folder/video.avi".
//
// `TIME_SEGMENT_START`
// : (`TIME_OFFSET`)
//
//	Expresses a beginning, inclusive, of a time segment
//	within an example that has a time dimension
//	(e.g. video).
//
// `TIME_SEGMENT_END`
// : (`TIME_OFFSET`)
//
//	Expresses an end, exclusive, of a time segment within
//	n example that has a time dimension (e.g. video).
//
// `TIME_OFFSET`
// : A number of seconds as measured from the start of an
//
//	 example (e.g. video). Fractions are allowed, up to a
//	 microsecond precision. "inf" is allowed, and it means the end
//	 of the example.
//
//	**Errors:**
//
//	If any of the provided CSV files can't be parsed or if more than certain
//	percent of CSV rows cannot be processed then the operation fails and
//	prediction does not happen. Regardless of overall success or failure the
//	per-row failures, up to a certain count cap, will be listed in
//	Operation.metadata.partial_failures.
type BatchPredictInputConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The source of the input.
	//
	// Types that are assignable to Source:
	//
	//	*BatchPredictInputConfig_GcsSource
	Source isBatchPredictInputConfig_Source `protobuf_oneof:"source"`
}

func (x *BatchPredictInputConfig) Reset() {
	*x = BatchPredictInputConfig{}
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[1]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *BatchPredictInputConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*BatchPredictInputConfig) ProtoMessage() {}

func (x *BatchPredictInputConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[1]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use BatchPredictInputConfig.ProtoReflect.Descriptor instead.
func (*BatchPredictInputConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{1}
}

func (m *BatchPredictInputConfig) GetSource() isBatchPredictInputConfig_Source {
	if m != nil {
		return m.Source
	}
	return nil
}

func (x *BatchPredictInputConfig) GetGcsSource() *GcsSource {
	if x, ok := x.GetSource().(*BatchPredictInputConfig_GcsSource); ok {
		return x.GcsSource
	}
	return nil
}

type isBatchPredictInputConfig_Source interface {
	isBatchPredictInputConfig_Source()
}

type BatchPredictInputConfig_GcsSource struct {
	// Required. The Google Cloud Storage location for the input content.
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3,oneof"`
}

func (*BatchPredictInputConfig_GcsSource) isBatchPredictInputConfig_Source() {}

// Input configuration of a [Document][google.cloud.automl.v1.Document].
type DocumentInputConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The Google Cloud Storage location of the document file. Only a single path
	// should be given.
	//
	// Max supported size: 512MB.
	//
	// Supported extensions: .PDF.
	GcsSource *GcsSource `protobuf:"bytes,1,opt,name=gcs_source,json=gcsSource,proto3" json:"gcs_source,omitempty"`
}

func (x *DocumentInputConfig) Reset() {
	*x = DocumentInputConfig{}
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[2]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *DocumentInputConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*DocumentInputConfig) ProtoMessage() {}

func (x *DocumentInputConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[2]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use DocumentInputConfig.ProtoReflect.Descriptor instead.
func (*DocumentInputConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{2}
}

func (x *DocumentInputConfig) GetGcsSource() *GcsSource {
	if x != nil {
		return x.GcsSource
	}
	return nil
}

//   - For Translation:
//     CSV file `translation.csv`, with each line in format:
//     ML_USE,GCS_FILE_PATH
//     GCS_FILE_PATH leads to a .TSV file which describes examples that have
//     given ML_USE, using the following row format per line:
//     TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target
//     language)
//
//   - For Tables:
//     Output depends on whether the dataset was imported from Google Cloud
//     Storage or BigQuery.
//     Google Cloud Storage case:
//     [gcs_destination][google.cloud.automl.v1p1beta.OutputConfig.gcs_destination]
//     must be set. Exported are CSV file(s) `tables_1.csv`,
//     `tables_2.csv`,...,`tables_N.csv` with each having as header line
//     the table's column names, and all other lines contain values for
//     the header columns.
//     BigQuery case:
//     [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination]
//     pointing to a BigQuery project must be set. In the given project a
//     new dataset will be created with name
//     `export_data_<automl-dataset-display-name>_<timestamp-of-export-call>`
//     where <automl-dataset-display-name> will be made
//     BigQuery-dataset-name compatible (e.g. most special characters will
//     become underscores), and timestamp will be in
//     YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that
//     dataset a new table called `primary_table` will be created, and
//     filled with precisely the same data as this obtained on import.
type OutputConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The destination of the output.
	//
	// Types that are assignable to Destination:
	//
	//	*OutputConfig_GcsDestination
	Destination isOutputConfig_Destination `protobuf_oneof:"destination"`
}

func (x *OutputConfig) Reset() {
	*x = OutputConfig{}
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[3]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *OutputConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*OutputConfig) ProtoMessage() {}

func (x *OutputConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[3]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use OutputConfig.ProtoReflect.Descriptor instead.
func (*OutputConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{3}
}

func (m *OutputConfig) GetDestination() isOutputConfig_Destination {
	if m != nil {
		return m.Destination
	}
	return nil
}

func (x *OutputConfig) GetGcsDestination() *GcsDestination {
	if x, ok := x.GetDestination().(*OutputConfig_GcsDestination); ok {
		return x.GcsDestination
	}
	return nil
}

type isOutputConfig_Destination interface {
	isOutputConfig_Destination()
}

type OutputConfig_GcsDestination struct {
	// 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 *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

func (*OutputConfig_GcsDestination) isOutputConfig_Destination() {}

// Output configuration for BatchPredict Action.
//
// As destination the
// [gcs_destination][google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination]
// must be set unless specified otherwise for a domain. If gcs_destination is
// set then in the given directory a new directory is created. Its name
// will be
// "prediction-<model-display-name>-<timestamp-of-prediction-call>",
// where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents
// of it depends on the ML problem the predictions are made for.
//
//   - For Image Classification:
//     In the created directory files `image_classification_1.jsonl`,
//     `image_classification_2.jsonl`,...,`image_classification_N.jsonl`
//     will be created, where N may be 1, and depends on the
//     total number of the successfully predicted images and annotations.
//     A single image will be listed only once with all its annotations,
//     and its annotations will never be split across files.
//     Each .JSONL file will contain, per line, a JSON representation of a
//     proto that wraps image's "ID" : "<id_value>" followed by a list of
//     zero or more AnnotationPayload protos (called annotations), which
//     have classification detail populated.
//     If prediction for any image failed (partially or completely), then an
//     additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
//     files will be created (N depends on total number of failed
//     predictions). These files will have a JSON representation of a proto
//     that wraps the same "ID" : "<id_value>" but here followed by
//     exactly one
//     [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//     containing only `code` and `message`fields.
//
//   - For Image Object Detection:
//     In the created directory files `image_object_detection_1.jsonl`,
//     `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl`
//     will be created, where N may be 1, and depends on the
//     total number of the successfully predicted images and annotations.
//     Each .JSONL file will contain, per line, a JSON representation of a
//     proto that wraps image's "ID" : "<id_value>" followed by a list of
//     zero or more AnnotationPayload protos (called annotations), which
//     have image_object_detection detail populated. A single image will
//     be listed only once with all its annotations, and its annotations
//     will never be split across files.
//     If prediction for any image failed (partially or completely), then
//     additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
//     files will be created (N depends on total number of failed
//     predictions). These files will have a JSON representation of a proto
//     that wraps the same "ID" : "<id_value>" but here followed by
//     exactly one
//     [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//     containing only `code` and `message`fields.
//
//   - For Video Classification:
//     In the created directory a video_classification.csv file, and a .JSON
//     file per each video classification requested in the input (i.e. each
//     line in given CSV(s)), will be created.
//
//     The format of video_classification.csv is:
//     GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
//     where:
//     GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
//     the prediction input lines (i.e. video_classification.csv has
//     precisely the same number of lines as the prediction input had.)
//     JSON_FILE_NAME = Name of .JSON file in the output directory, which
//     contains prediction responses for the video time segment.
//     STATUS = "OK" if prediction completed successfully, or an error code
//     with message otherwise. If STATUS is not "OK" then the .JSON file
//     for that line may not exist or be empty.
//
//     Each .JSON file, assuming STATUS is "OK", will contain a list of
//     AnnotationPayload protos in JSON format, which are the predictions
//     for the video time segment the file is assigned to in the
//     video_classification.csv. All AnnotationPayload protos will have
//     video_classification field set, and will be sorted by
//     video_classification.type field (note that the returned types are
//     governed by `classifaction_types` parameter in
//     [PredictService.BatchPredictRequest.params][]).
//
//   - For Video Object Tracking:
//     In the created directory a video_object_tracking.csv file will be
//     created, and multiple files video_object_trackinng_1.json,
//     video_object_trackinng_2.json,..., video_object_trackinng_N.json,
//     where N is the number of requests in the input (i.e. the number of
//     lines in given CSV(s)).
//
//     The format of video_object_tracking.csv is:
//     GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
//     where:
//     GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
//     the prediction input lines (i.e. video_object_tracking.csv has
//     precisely the same number of lines as the prediction input had.)
//     JSON_FILE_NAME = Name of .JSON file in the output directory, which
//     contains prediction responses for the video time segment.
//     STATUS = "OK" if prediction completed successfully, or an error
//     code with message otherwise. If STATUS is not "OK" then the .JSON
//     file for that line may not exist or be empty.
//
//     Each .JSON file, assuming STATUS is "OK", will contain a list of
//     AnnotationPayload protos in JSON format, which are the predictions
//     for each frame of the video time segment the file is assigned to in
//     video_object_tracking.csv. All AnnotationPayload protos will have
//     video_object_tracking field set.
//
//   - For Text Classification:
//     In the created directory files `text_classification_1.jsonl`,
//     `text_classification_2.jsonl`,...,`text_classification_N.jsonl`
//     will be created, where N may be 1, and depends on the
//     total number of inputs and annotations found.
//
//     Each .JSONL file will contain, per line, a JSON representation of a
//     proto that wraps input text file (or document) in
//     the text snippet (or document) proto and a list of
//     zero or more AnnotationPayload protos (called annotations), which
//     have classification detail populated. A single text file (or
//     document) will be listed only once with all its annotations, and its
//     annotations will never be split across files.
//
//     If prediction for any input file (or document) failed (partially or
//     completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
//     `errors_N.jsonl` files will be created (N depends on total number of
//     failed predictions). These files will have a JSON representation of a
//     proto that wraps input file followed by exactly one
//     [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//     containing only `code` and `message`.
//
//   - For Text Sentiment:
//     In the created directory files `text_sentiment_1.jsonl`,
//     `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl`
//     will be created, where N may be 1, and depends on the
//     total number of inputs and annotations found.
//
//     Each .JSONL file will contain, per line, a JSON representation of a
//     proto that wraps input text file (or document) in
//     the text snippet (or document) proto and a list of
//     zero or more AnnotationPayload protos (called annotations), which
//     have text_sentiment detail populated. A single text file (or
//     document) will be listed only once with all its annotations, and its
//     annotations will never be split across files.
//
//     If prediction for any input file (or document) failed (partially or
//     completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
//     `errors_N.jsonl` files will be created (N depends on total number of
//     failed predictions). These files will have a JSON representation of a
//     proto that wraps input file followed by exactly one
//     [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//     containing only `code` and `message`.
//
//   - For Text Extraction:
//     In the created directory files `text_extraction_1.jsonl`,
//     `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl`
//     will be created, where N may be 1, and depends on the
//     total number of inputs and annotations found.
//     The contents of these .JSONL file(s) depend on whether the input
//     used inline text, or documents.
//     If input was inline, then each .JSONL file will contain, per line,
//     a JSON representation of a proto that wraps given in request text
//     snippet's "id" (if specified), followed by input text snippet,
//     and a list of zero or more
//     AnnotationPayload protos (called annotations), which have
//     text_extraction detail populated. A single text snippet will be
//     listed only once with all its annotations, and its annotations will
//     never be split across files.
//     If input used documents, then each .JSONL file will contain, per
//     line, a JSON representation of a proto that wraps given in request
//     document proto, followed by its OCR-ed representation in the form
//     of a text snippet, finally followed by a list of zero or more
//     AnnotationPayload protos (called annotations), which have
//     text_extraction detail populated and refer, via their indices, to
//     the OCR-ed text snippet. A single document (and its text snippet)
//     will be listed only once with all its annotations, and its
//     annotations will never be split across files.
//     If prediction for any text snippet failed (partially or completely),
//     then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
//     `errors_N.jsonl` files will be created (N depends on total number of
//     failed predictions). These files will have a JSON representation of a
//     proto that wraps either the "id" : "<id_value>" (in case of inline)
//     or the document proto (in case of document) but here followed by
//     exactly one
//     [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//     containing only `code` and `message`.
//
//   - For Tables:
//     Output depends on whether
//     [gcs_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.gcs_destination]
//     or
//     [bigquery_destination][google.cloud.automl.v1p1beta.BatchPredictOutputConfig.bigquery_destination]
//     is set (either is allowed).
//     Google Cloud Storage case:
//     In the created directory files `tables_1.csv`, `tables_2.csv`,...,
//     `tables_N.csv` will be created, where N may be 1, and depends on
//     the total number of the successfully predicted rows.
//     For all CLASSIFICATION
//     [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]:
//     Each .csv file will contain a header, listing all columns'
//     [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name]
//     given on input followed by M target column names in the format of
//     "<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]
//     [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>_<target
//     value>_score" where M is the number of distinct target values,
//     i.e. number of distinct values in the target column of the table
//     used to train the model. Subsequent lines will contain the
//     respective values of successfully predicted rows, with the last,
//     i.e. the target, columns having the corresponding prediction
//     [scores][google.cloud.automl.v1p1beta.TablesAnnotation.score].
//     For REGRESSION and FORECASTING
//     [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]:
//     Each .csv file will contain a header, listing all columns'
//     [display_name-s][google.cloud.automl.v1p1beta.display_name]
//     given on input followed by the predicted target column with name
//     in the format of
//     "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]
//     [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>"
//     Subsequent lines will contain the respective values of
//     successfully predicted rows, with the last, i.e. the target,
//     column having the predicted target value.
//     If prediction for any rows failed, then an additional
//     `errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be
//     created (N depends on total number of failed rows). These files
//     will have analogous format as `tables_*.csv`, but always with a
//     single target column having
//     [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//     represented as a JSON string, and containing only `code` and
//     `message`.
//     BigQuery case:
//     [bigquery_destination][google.cloud.automl.v1p1beta.OutputConfig.bigquery_destination]
//     pointing to a BigQuery project must be set. In the given project a
//     new dataset will be created with name
//     `prediction_<model-display-name>_<timestamp-of-prediction-call>`
//     where <model-display-name> will be made
//     BigQuery-dataset-name compatible (e.g. most special characters will
//     become underscores), and timestamp will be in
//     YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset
//     two tables will be created, `predictions`, and `errors`.
//     The `predictions` table's column names will be the input columns'
//     [display_name-s][google.cloud.automl.v1p1beta.ColumnSpec.display_name]
//     followed by the target column with name in the format of
//     "predicted_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]
//     [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>"
//     The input feature columns will contain the respective values of
//     successfully predicted rows, with the target column having an
//     ARRAY of
//     [AnnotationPayloads][google.cloud.automl.v1p1beta.AnnotationPayload],
//     represented as STRUCT-s, containing
//     [TablesAnnotation][google.cloud.automl.v1p1beta.TablesAnnotation].
//     The `errors` table contains rows for which the prediction has
//     failed, it has analogous input columns while the target column name
//     is in the format of
//     "errors_<[target_column_specs][google.cloud.automl.v1p1beta.TablesModelMetadata.target_column_spec]
//     [display_name][google.cloud.automl.v1p1beta.ColumnSpec.display_name]>",
//     and as a value has
//     [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
//     represented as a STRUCT, and containing only `code` and `message`.
type BatchPredictOutputConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The destination of the output.
	//
	// Types that are assignable to Destination:
	//
	//	*BatchPredictOutputConfig_GcsDestination
	Destination isBatchPredictOutputConfig_Destination `protobuf_oneof:"destination"`
}

func (x *BatchPredictOutputConfig) Reset() {
	*x = BatchPredictOutputConfig{}
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[4]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *BatchPredictOutputConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*BatchPredictOutputConfig) ProtoMessage() {}

func (x *BatchPredictOutputConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[4]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use BatchPredictOutputConfig.ProtoReflect.Descriptor instead.
func (*BatchPredictOutputConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{4}
}

func (m *BatchPredictOutputConfig) GetDestination() isBatchPredictOutputConfig_Destination {
	if m != nil {
		return m.Destination
	}
	return nil
}

func (x *BatchPredictOutputConfig) GetGcsDestination() *GcsDestination {
	if x, ok := x.GetDestination().(*BatchPredictOutputConfig_GcsDestination); ok {
		return x.GcsDestination
	}
	return nil
}

type isBatchPredictOutputConfig_Destination interface {
	isBatchPredictOutputConfig_Destination()
}

type BatchPredictOutputConfig_GcsDestination struct {
	// Required. The Google Cloud Storage location of the directory where the
	// output is to be written to.
	GcsDestination *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

func (*BatchPredictOutputConfig_GcsDestination) isBatchPredictOutputConfig_Destination() {}

// Output configuration for ModelExport Action.
type ModelExportOutputConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The destination of the output.
	//
	// Types that are assignable to Destination:
	//
	//	*ModelExportOutputConfig_GcsDestination
	Destination isModelExportOutputConfig_Destination `protobuf_oneof:"destination"`
	// The format in which the model must be exported. The available, and default,
	// formats depend on the problem and model type (if given problem and type
	// combination doesn't have a format listed, it means its models are not
	// exportable):
	//
	//   - For Image Classification mobile-low-latency-1, mobile-versatile-1,
	//     mobile-high-accuracy-1:
	//     "tflite" (default), "edgetpu_tflite", "tf_saved_model", "tf_js",
	//     "docker".
	//
	//   - For Image Classification mobile-core-ml-low-latency-1,
	//     mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1:
	//     "core_ml" (default).
	//
	//   - For Image Object Detection mobile-low-latency-1, mobile-versatile-1,
	//     mobile-high-accuracy-1:
	//     "tflite", "tf_saved_model", "tf_js".
	//
	// Formats description:
	//
	//   - tflite - Used for Android mobile devices.
	//   - edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/)
	//     devices.
	//   - tf_saved_model - A tensorflow model in SavedModel format.
	//   - tf_js - A [TensorFlow.js](https://www.tensorflow.org/js) model that can
	//     be used in the browser and in Node.js using JavaScript.
	//   - docker - Used for Docker containers. Use the params field to customize
	//     the container. The container is verified to work correctly on
	//     ubuntu 16.04 operating system. See more at
	//     [containers
	//     quickstart](https://cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
	//   - core_ml - Used for iOS mobile devices.
	ModelFormat string `protobuf:"bytes,4,opt,name=model_format,json=modelFormat,proto3" json:"model_format,omitempty"`
	// Additional model-type and format specific parameters describing the
	// requirements for the to be exported model files, any string must be up to
	// 25000 characters long.
	//
	//   - For `docker` format:
	//     `cpu_architecture` - (string) "x86_64" (default).
	//     `gpu_architecture` - (string) "none" (default), "nvidia".
	Params map[string]string `protobuf:"bytes,2,rep,name=params,proto3" json:"params,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
}

func (x *ModelExportOutputConfig) Reset() {
	*x = ModelExportOutputConfig{}
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[5]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *ModelExportOutputConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelExportOutputConfig) ProtoMessage() {}

func (x *ModelExportOutputConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[5]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelExportOutputConfig.ProtoReflect.Descriptor instead.
func (*ModelExportOutputConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{5}
}

func (m *ModelExportOutputConfig) GetDestination() isModelExportOutputConfig_Destination {
	if m != nil {
		return m.Destination
	}
	return nil
}

func (x *ModelExportOutputConfig) GetGcsDestination() *GcsDestination {
	if x, ok := x.GetDestination().(*ModelExportOutputConfig_GcsDestination); ok {
		return x.GcsDestination
	}
	return nil
}

func (x *ModelExportOutputConfig) GetModelFormat() string {
	if x != nil {
		return x.ModelFormat
	}
	return ""
}

func (x *ModelExportOutputConfig) GetParams() map[string]string {
	if x != nil {
		return x.Params
	}
	return nil
}

type isModelExportOutputConfig_Destination interface {
	isModelExportOutputConfig_Destination()
}

type ModelExportOutputConfig_GcsDestination struct {
	// Required. 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 *GcsDestination `protobuf:"bytes,1,opt,name=gcs_destination,json=gcsDestination,proto3,oneof"`
}

func (*ModelExportOutputConfig_GcsDestination) isModelExportOutputConfig_Destination() {}

// The Google Cloud Storage location for the input content.
type GcsSource struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Required. Google Cloud Storage URIs to input files, up to 2000
	// characters long. Accepted forms:
	// * Full object path, e.g. gs://bucket/directory/object.csv
	InputUris []string `protobuf:"bytes,1,rep,name=input_uris,json=inputUris,proto3" json:"input_uris,omitempty"`
}

func (x *GcsSource) Reset() {
	*x = GcsSource{}
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[6]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *GcsSource) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*GcsSource) ProtoMessage() {}

func (x *GcsSource) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[6]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use GcsSource.ProtoReflect.Descriptor instead.
func (*GcsSource) Descriptor() ([]byte, []int) {
	return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{6}
}

func (x *GcsSource) GetInputUris() []string {
	if x != nil {
		return x.InputUris
	}
	return nil
}

// The Google Cloud Storage location where the output is to be written to.
type GcsDestination struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Required. Google Cloud Storage URI to output directory, up to 2000
	// characters long.
	// Accepted forms:
	// * Prefix path: gs://bucket/directory
	// The requesting user must have write permission to the bucket.
	// The directory is created if it doesn't exist.
	OutputUriPrefix string `protobuf:"bytes,1,opt,name=output_uri_prefix,json=outputUriPrefix,proto3" json:"output_uri_prefix,omitempty"`
}

func (x *GcsDestination) Reset() {
	*x = GcsDestination{}
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[7]
	ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
	ms.StoreMessageInfo(mi)
}

func (x *GcsDestination) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*GcsDestination) ProtoMessage() {}

func (x *GcsDestination) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_automl_v1_io_proto_msgTypes[7]
	if x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use GcsDestination.ProtoReflect.Descriptor instead.
func (*GcsDestination) Descriptor() ([]byte, []int) {
	return file_google_cloud_automl_v1_io_proto_rawDescGZIP(), []int{7}
}

func (x *GcsDestination) GetOutputUriPrefix() string {
	if x != nil {
		return x.OutputUriPrefix
	}
	return ""
}

var File_google_cloud_automl_v1_io_proto protoreflect.FileDescriptor

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}

var (
	file_google_cloud_automl_v1_io_proto_rawDescOnce sync.Once
	file_google_cloud_automl_v1_io_proto_rawDescData = file_google_cloud_automl_v1_io_proto_rawDesc
)

func file_google_cloud_automl_v1_io_proto_rawDescGZIP() []byte {
	file_google_cloud_automl_v1_io_proto_rawDescOnce.Do(func() {
		file_google_cloud_automl_v1_io_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_automl_v1_io_proto_rawDescData)
	})
	return file_google_cloud_automl_v1_io_proto_rawDescData
}

var file_google_cloud_automl_v1_io_proto_msgTypes = make([]protoimpl.MessageInfo, 10)
var file_google_cloud_automl_v1_io_proto_goTypes = []any{
	(*InputConfig)(nil),              // 0: google.cloud.automl.v1.InputConfig
	(*BatchPredictInputConfig)(nil),  // 1: google.cloud.automl.v1.BatchPredictInputConfig
	(*DocumentInputConfig)(nil),      // 2: google.cloud.automl.v1.DocumentInputConfig
	(*OutputConfig)(nil),             // 3: google.cloud.automl.v1.OutputConfig
	(*BatchPredictOutputConfig)(nil), // 4: google.cloud.automl.v1.BatchPredictOutputConfig
	(*ModelExportOutputConfig)(nil),  // 5: google.cloud.automl.v1.ModelExportOutputConfig
	(*GcsSource)(nil),                // 6: google.cloud.automl.v1.GcsSource
	(*GcsDestination)(nil),           // 7: google.cloud.automl.v1.GcsDestination
	nil,                              // 8: google.cloud.automl.v1.InputConfig.ParamsEntry
	nil,                              // 9: google.cloud.automl.v1.ModelExportOutputConfig.ParamsEntry
}
var file_google_cloud_automl_v1_io_proto_depIdxs = []int32{
	6, // 0: google.cloud.automl.v1.InputConfig.gcs_source:type_name -> google.cloud.automl.v1.GcsSource
	8, // 1: google.cloud.automl.v1.InputConfig.params:type_name -> google.cloud.automl.v1.InputConfig.ParamsEntry
	6, // 2: google.cloud.automl.v1.BatchPredictInputConfig.gcs_source:type_name -> google.cloud.automl.v1.GcsSource
	6, // 3: google.cloud.automl.v1.DocumentInputConfig.gcs_source:type_name -> google.cloud.automl.v1.GcsSource
	7, // 4: google.cloud.automl.v1.OutputConfig.gcs_destination:type_name -> google.cloud.automl.v1.GcsDestination
	7, // 5: google.cloud.automl.v1.BatchPredictOutputConfig.gcs_destination:type_name -> google.cloud.automl.v1.GcsDestination
	7, // 6: google.cloud.automl.v1.ModelExportOutputConfig.gcs_destination:type_name -> google.cloud.automl.v1.GcsDestination
	9, // 7: google.cloud.automl.v1.ModelExportOutputConfig.params:type_name -> google.cloud.automl.v1.ModelExportOutputConfig.ParamsEntry
	8, // [8:8] is the sub-list for method output_type
	8, // [8:8] is the sub-list for method input_type
	8, // [8:8] is the sub-list for extension type_name
	8, // [8:8] is the sub-list for extension extendee
	0, // [0:8] is the sub-list for field type_name
}

func init() { file_google_cloud_automl_v1_io_proto_init() }
func file_google_cloud_automl_v1_io_proto_init() {
	if File_google_cloud_automl_v1_io_proto != nil {
		return
	}
	file_google_cloud_automl_v1_io_proto_msgTypes[0].OneofWrappers = []any{
		(*InputConfig_GcsSource)(nil),
	}
	file_google_cloud_automl_v1_io_proto_msgTypes[1].OneofWrappers = []any{
		(*BatchPredictInputConfig_GcsSource)(nil),
	}
	file_google_cloud_automl_v1_io_proto_msgTypes[3].OneofWrappers = []any{
		(*OutputConfig_GcsDestination)(nil),
	}
	file_google_cloud_automl_v1_io_proto_msgTypes[4].OneofWrappers = []any{
		(*BatchPredictOutputConfig_GcsDestination)(nil),
	}
	file_google_cloud_automl_v1_io_proto_msgTypes[5].OneofWrappers = []any{
		(*ModelExportOutputConfig_GcsDestination)(nil),
	}
	type x struct{}
	out := protoimpl.TypeBuilder{
		File: protoimpl.DescBuilder{
			GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
			RawDescriptor: file_google_cloud_automl_v1_io_proto_rawDesc,
			NumEnums:      0,
			NumMessages:   10,
			NumExtensions: 0,
			NumServices:   0,
		},
		GoTypes:           file_google_cloud_automl_v1_io_proto_goTypes,
		DependencyIndexes: file_google_cloud_automl_v1_io_proto_depIdxs,
		MessageInfos:      file_google_cloud_automl_v1_io_proto_msgTypes,
	}.Build()
	File_google_cloud_automl_v1_io_proto = out.File
	file_google_cloud_automl_v1_io_proto_rawDesc = nil
	file_google_cloud_automl_v1_io_proto_goTypes = nil
	file_google_cloud_automl_v1_io_proto_depIdxs = nil
}
