# 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 V1
# Input configuration for {::Google::Cloud::AutoML::V1::AutoML::Client#import_data AutoMl.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::V1::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:
#
# 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,,,,,,,,,
#
#
#
#
# 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
# {::Google::Cloud::AutoML::V1::TextSnippet `text_snippet`} field followed by
# one or more {::Google::Cloud::AutoML::V1::AnnotationPayload `annotations`}
# 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
# {::Google::Cloud::AutoML::V1::InputConfig#gcs_source 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:
#
# "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.
#
#
#
#
#
# **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.
# @!attribute [rw] gcs_source
# @return [::Google::Cloud::AutoML::V1::GcsSource]
# The Google Cloud Storage location for the input content.
# For {::Google::Cloud::AutoML::V1::AutoML::Client#import_data AutoMl.ImportData}, `gcs_source` points to a CSV file with
# a structure described in {::Google::Cloud::AutoML::V1::InputConfig InputConfig}.
# @!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.
#
# 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".
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::V1::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:
#
# 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
# {::Google::Cloud::AutoML::V1::BatchPredictInputConfig#gcs_source 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:
#
# "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"}]}
#
# **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.
# @!attribute [rw] gcs_source
# @return [::Google::Cloud::AutoML::V1::GcsSource]
# Required. The Google Cloud Storage location for the input content.
class BatchPredictInputConfig
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Input configuration of a {::Google::Cloud::AutoML::V1::Document Document}.
# @!attribute [rw] gcs_source
# @return [::Google::Cloud::AutoML::V1::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 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__`
# where 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::V1::GcsDestination]
# 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-- where
# timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export
# output will be written into that directory.
class OutputConfig
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Output configuration for BatchPredict Action.
#
# As destination the
#
# {::Google::Cloud::AutoML::V1::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--",
# 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" : "" 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" : "" 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" : "" 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" : "" 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" : "" (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]>__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__`
# where 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`.
# @!attribute [rw] gcs_destination
# @return [::Google::Cloud::AutoML::V1::GcsDestination]
# Required. The Google Cloud Storage location of the directory where the output is to
# be written to.
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::V1::GcsDestination]
# 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--",
# 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.
# @!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".
# 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
# 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 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
end
end
end
end