text: # -*- coding: utf-8 -*-
  # Copyright 2020 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  import proto  # type: ignore
  
  
  __protobuf__ = proto.module(
      package="google.cloud.automl.v1beta1",
      manifest={
          "InputConfig",
          "BatchPredictInputConfig",
          "DocumentInputConfig",
          "OutputConfig",
          "BatchPredictOutputConfig",
          "ModelExportOutputConfig",
          "ExportEvaluatedExamplesOutputConfig",
          "GcsSource",
          "BigQuerySource",
          "GcsDestination",
          "BigQueryDestination",
          "GcrDestination",
      },
  )
  
  
  class InputConfig(proto.Message):
      r"""Input configuration for ImportData Action.
  
      The format of input depends on dataset_metadata the Dataset into
      which the import is happening has. As input source the
      [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] is
      expected, unless specified otherwise. Additionally any input .CSV
      file by itself must be 100MB or smaller, unless specified otherwise.
      If an "example" file (that is, image, video etc.) with identical
      content (even if it had different GCS_FILE_PATH) is mentioned
      multiple times, then its label, bounding boxes etc. are appended.
      The same file should be always provided with the same ML_USE and
      GCS_FILE_PATH, if it is not, then these values are
      nondeterministically selected from the given ones.
  
      The formats are represented in EBNF with commas being literal and
      with non-terminal symbols defined near the end of this comment. The
      formats are:
  
      -  For Image Classification: CSV file(s) with each line in format:
         ML_USE,GCS_FILE_PATH,LABEL,LABEL,... GCS_FILE_PATH leads to image
         of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG,
         .WEBP, .BMP, .TIFF, .ICO For MULTICLASS classification type, at
         most one LABEL is allowed per image. If an image has not yet been
         labeled, then it should be mentioned just once with no LABEL.
         Some sample rows: TRAIN,gs://folder/image1.jpg,daisy
         TEST,gs://folder/image2.jpg,dandelion,tulip,rose
         UNASSIGNED,gs://folder/image3.jpg,daisy
         UNASSIGNED,gs://folder/image4.jpg
  
      -  For Image Object Detection: CSV file(s) with each line in format:
         ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX \| ,,,,,,,)
         GCS_FILE_PATH leads to image of up to 30MB in size. Supported
         extensions: .JPEG, .GIF, .PNG. Each image is assumed to be
         exhaustively labeled. The minimum allowed BOUNDING_BOX edge
         length is 0.01, and no more than 500 BOUNDING_BOX-es per image
         are allowed (one BOUNDING_BOX is defined per line). If an image
         has not yet been labeled, then it should be mentioned just once
         with no LABEL and the ",,,,,,," in place of the BOUNDING_BOX. For
         images which are known to not contain any bounding boxes, they
         should be labelled explictly as "NEGATIVE_IMAGE", followed by
         ",,,,,,," in place of the BOUNDING_BOX. Sample rows:
         TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,,
         TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,,
         UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3
         TEST,gs://folder/im3.png,,,,,,,,,
         TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,,
  
      -  For Video Classification: CSV file(s) with each line in format:
         ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be
         used. The GCS_FILE_PATH should lead to another .csv file which
         describes examples that have given ML_USE, using the following
         row format:
         GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END \| ,,)
         Here GCS_FILE_PATH leads to a video of up to 50GB in size and up
         to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
         TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length
         of the video, and end has to be after the start. Any segment of a
         video which has one or more labels on it, is considered a hard
         negative for all other labels. Any segment with no labels on it
         is considered to be unknown. If a whole video is unknown, then it
         shuold be mentioned just once with ",," in place of LABEL,
         TIME_SEGMENT_START,TIME_SEGMENT_END. Sample top level CSV file:
         TRAIN,gs://folder/train_videos.csv
         TEST,gs://folder/test_videos.csv
         UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file
         for a particular ML_USE:
         gs://folder/video1.avi,car,120,180.000021
         gs://folder/video1.avi,bike,150,180.000021
         gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,,
  
      -  For Video Object Tracking: CSV file(s) with each line in format:
         ML_USE,GCS_FILE_PATH where ML_USE VALIDATE value should not be
         used. The GCS_FILE_PATH should lead to another .csv file which
         describes examples that have given ML_USE, using one of the
         following row format:
         GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or
         GCS_FILE_PATH,,,,,,,,,, Here GCS_FILE_PATH leads to a video of up
         to 50GB in size and up to 3h duration. Supported extensions:
         .MOV, .MPEG4, .MP4, .AVI. Providing INSTANCE_IDs can help to
         obtain a better model. When a specific labeled entity leaves the
         video frame, and shows up afterwards it is not required, albeit
         preferable, that the same INSTANCE_ID is given to it. TIMESTAMP
         must be within the length of the video, the BOUNDING_BOX is
         assumed to be drawn on the closest video's frame to the
         TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected to be
         exhaustively labeled and no more than 500 BOUNDING_BOX-es per
         frame are allowed. If a whole video is unknown, then it should be
         mentioned just once with ",,,,,,,,,," in place of LABEL,
         [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX. Sample top level CSV file:
         TRAIN,gs://folder/train_videos.csv
         TEST,gs://folder/test_videos.csv
         UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a
         CSV file for a particular ML_USE:
         gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
         gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
         gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
         gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
         gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,,
         gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,,
         gs://folder/video2.avi,,,,,,,,,,,
  
      -  For Text Extraction: CSV file(s) with each line in format:
         ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .JSONL (that is,
         JSON Lines) file which either imports text in-line or as
         documents. Any given .JSONL file must be 100MB or smaller. The
         in-line .JSONL file contains, per line, a proto that wraps a
         TextSnippet proto (in json representation) followed by one or
         more AnnotationPayload protos (called annotations), which have
         display_name and text_extraction detail populated. The given text
         is expected to be annotated exhaustively, for example, if you
         look for animals and text contains "dolphin" that is not labeled,
         then "dolphin" is assumed to not be an animal. Any given text
         snippet content must be 10KB or smaller, and also be UTF-8 NFC
         encoded (ASCII already is). The document .JSONL file contains,
         per line, a proto that wraps a Document proto. The Document proto
         must have either document_text or input_config set. In
         document_text case, the Document proto may also contain the
         spatial information of the document, including layout, document
         dimension and page number. In input_config case, only PDF
         documents are supported now, and each document may be up to 2MB
         large. Currently, annotations on documents cannot be specified at
         import. Three sample CSV rows: TRAIN,gs://folder/file1.jsonl
         VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl
         Sample in-line JSON Lines file for entity extraction (presented
         here with artificial line breaks, but the only actual line break
         is denoted by \\n).: { "document": { "document_text": {"content":
         "dog cat"} "layout": [ { "text_segment": { "start_offset": 0,
         "end_offset": 3, }, "page_number": 1, "bounding_poly": {
         "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y":
         0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ], },
         "text_segment_type": TOKEN, }, { "text_segment": {
         "start_offset": 4, "end_offset": 7, }, "page_number": 1,
         "bounding_poly": { "normalized_vertices": [ {"x": 0.4, "y": 0.1},
         {"x": 0.4, "y": 0.3}, {"x": 0.8, "y": 0.3}, {"x": 0.8, "y": 0.1},
         ], }, "text_segment_type": TOKEN, }
  
         ::
  
                  ],
                  "document_dimensions": {
                    "width": 8.27,
                    "height": 11.69,
                    "unit": INCH,
                  }
                  "page_count": 1,
                },
                "annotations": [
                  {
                    "display_name": "animal",
                    "text_extraction": {"text_segment": {"start_offset": 0,
                    "end_offset": 3}}
                  },
                  {
                    "display_name": "animal",
                    "text_extraction": {"text_segment": {"start_offset": 4,
                    "end_offset": 7}}
                  }
                ],
              }\n
              {
                 "text_snippet": {
                   "content": "This dog is good."
                 },
                 "annotations": [
                   {
                     "display_name": "animal",
                     "text_extraction": {
                       "text_segment": {"start_offset": 5, "end_offset": 8}
                     }
                   }
                 ]
              }
            Sample document JSON Lines file (presented here with artificial line
            breaks, but the only actual line break is denoted by \n).:
              {
                "document": {
                  "input_config": {
                    "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
                    }
                  }
                }
              }\n
              {
                "document": {
                  "input_config": {
                    "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
                    }
                  }
                }
              }
  
      -  For Text Classification: CSV file(s) with each line in format:
         ML_USE,(TEXT_SNIPPET \| GCS_FILE_PATH),LABEL,LABEL,...
         TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If
         the column content is a valid gcs file path, i.e. prefixed by
         "gs://", it will be treated as a GCS_FILE_PATH, else if the
         content is enclosed within double quotes (""), it is treated as a
         TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a
         .txt file with UTF-8 encoding, for example,
         "gs://folder/content.txt", and the content in it is extracted as
         a text snippet. In TEXT_SNIPPET case, the column content
         excluding quotes is treated as to be imported text snippet. In
         both cases, the text snippet/file size must be within 128kB.
         Maximum 100 unique labels are allowed per CSV row. Sample rows:
         TRAIN,"They have bad food and very rude",RudeService,BadFood
         TRAIN,gs://folder/content.txt,SlowService TEST,"Typically always
         bad service there.",RudeService VALIDATE,"Stomach ache to
         go.",BadFood
  
      -  For Text Sentiment: CSV file(s) with each line in format:
         ML_USE,(TEXT_SNIPPET \| GCS_FILE_PATH),SENTIMENT TEXT_SNIPPET and
         GCS_FILE_PATH are distinguished by a pattern. If the column
         content is a valid gcs file path, that is, prefixed by "gs://",
         it is treated as a GCS_FILE_PATH, otherwise it is treated as a
         TEXT_SNIPPET. In the GCS_FILE_PATH case, the path must lead to a
         .txt file with UTF-8 encoding, for example,
         "gs://folder/content.txt", and the content in it is extracted as
         a text snippet. In TEXT_SNIPPET case, the column content itself
         is treated as to be imported text snippet. In both cases, the
         text snippet must be up to 500 characters long. Sample rows:
         TRAIN,"@freewrytin this is way too good for your product",2
         TRAIN,"I need this product so bad",3 TEST,"Thank you for this
         product.",4 VALIDATE,gs://folder/content.txt,2
  
      -  For Tables: Either
         [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source]
         or
  
      [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]
      can be used. All inputs is concatenated into a single
  
      [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_name]
      For gcs_source: CSV file(s), where the first row of the first file
      is the header, containing unique column names. If the first row of a
      subsequent file is the same as the header, then it is also treated
      as a header. All other rows contain values for the corresponding
      columns. Each .CSV file by itself must be 10GB or smaller, and their
      total size must be 100GB or smaller. First three sample rows of a
      CSV file: "Id","First Name","Last Name","Dob","Addresses"
  
      "1","John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
  
      "2","Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
      For bigquery_source: An URI of a BigQuery table. The user data size
      of the BigQuery table must be 100GB or smaller. An imported table
      must have between 2 and 1,000 columns, inclusive, and between 1000
      and 100,000,000 rows, inclusive. There are at most 5 import data
      running in parallel. Definitions: ML_USE = "TRAIN" \| "VALIDATE" \|
      "TEST" \| "UNASSIGNED" Describes how the given example (file) should
      be used for model training. "UNASSIGNED" can be used when user has
      no preference. GCS_FILE_PATH = A path to file on GCS, e.g.
      "gs://folder/image1.png". LABEL = A display name of an object on an
      image, video etc., e.g. "dog". Must be up to 32 characters long and
      can consist only of ASCII Latin letters A-Z and a-z, underscores(_),
      and ASCII digits 0-9. For each label an AnnotationSpec is created
      which display_name becomes the label; AnnotationSpecs are given back
      in predictions. INSTANCE_ID = A positive integer that identifies a
      specific instance of a labeled entity on an example. Used e.g. to
      track two cars on a video while being able to tell apart which one
      is which. BOUNDING_BOX = VERTEX,VERTEX,VERTEX,VERTEX \|
      VERTEX,,,VERTEX,, A rectangle parallel to the frame of the example
      (image, video). If 4 vertices are given they are connected by edges
      in the order provided, if 2 are given they are recognized as
      diagonally opposite vertices of the rectangle. VERTEX =
      COORDINATE,COORDINATE First coordinate is horizontal (x), the second
      is vertical (y). COORDINATE = A float in 0 to 1 range, relative to
      total length of image or video in given dimension. For fractions the
      leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is
      in top left. TIME_SEGMENT_START = TIME_OFFSET Expresses a beginning,
      inclusive, of a time segment within an example that has a time
      dimension (e.g. video). TIME_SEGMENT_END = TIME_OFFSET Expresses an
      end, exclusive, of a time segment within an example that has a time
      dimension (e.g. video). TIME_OFFSET = A number of seconds as
      measured from the start of an example (e.g. video). Fractions are
      allowed, up to a microsecond precision. "inf" is allowed, and it
      means the end of the example. TEXT_SNIPPET = A content of a text
      snippet, UTF-8 encoded, enclosed within double quotes ("").
      SENTIMENT = An integer between 0 and
      Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive).
      Describes the ordinal of the sentiment - higher value means a more
      positive sentiment. All the values are completely relative, i.e.
      neither 0 needs to mean a negative or neutral sentiment nor
      sentiment_max needs to mean a positive one - it is just required
      that 0 is the least positive sentiment in the data, and
      sentiment_max is the most positive one. The SENTIMENT shouldn't be
      confused with "score" or "magnitude" from the previous Natural
      Language Sentiment Analysis API. All SENTIMENT values between 0 and
      sentiment_max must be represented in the imported data. On
      prediction the same 0 to sentiment_max range will be used. The
      difference between neighboring sentiment values needs not to be
      uniform, e.g. 1 and 2 may be similar whereas the difference between
      2 and 3 may be huge.
  
      Errors: If any of the provided CSV files can't be parsed or if more
      than certain percent of CSV rows cannot be processed then the
      operation fails and nothing is imported. Regardless of overall
      success or failure the per-row failures, up to a certain count cap,
      is listed in Operation.metadata.partial_failures.
  
      This message has `oneof`_ fields (mutually exclusive fields).
      For each oneof, at most one member field can be set at the same time.
      Setting any member of the oneof automatically clears all other
      members.
  
      .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
  
      Attributes:
          gcs_source (google.cloud.automl_v1beta1.types.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.
  
              This field is a member of `oneof`_ ``source``.
          bigquery_source (google.cloud.automl_v1beta1.types.BigQuerySource):
              The BigQuery location for the input content.
  
              This field is a member of `oneof`_ ``source``.
          params (Sequence[google.cloud.automl_v1beta1.types.InputConfig.ParamsEntry]):
              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".
      """
  
      gcs_source = proto.Field(
          proto.MESSAGE, number=1, oneof="source", message="GcsSource",
      )
      bigquery_source = proto.Field(
          proto.MESSAGE, number=3, oneof="source", message="BigQuerySource",
      )
      params = proto.MapField(proto.STRING, proto.STRING, number=2,)
  
  
  class BatchPredictInputConfig(proto.Message):
      r"""Input configuration for BatchPredict Action.
  
      The format of input depends on the ML problem of the model used for
      prediction. As input source the
      [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] is
      expected, unless specified otherwise.
  
      The formats are represented in EBNF with commas being literal and
      with non-terminal symbols defined near the end of this comment. The
      formats are:
  
      -  For Image Classification: CSV file(s) with each line having just
         a single column: GCS_FILE_PATH which leads to image of up to 30MB
         in size. Supported extensions: .JPEG, .GIF, .PNG. This path is
         treated as the ID in the Batch predict output. Three sample rows:
         gs://folder/image1.jpeg gs://folder/image2.gif
         gs://folder/image3.png
  
      -  For Image Object Detection: CSV file(s) with each line having
         just a single column: GCS_FILE_PATH which leads to image of up to
         30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path
         is treated as the ID in the Batch predict output. Three sample
         rows: gs://folder/image1.jpeg gs://folder/image2.gif
         gs://folder/image3.png
  
      -  For Video Classification: CSV file(s) with each line in format:
         GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH
         leads to video of up to 50GB in size and up to 3h duration.
         Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
         TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length
         of the video, and end has to be after the start. Three sample
         rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60
         gs://folder/vid2.mov,0,inf
  
      -  For Video Object Tracking: CSV file(s) with each line in format:
         GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH
         leads to video of up to 50GB in size and up to 3h duration.
         Supported extensions: .MOV, .MPEG4, .MP4, .AVI.
         TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length
         of the video, and end has to be after the start. Three sample
         rows: gs://folder/video1.mp4,10,240
         gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf
  
      -  For Text Classification: CSV file(s) with each line having just a
         single column: GCS_FILE_PATH \| TEXT_SNIPPET Any given text file
         can have size upto 128kB. Any given text snippet content must
         have 60,000 characters or less. Three sample rows:
         gs://folder/text1.txt "Some text content to predict"
         gs://folder/text3.pdf Supported file extensions: .txt, .pdf
  
      -  For Text Sentiment: CSV file(s) with each line having just a
         single column: GCS_FILE_PATH \| TEXT_SNIPPET Any given text file
         can have size upto 128kB. Any given text snippet content must
         have 500 characters or less. Three sample rows:
         gs://folder/text1.txt "Some text content to predict"
         gs://folder/text3.pdf Supported file extensions: .txt, .pdf
  
      -  For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either
         provide text in-line or as documents (for a single BatchPredict
         call only one of the these formats may be used). The in-line
         .JSONL file(s) contain per line a proto that wraps a temporary
         user-assigned TextSnippet ID (string up to 2000 characters long)
         called "id", a TextSnippet proto (in json representation) and
         zero or more TextFeature protos. Any given text snippet content
         must have 30,000 characters or less, and also be UTF-8 NFC
         encoded (ASCII already is). The IDs provided should be unique.
         The document .JSONL file(s) contain, per line, a proto that wraps
         a Document proto with input_config set. Only PDF documents are
         supported now, and each document must be up to 2MB large. Any
         given .JSONL file must be 100MB or smaller, and no more than 20
         files may be given. Sample in-line JSON Lines file (presented
         here with artificial line breaks, but the only actual line break
         is denoted by \\n): { "id": "my_first_id", "text_snippet": {
         "content": "dog car cat"}, "text_features": [ { "text_segment":
         {"start_offset": 4, "end_offset": 6}, "structural_type":
         PARAGRAPH, "bounding_poly": { "normalized_vertices": [ {"x": 0.1,
         "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3,
         "y": 0.1}, ] }, } ], }\n { "id": "2", "text_snippet": {
         "content": "An elaborate content", "mime_type": "text/plain" } }
         Sample document JSON Lines file (presented here with artificial
         line breaks, but the only actual line break is denoted by \\n).:
         { "document": { "input_config": { "gcs_source": { "input_uris": [
         "gs://folder/document1.pdf" ] } } } }\n { "document": {
         "input_config": { "gcs_source": { "input_uris": [
         "gs://folder/document2.pdf" ] } } } }
  
      -  For Tables: Either
         [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source]
         or
  
      [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source].
      GCS case: CSV file(s), each by itself 10GB or smaller and total size
      must be 100GB or smaller, where first file must have a header
      containing column names. If the first row of a subsequent file is
      the same as the header, then it is also treated as a header. All
      other rows contain values for the corresponding columns. The column
      names must contain the model's
  
      [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
  
      [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
      (order doesn't matter). The columns corresponding to the model's
      input feature column specs must contain values compatible with the
      column spec's data types. Prediction on all the rows, i.e. the CSV
      lines, will be attempted. For FORECASTING
  
      [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
      all columns having
  
      [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType]
      type will be ignored. First three sample rows of a CSV file: "First
      Name","Last Name","Dob","Addresses"
  
      "John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]"
  
      "Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]}
      BigQuery case: An URI of a BigQuery table. The user data size of the
      BigQuery table must be 100GB or smaller. The column names must
      contain the model's
  
      [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
  
      [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
      (order doesn't matter). The columns corresponding to the model's
      input feature column specs must contain values compatible with the
      column spec's data types. Prediction on all the rows of the table
      will be attempted. For FORECASTING
  
      [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
      all columns having
  
      [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType]
      type will be ignored.
  
      Definitions: GCS_FILE_PATH = A path to file on GCS, e.g.
      "gs://folder/video.avi". TEXT_SNIPPET = A content of a text snippet,
      UTF-8 encoded, enclosed within double quotes ("") TIME_SEGMENT_START
      = TIME_OFFSET Expresses a beginning, inclusive, of a time segment
      within an example that has a time dimension (e.g. video).
      TIME_SEGMENT_END = TIME_OFFSET Expresses an end, exclusive, of a
      time segment within an example that has a time dimension (e.g.
      video). TIME_OFFSET = A number of seconds as measured from the start
      of an example (e.g. video). Fractions are allowed, up to a
      microsecond precision. "inf" is allowed and it means the end of the
      example.
  
      Errors: If any of the provided CSV files can't be parsed or if more
      than certain percent of CSV rows cannot be processed then the
      operation fails and prediction does not happen. Regardless of
      overall success or failure the per-row failures, up to a certain
      count cap, will be listed in Operation.metadata.partial_failures.
  
      This message has `oneof`_ fields (mutually exclusive fields).
      For each oneof, at most one member field can be set at the same time.
      Setting any member of the oneof automatically clears all other
      members.
  
      .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
  
      Attributes:
          gcs_source (google.cloud.automl_v1beta1.types.GcsSource):
              The Google Cloud Storage location for the
              input content.
  
              This field is a member of `oneof`_ ``source``.
          bigquery_source (google.cloud.automl_v1beta1.types.BigQuerySource):
              The BigQuery location for the input content.
  
              This field is a member of `oneof`_ ``source``.
      """
  
      gcs_source = proto.Field(
          proto.MESSAGE, number=1, oneof="source", message="GcsSource",
      )
      bigquery_source = proto.Field(
          proto.MESSAGE, number=2, oneof="source", message="BigQuerySource",
      )
  
  
  class DocumentInputConfig(proto.Message):
      r"""Input configuration of a
      [Document][google.cloud.automl.v1beta1.Document].
  
      Attributes:
          gcs_source (google.cloud.automl_v1beta1.types.GcsSource):
              The Google Cloud Storage location of the
              document file. Only a single path should be
              given. Max supported size: 512MB.
              Supported extensions: .PDF.
      """
  
      gcs_source = proto.Field(proto.MESSAGE, number=1, message="GcsSource",)
  
  
  class OutputConfig(proto.Message):
      r"""-  For Translation: CSV file ``translation.csv``, with each line in
         format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file
         which describes examples that have given ML_USE, using the
         following row format per line: TEXT_SNIPPET (in source language)
         \\t TEXT_SNIPPET (in target language)
  
         -  For Tables: Output depends on whether the dataset was imported
            from GCS or BigQuery. GCS case:
  
      [gcs_destination][google.cloud.automl.v1beta1.OutputConfig.gcs_destination]
      must be set. Exported are CSV file(s) ``tables_1.csv``,
      ``tables_2.csv``,...,\ ``tables_N.csv`` with each having as header
      line the table's column names, and all other lines contain values
      for the header columns. BigQuery case:
  
      [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination]
      pointing to a BigQuery project must be set. In the given project a
      new dataset will be created with name
  
      ``export_data_<automl-dataset-display-name>_<timestamp-of-export-call>``
      where 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.
  
      This message has `oneof`_ fields (mutually exclusive fields).
      For each oneof, at most one member field can be set at the same time.
      Setting any member of the oneof automatically clears all other
      members.
  
      .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
  
      Attributes:
          gcs_destination (google.cloud.automl_v1beta1.types.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-- where
              timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format.
              All export output will be written into that directory.
  
              This field is a member of `oneof`_ ``destination``.
          bigquery_destination (google.cloud.automl_v1beta1.types.BigQueryDestination):
              The BigQuery location where the output is to
              be written to.
  
              This field is a member of `oneof`_ ``destination``.
      """
  
      gcs_destination = proto.Field(
          proto.MESSAGE, number=1, oneof="destination", message="GcsDestination",
      )
      bigquery_destination = proto.Field(
          proto.MESSAGE, number=2, oneof="destination", message="BigQueryDestination",
      )
  
  
  class BatchPredictOutputConfig(proto.Message):
      r"""Output configuration for BatchPredict Action.
  
      As destination the
  
      [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination]
      must be set unless specified otherwise for a domain. If
      gcs_destination is set then in the given directory a new directory
      is created. Its name will be "prediction--", where timestamp is in
      YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends
      on the ML problem the predictions are made for.
  
      -  For Image Classification: In the created directory files
         ``image_classification_1.jsonl``,
         ``image_classification_2.jsonl``,...,\ ``image_classification_N.jsonl``
         will be created, where N may be 1, and depends on the total
         number of the successfully predicted images and annotations. A
         single image will be listed only once with all its annotations,
         and its annotations will never be split across files. Each .JSONL
         file will contain, per line, a JSON representation of a proto
         that wraps image's "ID" : "<id_value>" followed by a list of zero
         or more AnnotationPayload protos (called annotations), which have
         classification detail populated. If prediction for any image
         failed (partially or completely), then an additional
         ``errors_1.jsonl``, ``errors_2.jsonl``,..., ``errors_N.jsonl``
         files will be created (N depends on total number of failed
         predictions). These files will have a JSON representation of a
         proto that wraps the same "ID" : "<id_value>" but here followed
         by exactly one
  
      [``google.rpc.Status``](https:
      //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
      containing only ``code`` and ``message``\ fields.
  
      -  For Image Object Detection: In the created directory files
         ``image_object_detection_1.jsonl``,
         ``image_object_detection_2.jsonl``,...,\ ``image_object_detection_N.jsonl``
         will be created, where N may be 1, and depends on the total
         number of the successfully predicted images and annotations. Each
         .JSONL file will contain, per line, a JSON representation of a
         proto that wraps image's "ID" : "<id_value>" followed by a list
         of zero or more AnnotationPayload protos (called annotations),
         which have image_object_detection detail populated. A single
         image will be listed only once with all its annotations, and its
         annotations will never be split across files. If prediction for
         any image failed (partially or completely), then additional
         ``errors_1.jsonl``, ``errors_2.jsonl``,..., ``errors_N.jsonl``
         files will be created (N depends on total number of failed
         predictions). These files will have a JSON representation of a
         proto that wraps the same "ID" : "<id_value>" but here followed
         by exactly one
  
      [``google.rpc.Status``](https:
      //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
      containing only ``code`` and ``message``\ fields.
  
      -  For Video Classification: In the created directory a
         video_classification.csv file, and a .JSON file per each video
         classification requested in the input (i.e. each line in given
         CSV(s)), will be created.
  
         ::
  
            The format of video_classification.csv is:
  
      GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
      where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1
      to 1 the prediction input lines (i.e. video_classification.csv has
      precisely the same number of lines as the prediction input had.)
      JSON_FILE_NAME = Name of .JSON file in the output directory, which
      contains prediction responses for the video time segment. STATUS =
      "OK" if prediction completed successfully, or an error code with
      message otherwise. If STATUS is not "OK" then the .JSON file for
      that line may not exist or be empty.
  
      ::
  
              Each .JSON file, assuming STATUS is "OK", will contain a list of
              AnnotationPayload protos in JSON format, which are the predictions
              for the video time segment the file is assigned to in the
              video_classification.csv. All AnnotationPayload protos will have
              video_classification field set, and will be sorted by
              video_classification.type field (note that the returned types are
              governed by `classifaction_types` parameter in
              [PredictService.BatchPredictRequest.params][]).
  
      -  For Video Object Tracking: In the created directory a
         video_object_tracking.csv file will be created, and multiple
         files video_object_trackinng_1.json,
         video_object_trackinng_2.json,..., video_object_trackinng_N.json,
         where N is the number of requests in the input (i.e. the number
         of lines in given CSV(s)).
  
         ::
  
            The format of video_object_tracking.csv is:
  
      GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
      where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1
      to 1 the prediction input lines (i.e. video_object_tracking.csv has
      precisely the same number of lines as the prediction input had.)
      JSON_FILE_NAME = Name of .JSON file in the output directory, which
      contains prediction responses for the video time segment. STATUS =
      "OK" if prediction completed successfully, or an error code with
      message otherwise. If STATUS is not "OK" then the .JSON file for
      that line may not exist or be empty.
  
      ::
  
              Each .JSON file, assuming STATUS is "OK", will contain a list of
              AnnotationPayload protos in JSON format, which are the predictions
              for each frame of the video time segment the file is assigned to in
              video_object_tracking.csv. All AnnotationPayload protos will have
              video_object_tracking field set.
  
      -  For Text Classification: In the created directory files
         ``text_classification_1.jsonl``,
         ``text_classification_2.jsonl``,...,\ ``text_classification_N.jsonl``
         will be created, where N may be 1, and depends on the total
         number of inputs and annotations found.
  
         ::
  
            Each .JSONL file will contain, per line, a JSON representation of a
            proto that wraps input text snippet or input text file and a list of
            zero or more AnnotationPayload protos (called annotations), which
            have classification detail populated. A single text snippet or file
            will be listed only once with all its annotations, and its
            annotations will never be split across files.
  
            If prediction for any text snippet or file failed (partially or
            completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
            `errors_N.jsonl` files will be created (N depends on total number of
            failed predictions). These files will have a JSON representation of a
            proto that wraps input text snippet or input text file followed by
            exactly one
  
      [``google.rpc.Status``](https:
      //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
      containing only ``code`` and ``message``.
  
      -  For Text Sentiment: In the created directory files
         ``text_sentiment_1.jsonl``,
         ``text_sentiment_2.jsonl``,...,\ ``text_sentiment_N.jsonl`` will
         be created, where N may be 1, and depends on the total number of
         inputs and annotations found.
  
         ::
  
            Each .JSONL file will contain, per line, a JSON representation of a
            proto that wraps input text snippet or input text file and a list of
            zero or more AnnotationPayload protos (called annotations), which
            have text_sentiment detail populated. A single text snippet or file
            will be listed only once with all its annotations, and its
            annotations will never be split across files.
  
            If prediction for any text snippet or file failed (partially or
            completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,...,
            `errors_N.jsonl` files will be created (N depends on total number of
            failed predictions). These files will have a JSON representation of a
            proto that wraps input text snippet or input text file followed by
            exactly one
  
      [``google.rpc.Status``](https:
      //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
      containing only ``code`` and ``message``.
  
      -  For Text Extraction: In the created directory files
         ``text_extraction_1.jsonl``,
         ``text_extraction_2.jsonl``,...,\ ``text_extraction_N.jsonl``
         will be created, where N may be 1, and depends on the total
         number of inputs and annotations found. The contents of these
         .JSONL file(s) depend on whether the input used inline text, or
         documents. If input was inline, then each .JSONL file will
         contain, per line, a JSON representation of a proto that wraps
         given in request text snippet's "id" (if specified), followed by
         input text snippet, and a list of zero or more AnnotationPayload
         protos (called annotations), which have text_extraction detail
         populated. A single text snippet will be listed only once with
         all its annotations, and its annotations will never be split
         across files. If input used documents, then each .JSONL file will
         contain, per line, a JSON representation of a proto that wraps
         given in request document proto, followed by its OCR-ed
         representation in the form of a text snippet, finally followed by
         a list of zero or more AnnotationPayload protos (called
         annotations), which have text_extraction detail populated and
         refer, via their indices, to the OCR-ed text snippet. A single
         document (and its text snippet) will be listed only once with all
         its annotations, and its annotations will never be split across
         files. If prediction for any text snippet failed (partially or
         completely), then additional ``errors_1.jsonl``,
         ``errors_2.jsonl``,..., ``errors_N.jsonl`` files will be created
         (N depends on total number of failed predictions). These files
         will have a JSON representation of a proto that wraps either the
         "id" : "<id_value>" (in case of inline) or the document proto (in
         case of document) but here followed by exactly one
  
      [``google.rpc.Status``](https:
      //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
      containing only ``code`` and ``message``.
  
      -  For Tables: Output depends on whether
  
      [gcs_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.gcs_destination]
      or
  
      [bigquery_destination][google.cloud.automl.v1beta1.BatchPredictOutputConfig.bigquery_destination]
      is set (either is allowed). GCS case: In the created directory files
      ``tables_1.csv``, ``tables_2.csv``,..., ``tables_N.csv`` will be
      created, where N may be 1, and depends on the total number of the
      successfully predicted rows. For all CLASSIFICATION
  
      [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
      Each .csv file will contain a header, listing all columns'
  
      [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
      given on input followed by M target column names in the format of
  
      "<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
  
      [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>\_\_score"
      where M is the number of distinct target values, i.e. number of
      distinct values in the target column of the table used to train the
      model. Subsequent lines will contain the respective values of
      successfully predicted rows, with the last, i.e. the target, columns
      having the corresponding prediction
      [scores][google.cloud.automl.v1beta1.TablesAnnotation.score]. For
      REGRESSION and FORECASTING
  
      [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
      Each .csv file will contain a header, listing all columns'
      [display_name-s][google.cloud.automl.v1beta1.display_name] given on
      input followed by the predicted target column with name in the
      format of
  
      "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
  
      [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>"
      Subsequent lines will contain the respective values of successfully
      predicted rows, with the last, i.e. the target, column having the
      predicted target value. If prediction for any rows failed, then an
      additional ``errors_1.csv``, ``errors_2.csv``,..., ``errors_N.csv``
      will be created (N depends on total number of failed rows). These
      files will have analogous format as ``tables_*.csv``, but always
      with a single target column having
  
      [``google.rpc.Status``](https:
      //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
      represented as a JSON string, and containing only ``code`` and
      ``message``. BigQuery case:
  
      [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination]
      pointing to a BigQuery project must be set. In the given project a
      new dataset will be created with name
      ``prediction_<model-display-name>_<timestamp-of-prediction-call>``
      where will be made BigQuery-dataset-name compatible (e.g. most
      special characters will become underscores), and timestamp will be
      in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the
      dataset two tables will be created, ``predictions``, and ``errors``.
      The ``predictions`` table's column names will be the input columns'
  
      [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
      followed by the target column with name in the format of
  
      "predicted_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
  
      [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>"
      The input feature columns will contain the respective values of
      successfully predicted rows, with the target column having an ARRAY
      of
  
      [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload],
      represented as STRUCT-s, containing
      [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].
      The ``errors`` table contains rows for which the prediction has
      failed, it has analogous input columns while the target column name
      is in the format of
  
      "errors_<[target_column_specs][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
  
      [display_name][google.cloud.automl.v1beta1.ColumnSpec.display_name]>",
      and as a value has
  
      [``google.rpc.Status``](https:
      //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto)
      represented as a STRUCT, and containing only ``code`` and
      ``message``.
  
      This message has `oneof`_ fields (mutually exclusive fields).
      For each oneof, at most one member field can be set at the same time.
      Setting any member of the oneof automatically clears all other
      members.
  
      .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
  
      Attributes:
          gcs_destination (google.cloud.automl_v1beta1.types.GcsDestination):
              The Google Cloud Storage location of the
              directory where the output is to be written to.
  
              This field is a member of `oneof`_ ``destination``.
          bigquery_destination (google.cloud.automl_v1beta1.types.BigQueryDestination):
              The BigQuery location where the output is to
              be written to.
  
              This field is a member of `oneof`_ ``destination``.
      """
  
      gcs_destination = proto.Field(
          proto.MESSAGE, number=1, oneof="destination", message="GcsDestination",
      )
      bigquery_destination = proto.Field(
          proto.MESSAGE, number=2, oneof="destination", message="BigQueryDestination",
      )
  
  
  class ModelExportOutputConfig(proto.Message):
      r"""Output configuration for ModelExport Action.
  
      This message has `oneof`_ fields (mutually exclusive fields).
      For each oneof, at most one member field can be set at the same time.
      Setting any member of the oneof automatically clears all other
      members.
  
      .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
  
      Attributes:
          gcs_destination (google.cloud.automl_v1beta1.types.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--", 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.
  
              This field is a member of `oneof`_ ``destination``.
          gcr_destination (google.cloud.automl_v1beta1.types.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.
  
              This field is a member of `oneof`_ ``destination``.
          model_format (str):
              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.
          params (Sequence[google.cloud.automl_v1beta1.types.ModelExportOutputConfig.ParamsEntry]):
              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".
      """
  
      gcs_destination = proto.Field(
          proto.MESSAGE, number=1, oneof="destination", message="GcsDestination",
      )
      gcr_destination = proto.Field(
          proto.MESSAGE, number=3, oneof="destination", message="GcrDestination",
      )
      model_format = proto.Field(proto.STRING, number=4,)
      params = proto.MapField(proto.STRING, proto.STRING, number=2,)
  
  
  class ExportEvaluatedExamplesOutputConfig(proto.Message):
      r"""Output configuration for ExportEvaluatedExamples Action. Note that
      this call is available only for 30 days since the moment the model
      was evaluated. The output depends on the domain, as follows (note
      that only examples from the TEST set are exported):
  
      -  For Tables:
  
      [bigquery_destination][google.cloud.automl.v1beta1.OutputConfig.bigquery_destination]
      pointing to a BigQuery project must be set. In the given project a
      new dataset will be created with name
  
      ``export_evaluated_examples_<model-display-name>_<timestamp-of-export-call>``
      where will be made BigQuery-dataset-name compatible (e.g. most
      special characters will become underscores), and timestamp will be
      in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the
      dataset an ``evaluated_examples`` table will be created. It will
      have all the same columns as the
  
      [primary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id]
      of the [dataset][google.cloud.automl.v1beta1.Model.dataset_id] from
      which the model was created, as they were at the moment of model's
      evaluation (this includes the target column with its ground truth),
      followed by a column called "predicted_<target_column>". That last
      column will contain the model's prediction result for each
      respective row, given as ARRAY of
      [AnnotationPayloads][google.cloud.automl.v1beta1.AnnotationPayload],
      represented as STRUCT-s, containing
      [TablesAnnotation][google.cloud.automl.v1beta1.TablesAnnotation].
  
  
      .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields
  
      Attributes:
          bigquery_destination (google.cloud.automl_v1beta1.types.BigQueryDestination):
              The BigQuery location where the output is to
              be written to.
  
              This field is a member of `oneof`_ ``destination``.
      """
  
      bigquery_destination = proto.Field(
          proto.MESSAGE, number=2, oneof="destination", message="BigQueryDestination",
      )
  
  
  class GcsSource(proto.Message):
      r"""The Google Cloud Storage location for the input content.
  
      Attributes:
          input_uris (Sequence[str]):
              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
      """
  
      input_uris = proto.RepeatedField(proto.STRING, number=1,)
  
  
  class BigQuerySource(proto.Message):
      r"""The BigQuery location for the input content.
  
      Attributes:
          input_uri (str):
              Required. BigQuery URI to a table, up to 2000 characters
              long. Accepted forms:
  
              -  BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId
      """
  
      input_uri = proto.Field(proto.STRING, number=1,)
  
  
  class GcsDestination(proto.Message):
      r"""The Google Cloud Storage location where the output is to be
      written to.
  
      Attributes:
          output_uri_prefix (str):
              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.
      """
  
      output_uri_prefix = proto.Field(proto.STRING, number=1,)
  
  
  class BigQueryDestination(proto.Message):
      r"""The BigQuery location for the output content.
  
      Attributes:
          output_uri (str):
              Required. BigQuery URI to a project, up to 2000 characters
              long. Accepted forms:
  
              -  BigQuery path e.g. bq://projectId
      """
  
      output_uri = proto.Field(proto.STRING, number=1,)
  
  
  class GcrDestination(proto.Message):
      r"""The GCR location where the image must be pushed to.
  
      Attributes:
          output_uri (str):
              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.
      """
  
      output_uri = proto.Field(proto.STRING, number=1,)
  
  
  __all__ = tuple(sorted(__protobuf__.manifest))
  