# frozen_string_literal: true

# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://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.

# Auto-generated by gapic-generator-ruby. DO NOT EDIT!


module Google
  module Cloud
    module AutoML
      module V1beta1
        # Input configuration for ImportData Action.
        #
        # The format of input depends on dataset_metadata the Dataset into which
        # the import is happening has. As input source the
        # {::Google::Cloud::AutoML::V1beta1::InputConfig#gcs_source gcs_source}
        # is expected, unless specified otherwise. Additionally any input .CSV file
        # by itself must be 100MB or smaller, unless specified otherwise.
        # If an "example" file (that is, image, video etc.) with identical content
        # (even if it had different GCS_FILE_PATH) is mentioned multiple times, then
        # its label, bounding boxes etc. are appended. The same file should be always
        # provided with the same ML_USE and GCS_FILE_PATH, if it is not, then
        # these values are nondeterministically selected from the given ones.
        #
        # The formats are represented in EBNF with commas being literal and with
        # non-terminal symbols defined near the end of this comment. The formats are:
        #
        #  *  For Image Classification:
        #         CSV file(s) with each line in format:
        #           ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
        #           GCS_FILE_PATH leads to image of up to 30MB in size. Supported
        #           extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO
        #           For MULTICLASS classification type, at most one LABEL is allowed
        #           per image. If an image has not yet been labeled, then it should be
        #           mentioned just once with no LABEL.
        #         Some sample rows:
        #           TRAIN,gs://folder/image1.jpg,daisy
        #           TEST,gs://folder/image2.jpg,dandelion,tulip,rose
        #           UNASSIGNED,gs://folder/image3.jpg,daisy
        #           UNASSIGNED,gs://folder/image4.jpg
        #
        #  *  For Image Object Detection:
        #         CSV file(s) with each line in format:
        #           ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,)
        #           GCS_FILE_PATH leads to image of up to 30MB in size. Supported
        #           extensions: .JPEG, .GIF, .PNG.
        #           Each image is assumed to be exhaustively labeled. The minimum
        #           allowed BOUNDING_BOX edge length is 0.01, and no more than 500
        #           BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined
        #           per line). If an image has not yet been labeled, then it should be
        #           mentioned just once with no LABEL and the ",,,,,,," in place of the
        #           BOUNDING_BOX. For images which are known to not contain any
        #           bounding boxes, they should be labelled explictly as
        #           "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the
        #           BOUNDING_BOX.
        #         Sample rows:
        #           TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,,
        #           TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,,
        #           UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3
        #           TEST,gs://folder/im3.png,,,,,,,,,
        #           TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,
        #
        #  *  For Video Classification:
        #         CSV file(s) with each line in format:
        #           ML_USE,GCS_FILE_PATH
        #           where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH
        #           should lead to another .csv file which describes examples that have
        #           given ML_USE, using the following row format:
        #           GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,)
        #           Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
        #           to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
        #           TIME_SEGMENT_START and TIME_SEGMENT_END must be within the
        #           length of the video, and end has to be after the start. Any segment
        #           of a video which has one or more labels on it, is considered a
        #           hard negative for all other labels. Any segment with no labels on
        #           it is considered to be unknown. If a whole video is unknown, then
        #           it shuold be mentioned just once with ",," in place of LABEL,
        #           TIME_SEGMENT_START,TIME_SEGMENT_END.
        #         Sample top level CSV file:
        #           TRAIN,gs://folder/train_videos.csv
        #           TEST,gs://folder/test_videos.csv
        #           UNASSIGNED,gs://folder/other_videos.csv
        #         Sample rows of a CSV file for a particular ML_USE:
        #           gs://folder/video1.avi,car,120,180.000021
        #           gs://folder/video1.avi,bike,150,180.000021
        #           gs://folder/vid2.avi,car,0,60.5
        #           gs://folder/vid3.avi,,,
        #
        #  *  For Video Object Tracking:
        #         CSV file(s) with each line in format:
        #           ML_USE,GCS_FILE_PATH
        #           where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH
        #           should lead to another .csv file which describes examples that have
        #           given ML_USE, using one of the following row format:
        #           GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX
        #           or
        #           GCS_FILE_PATH,,,,,,,,,,
        #           Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
        #           to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
        #           Providing INSTANCE_IDs can help to obtain a better model. When
        #           a specific labeled entity leaves the video frame, and shows up
        #           afterwards it is not required, albeit preferable, that the same
        #           INSTANCE_ID is given to it.
        #           TIMESTAMP must be within the length of the video, the
        #           BOUNDING_BOX is assumed to be drawn on the closest video's frame
        #           to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected
        #           to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per
        #           frame are allowed. If a whole video is unknown, then it should be
        #           mentioned just once with ",,,,,,,,,," in place of LABEL,
        #           [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX.
        #         Sample top level CSV file:
        #           TRAIN,gs://folder/train_videos.csv
        #           TEST,gs://folder/test_videos.csv
        #           UNASSIGNED,gs://folder/other_videos.csv
        #         Seven sample rows of a CSV file for a particular ML_USE:
        #           gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
        #           gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
        #           gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
        #           gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
        #           gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,,
        #           gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,,
        #           gs://folder/video2.avi,,,,,,,,,,,
        #  *  For Text Extraction:
        #         CSV file(s) with each line in format:
        #           ML_USE,GCS_FILE_PATH
        #           GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which
        #           either imports text in-line or as documents. Any given
        #           .JSONL file must be 100MB or smaller.
        #           The in-line .JSONL file contains, per line, a proto that wraps a
        #           TextSnippet proto (in json representation) followed by one or more
        #           AnnotationPayload protos (called annotations), which have
        #           display_name and text_extraction detail populated. The given text
        #           is expected to be annotated exhaustively, for example, if you look
        #           for animals and text contains "dolphin" that is not labeled, then
        #           "dolphin" is assumed to not be an animal. Any given text snippet
        #           content must be 10KB or smaller, and also be UTF-8 NFC encoded
        #           (ASCII already is).
        #           The document .JSONL file contains, per line, a proto that wraps a
        #           Document proto. The Document proto must have either document_text
        #           or input_config set. In document_text case, the Document proto may
        #           also contain the spatial information of the document, including
        #           layout, document dimension and page number. In input_config case,
        #           only PDF documents are supported now, and each document may be up
        #           to 2MB large. Currently, annotations on documents cannot be
        #           specified at import.
        #         Three sample CSV rows:
        #           TRAIN,gs://folder/file1.jsonl
        #           VALIDATE,gs://folder/file2.jsonl
        #           TEST,gs://folder/file3.jsonl
        #         Sample in-line JSON Lines file for entity extraction (presented here
        #         with artificial line breaks, but the only actual line break is
        #         denoted by \n).:
        #           {
        #             "document": {
        #               "document_text": \\{"content": "dog cat"}
        #               "layout": [
        #                 {
        #                   "text_segment": {
        #                     "start_offset": 0,
        #                     "end_offset": 3,
        #                   },
        #                   "page_number": 1,
        #                   "bounding_poly": {
        #                     "normalized_vertices": [
        #                       \\{"x": 0.1, "y": 0.1},
        #                       \\{"x": 0.1, "y": 0.3},
        #                       \\{"x": 0.3, "y": 0.3},
        #                       \\{"x": 0.3, "y": 0.1},
        #                     ],
        #                   },
        #                   "text_segment_type": TOKEN,
        #                 },
        #                 {
        #                   "text_segment": {
        #                     "start_offset": 4,
        #                     "end_offset": 7,
        #                   },
        #                   "page_number": 1,
        #                   "bounding_poly": {
        #                     "normalized_vertices": [
        #                       \\{"x": 0.4, "y": 0.1},
        #                       \\{"x": 0.4, "y": 0.3},
        #                       \\{"x": 0.8, "y": 0.3},
        #                       \\{"x": 0.8, "y": 0.1},
        #                     ],
        #                   },
        #                   "text_segment_type": TOKEN,
        #                 }
        #
        #               ],
        #               "document_dimensions": {
        #                 "width": 8.27,
        #                 "height": 11.69,
        #                 "unit": INCH,
        #               }
        #               "page_count": 1,
        #             },
        #             "annotations": [
        #               {
        #                 "display_name": "animal",
        #                 "text_extraction": {"text_segment": {"start_offset": 0,
        #                 "end_offset": 3}}
        #               },
        #               {
        #                 "display_name": "animal",
        #                 "text_extraction": {"text_segment": {"start_offset": 4,
        #                 "end_offset": 7}}
        #               }
        #             ],
        #           }\n
        #           {
        #              "text_snippet": {
        #                "content": "This dog is good."
        #              },
        #              "annotations": [
        #                {
        #                  "display_name": "animal",
        #                  "text_extraction": {
        #                    "text_segment": \\{"start_offset": 5, "end_offset": 8}
        #                  }
        #                }
        #              ]
        #           }
        #         Sample document JSON Lines file (presented here with artificial line
        #         breaks, but the only actual line break is denoted by \n).:
        #           {
        #             "document": {
        #               "input_config": {
        #                 "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
        #                 }
        #               }
        #             }
        #           }\n
        #           {
        #             "document": {
        #               "input_config": {
        #                 "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
        #                 }
        #               }
        #             }
        #           }
        #
        #  *  For Text Classification:
        #         CSV file(s) with each line in format:
        #           ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...
        #           TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
        #           the column content is a valid gcs file path, i.e. prefixed by
        #           "gs://", it will be treated as a GCS_FILE_PATH, else if the content
        #           is enclosed within double quotes (""), it is
        #           treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
        #           must lead to a .txt file with UTF-8 encoding, for example,
        #           "gs://folder/content.txt", and the content in it is extracted
        #           as a text snippet. In TEXT_SNIPPET case, the column content
        #           excluding quotes is treated as to be imported text snippet. In
        #           both cases, the text snippet/file size must be within 128kB.
        #           Maximum 100 unique labels are allowed per CSV row.
        #         Sample rows:
        #           TRAIN,"They have bad food and very rude",RudeService,BadFood
        #           TRAIN,gs://folder/content.txt,SlowService
        #           TEST,"Typically always bad service there.",RudeService
        #           VALIDATE,"Stomach ache to go.",BadFood
        #
        #  *  For Text Sentiment:
        #         CSV file(s) with each line in format:
        #           ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT
        #           TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
        #           the column content is a valid gcs file path, that is, prefixed by
        #           "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated
        #           as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path
        #           must lead to a .txt file with UTF-8 encoding, for example,
        #           "gs://folder/content.txt", and the content in it is extracted
        #           as a text snippet. In TEXT_SNIPPET case, the column content itself
        #           is treated as to be imported text snippet. In both cases, the
        #           text snippet must be up to 500 characters long.
        #         Sample rows:
        #           TRAIN,"@freewrytin this is way too good for your product",2
        #           TRAIN,"I need this product so bad",3
        #           TEST,"Thank you for this product.",4
        #           VALIDATE,gs://folder/content.txt,2
        #
        #   *  For Tables:
        #         Either
        #         {::Google::Cloud::AutoML::V1beta1::InputConfig#gcs_source gcs_source} or
        #
        # {::Google::Cloud::AutoML::V1beta1::InputConfig#bigquery_source bigquery_source}
        #         can be used. All inputs is concatenated into a single
        #
        # [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name]
        #         For gcs_source:
        #           CSV file(s), where the first row of the first file is the header,
        #           containing unique column names. If the first row of a subsequent
        #           file is the same as the header, then it is also treated as a
        #           header. All other rows contain values for the corresponding
        #           columns.
        #           Each .CSV file by itself must be 10GB or smaller, and their total
        #           size must be 100GB or smaller.
        #           First three sample rows of a CSV file:
        #           "Id","First Name","Last Name","Dob","Addresses"
        #
        # "1","John","Doe","1968-01-22","[\\{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},\\{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
        #
        # "2","Jane","Doe","1980-10-16","[\\{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},\\{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
        #         For bigquery_source:
        #           An URI of a BigQuery table. The user data size of the BigQuery
        #           table must be 100GB or smaller.
        #         An imported table must have between 2 and 1,000 columns, inclusive,
        #         and between 1000 and 100,000,000 rows, inclusive. There are at most 5
        #         import data running in parallel.
        #  Definitions:
        #  ML_USE = "TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED"
        #           Describes how the given example (file) should be used for model
        #           training. "UNASSIGNED" can be used when user has no preference.
        #  GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/image1.png".
        #  LABEL = A display name of an object on an image, video etc., e.g. "dog".
        #          Must be up to 32 characters long and can consist only of ASCII
        #          Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9.
        #          For each label an AnnotationSpec is created which display_name
        #          becomes the label; AnnotationSpecs are given back in predictions.
        #  INSTANCE_ID = A positive integer that identifies a specific instance of a
        #                labeled entity on an example. Used e.g. to track two cars on
        #                a video while being able to tell apart which one is which.
        #  BOUNDING_BOX = VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,
        #                 A rectangle parallel to the frame of the example (image,
        #                 video). If 4 vertices are given they are connected by edges
        #                 in the order provided, if 2 are given they are recognized
        #                 as diagonally opposite vertices of the rectangle.
        #  VERTEX = COORDINATE,COORDINATE
        #           First coordinate is horizontal (x), the second is vertical (y).
        #  COORDINATE = A float in 0 to 1 range, relative to total length of
        #               image or video in given dimension. For fractions the
        #               leading non-decimal 0 can be omitted (i.e. 0.3 = .3).
        #               Point 0,0 is in top left.
        #  TIME_SEGMENT_START = TIME_OFFSET
        #                       Expresses a beginning, inclusive, of a time segment
        #                       within an example that has a time dimension
        #                       (e.g. video).
        #  TIME_SEGMENT_END = TIME_OFFSET
        #                     Expresses an end, exclusive, of a time segment within
        #                     an example that has a time dimension (e.g. video).
        #  TIME_OFFSET = A number of seconds as measured from the start of an
        #                example (e.g. video). Fractions are allowed, up to a
        #                microsecond precision. "inf" is allowed, and it means the end
        #                of the example.
        #  TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
        #                 double quotes ("").
        #  SENTIMENT = An integer between 0 and
        #              Dataset.text_sentiment_dataset_metadata.sentiment_max
        #              (inclusive). Describes the ordinal of the sentiment - higher
        #              value means a more positive sentiment. All the values are
        #              completely relative, i.e. neither 0 needs to mean a negative or
        #              neutral sentiment nor sentiment_max needs to mean a positive one
        #              - it is just required that 0 is the least positive sentiment
        #              in the data, and sentiment_max is the  most positive one.
        #              The SENTIMENT shouldn't be confused with "score" or "magnitude"
        #              from the previous Natural Language Sentiment Analysis API.
        #              All SENTIMENT values between 0 and sentiment_max must be
        #              represented in the imported data. On prediction the same 0 to
        #              sentiment_max range will be used. The difference between
        #              neighboring sentiment values needs not to be uniform, e.g. 1 and
        #              2 may be similar whereas the difference between 2 and 3 may be
        #              huge.
        #
        #  Errors:
        #  If any of the provided CSV files can't be parsed or if more than certain
        #  percent of CSV rows cannot be processed then the operation fails and
        #  nothing is imported. Regardless of overall success or failure the per-row
        #  failures, up to a certain count cap, is listed in
        #  Operation.metadata.partial_failures.
        # @!attribute [rw] gcs_source
        #   @return [::Google::Cloud::AutoML::V1beta1::GcsSource]
        #     The Google Cloud Storage location for the input content.
        #     In ImportData, the gcs_source points to a csv with structure described in
        #     the comment.
        #
        #     Note: The following fields are mutually exclusive: `gcs_source`, `bigquery_source`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        # @!attribute [rw] bigquery_source
        #   @return [::Google::Cloud::AutoML::V1beta1::BigQuerySource]
        #     The BigQuery location for the input content.
        #
        #     Note: The following fields are mutually exclusive: `bigquery_source`, `gcs_source`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        # @!attribute [rw] params
        #   @return [::Google::Protobuf::Map{::String => ::String}]
        #     Additional domain-specific parameters describing the semantic of the
        #     imported data, any string must be up to 25000
        #     characters long.
        #
        #     *  For Tables:
        #        `schema_inference_version` - (integer) Required. The version of the
        #            algorithm that should be used for the initial inference of the
        #            schema (columns' DataTypes) of the table the data is being imported
        #            into. Allowed values: "1".
        class InputConfig
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods

          # @!attribute [rw] key
          #   @return [::String]
          # @!attribute [rw] value
          #   @return [::String]
          class ParamsEntry
            include ::Google::Protobuf::MessageExts
            extend ::Google::Protobuf::MessageExts::ClassMethods
          end
        end

        # Input configuration for BatchPredict Action.
        #
        # The format of input depends on the ML problem of the model used for
        # prediction. As input source the
        # {::Google::Cloud::AutoML::V1beta1::InputConfig#gcs_source gcs_source}
        # is expected, unless specified otherwise.
        #
        # The formats are represented in EBNF with commas being literal and with
        # non-terminal symbols defined near the end of this comment. The formats
        # are:
        #
        #  *  For Image Classification:
        #         CSV file(s) with each line having just a single column:
        #           GCS_FILE_PATH
        #           which leads to image of up to 30MB in size. Supported
        #           extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in
        #           the Batch predict output.
        #         Three sample rows:
        #           gs://folder/image1.jpeg
        #           gs://folder/image2.gif
        #           gs://folder/image3.png
        #
        #  *  For Image Object Detection:
        #         CSV file(s) with each line having just a single column:
        #           GCS_FILE_PATH
        #           which leads to image of up to 30MB in size. Supported
        #           extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in
        #           the Batch predict output.
        #         Three sample rows:
        #           gs://folder/image1.jpeg
        #           gs://folder/image2.gif
        #           gs://folder/image3.png
        #  *  For Video Classification:
        #         CSV file(s) with each line in format:
        #           GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
        #           GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h
        #           duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
        #           TIME_SEGMENT_START and TIME_SEGMENT_END must be within the
        #           length of the video, and end has to be after the start.
        #         Three sample rows:
        #           gs://folder/video1.mp4,10,40
        #           gs://folder/video1.mp4,20,60
        #           gs://folder/vid2.mov,0,inf
        #
        #  *  For Video Object Tracking:
        #         CSV file(s) with each line in format:
        #           GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
        #           GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h
        #           duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
        #           TIME_SEGMENT_START and TIME_SEGMENT_END must be within the
        #           length of the video, and end has to be after the start.
        #         Three sample rows:
        #           gs://folder/video1.mp4,10,240
        #           gs://folder/video1.mp4,300,360
        #           gs://folder/vid2.mov,0,inf
        #  *  For Text Classification:
        #         CSV file(s) with each line having just a single column:
        #           GCS_FILE_PATH | TEXT_SNIPPET
        #         Any given text file can have size upto 128kB.
        #         Any given text snippet content must have 60,000 characters or less.
        #         Three sample rows:
        #           gs://folder/text1.txt
        #           "Some text content to predict"
        #           gs://folder/text3.pdf
        #         Supported file extensions: .txt, .pdf
        #
        #  *  For Text Sentiment:
        #         CSV file(s) with each line having just a single column:
        #           GCS_FILE_PATH | TEXT_SNIPPET
        #         Any given text file can have size upto 128kB.
        #         Any given text snippet content must have 500 characters or less.
        #         Three sample rows:
        #           gs://folder/text1.txt
        #           "Some text content to predict"
        #           gs://folder/text3.pdf
        #         Supported file extensions: .txt, .pdf
        #
        #  * For Text Extraction
        #         .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or
        #         as documents (for a single BatchPredict call only one of the these
        #         formats may be used).
        #         The in-line .JSONL file(s) contain per line a proto that
        #           wraps a temporary user-assigned TextSnippet ID (string up to 2000
        #           characters long) called "id", a TextSnippet proto (in
        #           json representation) and zero or more TextFeature protos. Any given
        #           text snippet content must have 30,000 characters or less, and also
        #           be UTF-8 NFC encoded (ASCII already is). The IDs provided should be
        #           unique.
        #         The document .JSONL file(s) contain, per line, a proto that wraps a
        #           Document proto with input_config set. Only PDF documents are
        #           supported now, and each document must be up to 2MB large.
        #         Any given .JSONL file must be 100MB or smaller, and no more than 20
        #         files may be given.
        #         Sample in-line JSON Lines file (presented here with artificial line
        #         breaks, but the only actual line break is denoted by \n):
        #           {
        #             "id": "my_first_id",
        #             "text_snippet": { "content": "dog car cat"},
        #             "text_features": [
        #               {
        #                 "text_segment": \\{"start_offset": 4, "end_offset": 6},
        #                 "structural_type": PARAGRAPH,
        #                 "bounding_poly": {
        #                   "normalized_vertices": [
        #                     \\{"x": 0.1, "y": 0.1},
        #                     \\{"x": 0.1, "y": 0.3},
        #                     \\{"x": 0.3, "y": 0.3},
        #                     \\{"x": 0.3, "y": 0.1},
        #                   ]
        #                 },
        #               }
        #             ],
        #           }\n
        #           {
        #             "id": "2",
        #             "text_snippet": {
        #               "content": "An elaborate content",
        #               "mime_type": "text/plain"
        #             }
        #           }
        #         Sample document JSON Lines file (presented here with artificial line
        #         breaks, but the only actual line break is denoted by \n).:
        #           {
        #             "document": {
        #               "input_config": {
        #                 "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
        #                 }
        #               }
        #             }
        #           }\n
        #           {
        #             "document": {
        #               "input_config": {
        #                 "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
        #                 }
        #               }
        #             }
        #           }
        #
        #  *  For Tables:
        #         Either
        #         {::Google::Cloud::AutoML::V1beta1::InputConfig#gcs_source gcs_source} or
        #
        # {::Google::Cloud::AutoML::V1beta1::InputConfig#bigquery_source bigquery_source}.
        #         GCS case:
        #           CSV file(s), each by itself 10GB or smaller and total size must be
        #           100GB or smaller, where first file must have a header containing
        #           column names. If the first row of a subsequent file is the same as
        #           the header, then it is also treated as a header. All other rows
        #           contain values for the corresponding columns.
        #           The column names must contain the model's
        #
        # [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
        #
        # {::Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name-s}
        #           (order doesn't matter). The columns corresponding to the model's
        #           input feature column specs must contain values compatible with the
        #           column spec's data types. Prediction on all the rows, i.e. the CSV
        #           lines, will be attempted. For FORECASTING
        #
        # [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
        #           all columns having
        #
        # [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType]
        #           type will be ignored.
        #           First three sample rows of a CSV file:
        #             "First Name","Last Name","Dob","Addresses"
        #
        # "John","Doe","1968-01-22","[\\{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},\\{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
        #
        # "Jane","Doe","1980-10-16","[\\{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},\\{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
        #         BigQuery case:
        #           An URI of a BigQuery table. The user data size of the BigQuery
        #           table must be 100GB or smaller.
        #           The column names must contain the model's
        #
        # [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
        #
        # {::Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name-s}
        #           (order doesn't matter). The columns corresponding to the model's
        #           input feature column specs must contain values compatible with the
        #           column spec's data types. Prediction on all the rows of the table
        #           will be attempted. For FORECASTING
        #
        # [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
        #           all columns having
        #
        # [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType]
        #           type will be ignored.
        #
        #  Definitions:
        #  GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi".
        #  TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
        #                 double quotes ("")
        #  TIME_SEGMENT_START = TIME_OFFSET
        #                       Expresses a beginning, inclusive, of a time segment
        #                       within an
        #                       example that has a time dimension (e.g. video).
        #  TIME_SEGMENT_END = TIME_OFFSET
        #                     Expresses an end, exclusive, of a time segment within
        #                     an example that has a time dimension (e.g. video).
        #  TIME_OFFSET = A number of seconds as measured from the start of an
        #                example (e.g. video). Fractions are allowed, up to a
        #                microsecond precision. "inf" is allowed and it means the end
        #                of the example.
        #
        #  Errors:
        #  If any of the provided CSV files can't be parsed or if more than certain
        #  percent of CSV rows cannot be processed then the operation fails and
        #  prediction does not happen. Regardless of overall success or failure the
        #  per-row failures, up to a certain count cap, will be listed in
        #  Operation.metadata.partial_failures.
        # @!attribute [rw] gcs_source
        #   @return [::Google::Cloud::AutoML::V1beta1::GcsSource]
        #     The Google Cloud Storage location for the input content.
        #
        #     Note: The following fields are mutually exclusive: `gcs_source`, `bigquery_source`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        # @!attribute [rw] bigquery_source
        #   @return [::Google::Cloud::AutoML::V1beta1::BigQuerySource]
        #     The BigQuery location for the input content.
        #
        #     Note: The following fields are mutually exclusive: `bigquery_source`, `gcs_source`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        class BatchPredictInputConfig
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # Input configuration of a {::Google::Cloud::AutoML::V1beta1::Document Document}.
        # @!attribute [rw] gcs_source
        #   @return [::Google::Cloud::AutoML::V1beta1::GcsSource]
        #     The Google Cloud Storage location of the document file. Only a single path
        #     should be given.
        #     Max supported size: 512MB.
        #     Supported extensions: .PDF.
        class DocumentInputConfig
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # *  For Translation:
        #         CSV file `translation.csv`, with each line in format:
        #         ML_USE,GCS_FILE_PATH
        #         GCS_FILE_PATH leads to a .TSV file which describes examples that have
        #         given ML_USE, using the following row format per line:
        #         TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target
        #         language)
        #
        #   *  For Tables:
        #         Output depends on whether the dataset was imported from GCS or
        #         BigQuery.
        #         GCS case:
        #
        # {::Google::Cloud::AutoML::V1beta1::OutputConfig#gcs_destination 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:
        #
        # {::Google::Cloud::AutoML::V1beta1::OutputConfig#bigquery_destination 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.
        # @!attribute [rw] gcs_destination
        #   @return [::Google::Cloud::AutoML::V1beta1::GcsDestination]
        #     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.
        #
        #     Note: The following fields are mutually exclusive: `gcs_destination`, `bigquery_destination`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        # @!attribute [rw] bigquery_destination
        #   @return [::Google::Cloud::AutoML::V1beta1::BigQueryDestination]
        #     The BigQuery location where the output is to be written to.
        #
        #     Note: The following fields are mutually exclusive: `bigquery_destination`, `gcs_destination`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        class OutputConfig
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # Output configuration for BatchPredict Action.
        #
        # As destination the
        #
        # {::Google::Cloud::AutoML::V1beta1::BatchPredictOutputConfig#gcs_destination gcs_destination}
        # must be set unless specified otherwise for a domain. If gcs_destination is
        # set then in the given directory a new directory is created. Its name
        # will be
        # "prediction-<model-display-name>-<timestamp-of-prediction-call>",
        # where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents
        # of it depends on the ML problem the predictions are made for.
        #
        #  *  For Image Classification:
        #         In the created directory files `image_classification_1.jsonl`,
        #         `image_classification_2.jsonl`,...,`image_classification_N.jsonl`
        #         will be created, where N may be 1, and depends on the
        #         total number of the successfully predicted images and annotations.
        #         A single image will be listed only once with all its annotations,
        #         and its annotations will never be split across files.
        #         Each .JSONL file will contain, per line, a JSON representation of a
        #         proto that wraps image's "ID" : "<id_value>" followed by a list of
        #         zero or more AnnotationPayload protos (called annotations), which
        #         have classification detail populated.
        #         If prediction for any image failed (partially or completely), then an
        #         additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
        #         files will be created (N depends on total number of failed
        #         predictions). These files will have a JSON representation of a proto
        #         that wraps the same "ID" : "<id_value>" but here followed by
        #         exactly one
        #
        # [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
        #         containing only `code` and `message`fields.
        #
        #  *  For Image Object Detection:
        #         In the created directory files `image_object_detection_1.jsonl`,
        #         `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl`
        #         will be created, where N may be 1, and depends on the
        #         total number of the successfully predicted images and annotations.
        #         Each .JSONL file will contain, per line, a JSON representation of a
        #         proto that wraps image's "ID" : "<id_value>" followed by a list of
        #         zero or more AnnotationPayload protos (called annotations), which
        #         have image_object_detection detail populated. A single image will
        #         be listed only once with all its annotations, and its annotations
        #         will never be split across files.
        #         If prediction for any image failed (partially or completely), then
        #         additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl`
        #         files will be created (N depends on total number of failed
        #         predictions). These files will have a JSON representation of a proto
        #         that wraps the same "ID" : "<id_value>" but here followed by
        #         exactly one
        #
        # [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
        #         containing only `code` and `message`fields.
        #  *  For Video Classification:
        #         In the created directory a video_classification.csv file, and a .JSON
        #         file per each video classification requested in the input (i.e. each
        #         line in given CSV(s)), will be created.
        #
        #         The format of video_classification.csv is:
        #
        # GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
        #         where:
        #         GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
        #             the prediction input lines (i.e. video_classification.csv has
        #             precisely the same number of lines as the prediction input had.)
        #         JSON_FILE_NAME = Name of .JSON file in the output directory, which
        #             contains prediction responses for the video time segment.
        #         STATUS = "OK" if prediction completed successfully, or an error code
        #             with message otherwise. If STATUS is not "OK" then the .JSON file
        #             for that line may not exist or be empty.
        #
        #         Each .JSON file, assuming STATUS is "OK", will contain a list of
        #         AnnotationPayload protos in JSON format, which are the predictions
        #         for the video time segment the file is assigned to in the
        #         video_classification.csv. All AnnotationPayload protos will have
        #         video_classification field set, and will be sorted by
        #         video_classification.type field (note that the returned types are
        #         governed by `classifaction_types` parameter in
        #         [PredictService.BatchPredictRequest.params][]).
        #
        #  *  For Video Object Tracking:
        #         In the created directory a video_object_tracking.csv file will be
        #         created, and multiple files video_object_trackinng_1.json,
        #         video_object_trackinng_2.json,..., video_object_trackinng_N.json,
        #         where N is the number of requests in the input (i.e. the number of
        #         lines in given CSV(s)).
        #
        #         The format of video_object_tracking.csv is:
        #
        # GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
        #         where:
        #         GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
        #             the prediction input lines (i.e. video_object_tracking.csv has
        #             precisely the same number of lines as the prediction input had.)
        #         JSON_FILE_NAME = Name of .JSON file in the output directory, which
        #             contains prediction responses for the video time segment.
        #         STATUS = "OK" if prediction completed successfully, or an error
        #             code with message otherwise. If STATUS is not "OK" then the .JSON
        #             file for that line may not exist or be empty.
        #
        #         Each .JSON file, assuming STATUS is "OK", will contain a list of
        #         AnnotationPayload protos in JSON format, which are the predictions
        #         for each frame of the video time segment the file is assigned to in
        #         video_object_tracking.csv. All AnnotationPayload protos will have
        #         video_object_tracking field set.
        #  *  For Text Classification:
        #         In the created directory files `text_classification_1.jsonl`,
        #         `text_classification_2.jsonl`,...,`text_classification_N.jsonl`
        #         will be created, where N may be 1, and depends on the
        #         total number of inputs and annotations found.
        #
        #         Each .JSONL file will contain, per line, a JSON representation of a
        #         proto that wraps input text snippet or input text file and a list of
        #         zero or more AnnotationPayload protos (called annotations), which
        #         have classification detail populated. A single text snippet or file
        #         will be listed only once with all its annotations, and its
        #         annotations will never be split across files.
        #
        #         If prediction for any text snippet or file failed (partially or
        #         completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
        #         `errors_N.jsonl` files will be created (N depends on total number of
        #         failed predictions). These files will have a JSON representation of a
        #         proto that wraps input text snippet or input text file followed by
        #         exactly one
        #
        # [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
        #         containing only `code` and `message`.
        #
        #  *  For Text Sentiment:
        #         In the created directory files `text_sentiment_1.jsonl`,
        #         `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl`
        #         will be created, where N may be 1, and depends on the
        #         total number of inputs and annotations found.
        #
        #         Each .JSONL file will contain, per line, a JSON representation of a
        #         proto that wraps input text snippet or input text file and a list of
        #         zero or more AnnotationPayload protos (called annotations), which
        #         have text_sentiment detail populated. A single text snippet or file
        #         will be listed only once with all its annotations, and its
        #         annotations will never be split across files.
        #
        #         If prediction for any text snippet or file failed (partially or
        #         completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
        #         `errors_N.jsonl` files will be created (N depends on total number of
        #         failed predictions). These files will have a JSON representation of a
        #         proto that wraps input text snippet or input text file followed by
        #         exactly one
        #
        # [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
        #         containing only `code` and `message`.
        #
        #   *  For Text Extraction:
        #         In the created directory files `text_extraction_1.jsonl`,
        #         `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl`
        #         will be created, where N may be 1, and depends on the
        #         total number of inputs and annotations found.
        #         The contents of these .JSONL file(s) depend on whether the input
        #         used inline text, or documents.
        #         If input was inline, then each .JSONL file will contain, per line,
        #           a JSON representation of a proto that wraps given in request text
        #           snippet's "id" (if specified), followed by input text snippet,
        #           and a list of zero or more
        #           AnnotationPayload protos (called annotations), which have
        #           text_extraction detail populated. A single text snippet will be
        #           listed only once with all its annotations, and its annotations will
        #           never be split across files.
        #         If input used documents, then each .JSONL file will contain, per
        #           line, a JSON representation of a proto that wraps given in request
        #           document proto, followed by its OCR-ed representation in the form
        #           of a text snippet, finally followed by a list of zero or more
        #           AnnotationPayload protos (called annotations), which have
        #           text_extraction detail populated and refer, via their indices, to
        #           the OCR-ed text snippet. A single document (and its text snippet)
        #           will be listed only once with all its annotations, and its
        #           annotations will never be split across files.
        #         If prediction for any text snippet failed (partially or completely),
        #         then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
        #         `errors_N.jsonl` files will be created (N depends on total number of
        #         failed predictions). These files will have a JSON representation of a
        #         proto that wraps either the "id" : "<id_value>" (in case of inline)
        #         or the document proto (in case of document) but here followed by
        #         exactly one
        #
        # [`google.rpc.Status`](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
        #         containing only `code` and `message`.
        #
        #  *  For Tables:
        #         Output depends on whether
        #
        # {::Google::Cloud::AutoML::V1beta1::BatchPredictOutputConfig#gcs_destination gcs_destination}
        #         or
        #
        # {::Google::Cloud::AutoML::V1beta1::BatchPredictOutputConfig#bigquery_destination bigquery_destination}
        #         is set (either is allowed).
        #         GCS case:
        #           In the created directory files `tables_1.csv`, `tables_2.csv`,...,
        #           `tables_N.csv` will be created, where N may be 1, and depends on
        #           the total number of the successfully predicted rows.
        #           For all CLASSIFICATION
        #
        # [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
        #             Each .csv file will contain a header, listing all columns'
        #
        # {::Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name-s}
        #             given on input followed by M target column names in the format of
        #
        # "<{::Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_specs}
        #
        # {::Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name 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
        #             {::Google::Cloud::AutoML::V1beta1::TablesAnnotation#score scores}.
        #           For REGRESSION and FORECASTING
        #
        # [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
        #             Each .csv file will contain a header, listing all columns'
        #             [display_name-s][google.cloud.automl.v1beta1.display_name] given
        #             on input followed by the predicted target column with name in the
        #             format of
        #
        # "predicted_<{::Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_specs}
        #
        # {::Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name 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:
        #
        # {::Google::Cloud::AutoML::V1beta1::OutputConfig#bigquery_destination 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'
        #
        # {::Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name-s}
        #           followed by the target column with name in the format of
        #
        # "predicted_<{::Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_specs}
        #
        # {::Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name}>"
        #           The input feature columns will contain the respective values of
        #           successfully predicted rows, with the target column having an
        #           ARRAY of
        #
        # {::Google::Cloud::AutoML::V1beta1::AnnotationPayload AnnotationPayloads},
        #           represented as STRUCT-s, containing
        #           {::Google::Cloud::AutoML::V1beta1::TablesAnnotation 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_<{::Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_specs}
        #
        # {::Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name 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`.
        # @!attribute [rw] gcs_destination
        #   @return [::Google::Cloud::AutoML::V1beta1::GcsDestination]
        #     The Google Cloud Storage location of the directory where the output is to
        #     be written to.
        #
        #     Note: The following fields are mutually exclusive: `gcs_destination`, `bigquery_destination`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        # @!attribute [rw] bigquery_destination
        #   @return [::Google::Cloud::AutoML::V1beta1::BigQueryDestination]
        #     The BigQuery location where the output is to be written to.
        #
        #     Note: The following fields are mutually exclusive: `bigquery_destination`, `gcs_destination`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        class BatchPredictOutputConfig
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # Output configuration for ModelExport Action.
        # @!attribute [rw] gcs_destination
        #   @return [::Google::Cloud::AutoML::V1beta1::GcsDestination]
        #     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.
        #
        #     Note: The following fields are mutually exclusive: `gcs_destination`, `gcr_destination`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        # @!attribute [rw] gcr_destination
        #   @return [::Google::Cloud::AutoML::V1beta1::GcrDestination]
        #     The GCR location where model image is to be pushed to. This location
        #     may only be set for the following model formats:
        #       "docker".
        #
        #     The model image will be created under the given URI.
        #
        #     Note: The following fields are mutually exclusive: `gcr_destination`, `gcs_destination`. If a field in that set is populated, all other fields in the set will automatically be cleared.
        # @!attribute [rw] model_format
        #   @return [::String]
        #     The format in which the model must be exported. The available, and default,
        #     formats depend on the problem and model type (if given problem and type
        #     combination doesn't have a format listed, it means its models are not
        #     exportable):
        #
        #     *  For Image Classification mobile-low-latency-1, mobile-versatile-1,
        #            mobile-high-accuracy-1:
        #          "tflite" (default), "edgetpu_tflite", "tf_saved_model", "tf_js",
        #          "docker".
        #
        #     *  For Image Classification mobile-core-ml-low-latency-1,
        #            mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1:
        #          "core_ml" (default).
        #
        #     *  For Image Object Detection mobile-low-latency-1, mobile-versatile-1,
        #            mobile-high-accuracy-1:
        #          "tflite", "tf_saved_model", "tf_js".
        #
        #     *  For Video Classification cloud,
        #          "tf_saved_model".
        #
        #     *  For Video Object Tracking cloud,
        #          "tf_saved_model".
        #
        #     *  For Video Object Tracking mobile-versatile-1:
        #          "tflite", "edgetpu_tflite", "tf_saved_model", "docker".
        #
        #     *  For Video Object Tracking mobile-coral-versatile-1:
        #          "tflite", "edgetpu_tflite", "docker".
        #
        #     *  For Video Object Tracking mobile-coral-low-latency-1:
        #          "tflite", "edgetpu_tflite", "docker".
        #
        #     *  For Video Object Tracking mobile-jetson-versatile-1:
        #          "tf_saved_model", "docker".
        #
        #     *  For Tables:
        #          "docker".
        #
        #     Formats description:
        #
        #     * tflite - Used for Android mobile devices.
        #     * edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/)
        #                        devices.
        #     * tf_saved_model - A tensorflow model in SavedModel format.
        #     * tf_js - A [TensorFlow.js](https://www.tensorflow.org/js) model that can
        #               be used in the browser and in Node.js using JavaScript.
        #     * docker - Used for Docker containers. Use the params field to customize
        #                the container. The container is verified to work correctly on
        #                ubuntu 16.04 operating system. See more at
        #                [containers
        #
        #     quickstart](https:
        #     //cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
        #     * core_ml - Used for iOS mobile devices.
        # @!attribute [rw] params
        #   @return [::Google::Protobuf::Map{::String => ::String}]
        #     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".
        class ModelExportOutputConfig
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods

          # @!attribute [rw] key
          #   @return [::String]
          # @!attribute [rw] value
          #   @return [::String]
          class ParamsEntry
            include ::Google::Protobuf::MessageExts
            extend ::Google::Protobuf::MessageExts::ClassMethods
          end
        end

        # Output configuration for ExportEvaluatedExamples Action. Note that this call
        # is available only for 30 days since the moment the model was evaluated.
        # The output depends on the domain, as follows (note that only examples from
        # the TEST set are exported):
        #
        #  *  For Tables:
        #
        # {::Google::Cloud::AutoML::V1beta1::OutputConfig#bigquery_destination bigquery_destination}
        #       pointing to a BigQuery project must be set. In the given project a
        #       new dataset will be created with name
        #
        # `export_evaluated_examples_<model-display-name>_<timestamp-of-export-call>`
        #       where <model-display-name> will be made BigQuery-dataset-name
        #       compatible (e.g. most special characters will become underscores),
        #       and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601"
        #       format. In the dataset an `evaluated_examples` table will be
        #       created. It will have all the same columns as the
        #
        # {::Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata#primary_table_spec_id primary_table}
        #       of the
        #       {::Google::Cloud::AutoML::V1beta1::Model#dataset_id dataset} from which
        #       the model was created, as they were at the moment of model's
        #       evaluation (this includes the target column with its ground
        #       truth), followed by a column called "predicted_<target_column>". That
        #       last column will contain the model's prediction result for each
        #       respective row, given as ARRAY of
        #       {::Google::Cloud::AutoML::V1beta1::AnnotationPayload AnnotationPayloads},
        #       represented as STRUCT-s, containing
        #       {::Google::Cloud::AutoML::V1beta1::TablesAnnotation TablesAnnotation}.
        # @!attribute [rw] bigquery_destination
        #   @return [::Google::Cloud::AutoML::V1beta1::BigQueryDestination]
        #     The BigQuery location where the output is to be written to.
        class ExportEvaluatedExamplesOutputConfig
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # The Google Cloud Storage location for the input content.
        # @!attribute [rw] input_uris
        #   @return [::Array<::String>]
        #     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
        class GcsSource
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # The BigQuery location for the input content.
        # @!attribute [rw] input_uri
        #   @return [::String]
        #     Required. BigQuery URI to a table, up to 2000 characters long.
        #     Accepted forms:
        #     *  BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId
        class BigQuerySource
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # The Google Cloud Storage location where the output is to be written to.
        # @!attribute [rw] output_uri_prefix
        #   @return [::String]
        #     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.
        class GcsDestination
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # The BigQuery location for the output content.
        # @!attribute [rw] output_uri
        #   @return [::String]
        #     Required. BigQuery URI to a project, up to 2000 characters long.
        #     Accepted forms:
        #     *  BigQuery path e.g. bq://projectId
        class BigQueryDestination
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end

        # The GCR location where the image must be pushed to.
        # @!attribute [rw] output_uri
        #   @return [::String]
        #     Required. Google Contained Registry URI of the new image, up to 2000
        #     characters long. See
        #
        #     https:
        #     //cloud.google.com/container-registry/do
        #     // cs/pushing-and-pulling#pushing_an_image_to_a_registry
        #     Accepted forms:
        #     * [HOSTNAME]/[PROJECT-ID]/[IMAGE]
        #     * [HOSTNAME]/[PROJECT-ID]/[IMAGE]:[TAG]
        #
        #     The requesting user must have permission to push images the project.
        class GcrDestination
          include ::Google::Protobuf::MessageExts
          extend ::Google::Protobuf::MessageExts::ClassMethods
        end
      end
    end
  end
end
