xref: /dports/net/google-cloud-sdk/google-cloud-sdk/lib/googlecloudsdk/third_party/apis/aiplatform/v1/aiplatform_v1_messages.py

"""Generated message classes for aiplatform version v1.

Train high-quality custom machine learning models with minimal machine
learning expertise and effort.
"""
# NOTE: This file is autogenerated and should not be edited by hand.

from __future__ import absolute_import

from apitools.base.protorpclite import messages as _messages
from apitools.base.py import encoding
from apitools.base.py import extra_types


package = 'aiplatform'


class AiplatformProjectsLocationsBatchPredictionJobsCancelRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsBatchPredictionJobsCancelRequest object.

  Fields:
    googleCloudAiplatformV1CancelBatchPredictionJobRequest: A
      GoogleCloudAiplatformV1CancelBatchPredictionJobRequest resource to be
      passed as the request body.
    name: Required. The name of the BatchPredictionJob to cancel. Format: `pro
      jects/{project}/locations/{location}/batchPredictionJobs/{batch_predicti
      on_job}`
  """

  googleCloudAiplatformV1CancelBatchPredictionJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelBatchPredictionJobRequest', 1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsBatchPredictionJobsCreateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsBatchPredictionJobsCreateRequest object.

  Fields:
    googleCloudAiplatformV1BatchPredictionJob: A
      GoogleCloudAiplatformV1BatchPredictionJob resource to be passed as the
      request body.
    parent: Required. The resource name of the Location to create the
      BatchPredictionJob in. Format: `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1BatchPredictionJob = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJob', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsBatchPredictionJobsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsBatchPredictionJobsDeleteRequest object.

  Fields:
    name: Required. The name of the BatchPredictionJob resource to be deleted.
      Format: `projects/{project}/locations/{location}/batchPredictionJobs/{ba
      tch_prediction_job}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsBatchPredictionJobsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsBatchPredictionJobsGetRequest object.

  Fields:
    name: Required. The name of the BatchPredictionJob resource. Format: `proj
      ects/{project}/locations/{location}/batchPredictionJobs/{batch_predictio
      n_job}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsBatchPredictionJobsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsBatchPredictionJobsListRequest object.

  Fields:
    filter: The standard list filter. Supported fields: * `display_name`
      supports = and !=. * `state` supports = and !=. * `model_display_name`
      supports = and != Some examples of using the filter are: *
      `state="JOB_STATE_SUCCEEDED" AND display_name="my_job"` *
      `state="JOB_STATE_RUNNING" OR display_name="my_job"` * `NOT
      display_name="my_job"` * `state="JOB_STATE_FAILED"`
    pageSize: The standard list page size.
    pageToken: The standard list page token. Typically obtained via
      ListBatchPredictionJobsResponse.next_page_token of the previous
      JobService.ListBatchPredictionJobs call.
    parent: Required. The resource name of the Location to list the
      BatchPredictionJobs from. Format:
      `projects/{project}/locations/{location}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(3)
  parent = _messages.StringField(4, required=True)
  readMask = _messages.StringField(5)


class AiplatformProjectsLocationsCustomJobsCancelRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsCustomJobsCancelRequest object.

  Fields:
    googleCloudAiplatformV1CancelCustomJobRequest: A
      GoogleCloudAiplatformV1CancelCustomJobRequest resource to be passed as
      the request body.
    name: Required. The name of the CustomJob to cancel. Format:
      `projects/{project}/locations/{location}/customJobs/{custom_job}`
  """

  googleCloudAiplatformV1CancelCustomJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelCustomJobRequest', 1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsCustomJobsCreateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsCustomJobsCreateRequest object.

  Fields:
    googleCloudAiplatformV1CustomJob: A GoogleCloudAiplatformV1CustomJob
      resource to be passed as the request body.
    parent: Required. The resource name of the Location to create the
      CustomJob in. Format: `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1CustomJob = _messages.MessageField('GoogleCloudAiplatformV1CustomJob', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsCustomJobsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsCustomJobsDeleteRequest object.

  Fields:
    name: Required. The name of the CustomJob resource to be deleted. Format:
      `projects/{project}/locations/{location}/customJobs/{custom_job}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsCustomJobsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsCustomJobsGetRequest object.

  Fields:
    name: Required. The name of the CustomJob resource. Format:
      `projects/{project}/locations/{location}/customJobs/{custom_job}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsCustomJobsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsCustomJobsListRequest object.

  Fields:
    filter: The standard list filter. Supported fields: * `display_name`
      supports = and !=. * `state` supports = and !=. Some examples of using
      the filter are: * `state="JOB_STATE_SUCCEEDED" AND
      display_name="my_job"` * `state="JOB_STATE_RUNNING" OR
      display_name="my_job"` * `NOT display_name="my_job"` *
      `state="JOB_STATE_FAILED"`
    pageSize: The standard list page size.
    pageToken: The standard list page token. Typically obtained via
      ListCustomJobsResponse.next_page_token of the previous
      JobService.ListCustomJobs call.
    parent: Required. The resource name of the Location to list the CustomJobs
      from. Format: `projects/{project}/locations/{location}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(3)
  parent = _messages.StringField(4, required=True)
  readMask = _messages.StringField(5)


class AiplatformProjectsLocationsDataLabelingJobsCancelRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDataLabelingJobsCancelRequest object.

  Fields:
    googleCloudAiplatformV1CancelDataLabelingJobRequest: A
      GoogleCloudAiplatformV1CancelDataLabelingJobRequest resource to be
      passed as the request body.
    name: Required. The name of the DataLabelingJob. Format: `projects/{projec
      t}/locations/{location}/dataLabelingJobs/{data_labeling_job}`
  """

  googleCloudAiplatformV1CancelDataLabelingJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelDataLabelingJobRequest', 1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsDataLabelingJobsCreateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDataLabelingJobsCreateRequest object.

  Fields:
    googleCloudAiplatformV1DataLabelingJob: A
      GoogleCloudAiplatformV1DataLabelingJob resource to be passed as the
      request body.
    parent: Required. The parent of the DataLabelingJob. Format:
      `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1DataLabelingJob = _messages.MessageField('GoogleCloudAiplatformV1DataLabelingJob', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsDataLabelingJobsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDataLabelingJobsDeleteRequest object.

  Fields:
    name: Required. The name of the DataLabelingJob to be deleted. Format: `pr
      ojects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_jo
      b}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsDataLabelingJobsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDataLabelingJobsGetRequest object.

  Fields:
    name: Required. The name of the DataLabelingJob. Format: `projects/{projec
      t}/locations/{location}/dataLabelingJobs/{data_labeling_job}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsDataLabelingJobsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDataLabelingJobsListRequest object.

  Fields:
    filter: The standard list filter. Supported fields: * `display_name`
      supports = and !=. * `state` supports = and !=. Some examples of using
      the filter are: * `state="JOB_STATE_SUCCEEDED" AND
      display_name="my_job"` * `state="JOB_STATE_RUNNING" OR
      display_name="my_job"` * `NOT display_name="my_job"` *
      `state="JOB_STATE_FAILED"`
    orderBy: A comma-separated list of fields to order by, sorted in ascending
      order by default. Use `desc` after a field name for descending.
    pageSize: The standard list page size.
    pageToken: The standard list page token.
    parent: Required. The parent of the DataLabelingJob. Format:
      `projects/{project}/locations/{location}`
    readMask: Mask specifying which fields to read. FieldMask represents a set
      of symbolic field paths. For example, the mask can be `paths: "name"`.
      The "name" here is a field in DataLabelingJob. If this field is not set,
      all fields of the DataLabelingJob are returned.
  """

  filter = _messages.StringField(1)
  orderBy = _messages.StringField(2)
  pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(4)
  parent = _messages.StringField(5, required=True)
  readMask = _messages.StringField(6)


class AiplatformProjectsLocationsDatasetsAnnotationSpecsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsAnnotationSpecsGetRequest object.

  Fields:
    name: Required. The name of the AnnotationSpec resource. Format: `projects
      /{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{anno
      tation_spec}`
    readMask: Mask specifying which fields to read.
  """

  name = _messages.StringField(1, required=True)
  readMask = _messages.StringField(2)


class AiplatformProjectsLocationsDatasetsCreateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsCreateRequest object.

  Fields:
    googleCloudAiplatformV1Dataset: A GoogleCloudAiplatformV1Dataset resource
      to be passed as the request body.
    parent: Required. The resource name of the Location to create the Dataset
      in. Format: `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1Dataset = _messages.MessageField('GoogleCloudAiplatformV1Dataset', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsDatasetsDataItemsAnnotationsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsDataItemsAnnotationsListRequest
  object.

  Fields:
    filter: The standard list filter.
    orderBy: A comma-separated list of fields to order by, sorted in ascending
      order. Use "desc" after a field name for descending.
    pageSize: The standard list page size.
    pageToken: The standard list page token.
    parent: Required. The resource name of the DataItem to list Annotations
      from. Format: `projects/{project}/locations/{location}/datasets/{dataset
      }/dataItems/{data_item}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  orderBy = _messages.StringField(2)
  pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(4)
  parent = _messages.StringField(5, required=True)
  readMask = _messages.StringField(6)


class AiplatformProjectsLocationsDatasetsDataItemsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsDataItemsListRequest object.

  Fields:
    filter: The standard list filter.
    orderBy: A comma-separated list of fields to order by, sorted in ascending
      order. Use "desc" after a field name for descending.
    pageSize: The standard list page size.
    pageToken: The standard list page token.
    parent: Required. The resource name of the Dataset to list DataItems from.
      Format: `projects/{project}/locations/{location}/datasets/{dataset}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  orderBy = _messages.StringField(2)
  pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(4)
  parent = _messages.StringField(5, required=True)
  readMask = _messages.StringField(6)


class AiplatformProjectsLocationsDatasetsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsDeleteRequest object.

  Fields:
    name: Required. The resource name of the Dataset to delete. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsDatasetsExportRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsExportRequest object.

  Fields:
    googleCloudAiplatformV1ExportDataRequest: A
      GoogleCloudAiplatformV1ExportDataRequest resource to be passed as the
      request body.
    name: Required. The name of the Dataset resource. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`
  """

  googleCloudAiplatformV1ExportDataRequest = _messages.MessageField('GoogleCloudAiplatformV1ExportDataRequest', 1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsDatasetsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsGetRequest object.

  Fields:
    name: Required. The name of the Dataset resource.
    readMask: Mask specifying which fields to read.
  """

  name = _messages.StringField(1, required=True)
  readMask = _messages.StringField(2)


class AiplatformProjectsLocationsDatasetsImportRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsImportRequest object.

  Fields:
    googleCloudAiplatformV1ImportDataRequest: A
      GoogleCloudAiplatformV1ImportDataRequest resource to be passed as the
      request body.
    name: Required. The name of the Dataset resource. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`
  """

  googleCloudAiplatformV1ImportDataRequest = _messages.MessageField('GoogleCloudAiplatformV1ImportDataRequest', 1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsDatasetsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsListRequest object.

  Fields:
    filter: An expression for filtering the results of the request. For field
      names both snake_case and camelCase are supported. * `display_name`:
      supports = and != * `metadata_schema_uri`: supports = and != * `labels`
      supports general map functions that is: * `labels.key=value` - key:value
      equality * `labels.key:* or labels:key - key existence * A key including
      a space must be quoted. `labels."a key"`. Some examples: *
      `displayName="myDisplayName"` * `labels.myKey="myValue"`
    orderBy: A comma-separated list of fields to order by, sorted in ascending
      order. Use "desc" after a field name for descending. Supported fields: *
      `display_name` * `create_time` * `update_time`
    pageSize: The standard list page size.
    pageToken: The standard list page token.
    parent: Required. The name of the Dataset's parent resource. Format:
      `projects/{project}/locations/{location}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  orderBy = _messages.StringField(2)
  pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(4)
  parent = _messages.StringField(5, required=True)
  readMask = _messages.StringField(6)


class AiplatformProjectsLocationsDatasetsPatchRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsDatasetsPatchRequest object.

  Fields:
    googleCloudAiplatformV1Dataset: A GoogleCloudAiplatformV1Dataset resource
      to be passed as the request body.
    name: Output only. The resource name of the Dataset.
    updateMask: Required. The update mask applies to the resource. For the
      `FieldMask` definition, see
      [FieldMask](https://tinyurl.com/protobufs/google.protobuf#fieldmask).
      Updatable fields: * `display_name` * `description` * `labels`
  """

  googleCloudAiplatformV1Dataset = _messages.MessageField('GoogleCloudAiplatformV1Dataset', 1)
  name = _messages.StringField(2, required=True)
  updateMask = _messages.StringField(3)


class AiplatformProjectsLocationsEndpointsCreateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsEndpointsCreateRequest object.

  Fields:
    googleCloudAiplatformV1Endpoint: A GoogleCloudAiplatformV1Endpoint
      resource to be passed as the request body.
    parent: Required. The resource name of the Location to create the Endpoint
      in. Format: `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1Endpoint = _messages.MessageField('GoogleCloudAiplatformV1Endpoint', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsEndpointsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsEndpointsDeleteRequest object.

  Fields:
    name: Required. The name of the Endpoint resource to be deleted. Format:
      `projects/{project}/locations/{location}/endpoints/{endpoint}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsEndpointsDeployModelRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsEndpointsDeployModelRequest object.

  Fields:
    endpoint: Required. The name of the Endpoint resource into which to deploy
      a Model. Format:
      `projects/{project}/locations/{location}/endpoints/{endpoint}`
    googleCloudAiplatformV1DeployModelRequest: A
      GoogleCloudAiplatformV1DeployModelRequest resource to be passed as the
      request body.
  """

  endpoint = _messages.StringField(1, required=True)
  googleCloudAiplatformV1DeployModelRequest = _messages.MessageField('GoogleCloudAiplatformV1DeployModelRequest', 2)


class AiplatformProjectsLocationsEndpointsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsEndpointsGetRequest object.

  Fields:
    name: Required. The name of the Endpoint resource. Format:
      `projects/{project}/locations/{location}/endpoints/{endpoint}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsEndpointsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsEndpointsListRequest object.

  Fields:
    filter: Optional. An expression for filtering the results of the request.
      For field names both snake_case and camelCase are supported. *
      `endpoint` supports = and !=. `endpoint` represents the Endpoint ID,
      i.e. the last segment of the Endpoint's resource name. * `display_name`
      supports = and, != * `labels` supports general map functions that is: *
      `labels.key=value` - key:value equality * `labels.key:* or labels:key -
      key existence * A key including a space must be quoted. `labels."a
      key"`. Some examples: * `endpoint=1` * `displayName="myDisplayName"` *
      `labels.myKey="myValue"`
    orderBy: A comma-separated list of fields to order by, sorted in ascending
      order. Use "desc" after a field name for descending. Supported fields: *
      `display_name` * `create_time` * `update_time` Example: `display_name,
      create_time desc`.
    pageSize: Optional. The standard list page size.
    pageToken: Optional. The standard list page token. Typically obtained via
      ListEndpointsResponse.next_page_token of the previous
      EndpointService.ListEndpoints call.
    parent: Required. The resource name of the Location from which to list the
      Endpoints. Format: `projects/{project}/locations/{location}`
    readMask: Optional. Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  orderBy = _messages.StringField(2)
  pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(4)
  parent = _messages.StringField(5, required=True)
  readMask = _messages.StringField(6)


class AiplatformProjectsLocationsEndpointsPatchRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsEndpointsPatchRequest object.

  Fields:
    googleCloudAiplatformV1Endpoint: A GoogleCloudAiplatformV1Endpoint
      resource to be passed as the request body.
    name: Output only. The resource name of the Endpoint.
    updateMask: Required. The update mask applies to the resource. See
      [FieldMask](https://tinyurl.com/protobufs/google.protobuf#fieldmask).
  """

  googleCloudAiplatformV1Endpoint = _messages.MessageField('GoogleCloudAiplatformV1Endpoint', 1)
  name = _messages.StringField(2, required=True)
  updateMask = _messages.StringField(3)


class AiplatformProjectsLocationsEndpointsPredictRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsEndpointsPredictRequest object.

  Fields:
    endpoint: Required. The name of the Endpoint requested to serve the
      prediction. Format:
      `projects/{project}/locations/{location}/endpoints/{endpoint}`
    googleCloudAiplatformV1PredictRequest: A
      GoogleCloudAiplatformV1PredictRequest resource to be passed as the
      request body.
  """

  endpoint = _messages.StringField(1, required=True)
  googleCloudAiplatformV1PredictRequest = _messages.MessageField('GoogleCloudAiplatformV1PredictRequest', 2)


class AiplatformProjectsLocationsEndpointsUndeployModelRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsEndpointsUndeployModelRequest object.

  Fields:
    endpoint: Required. The name of the Endpoint resource from which to
      undeploy a Model. Format:
      `projects/{project}/locations/{location}/endpoints/{endpoint}`
    googleCloudAiplatformV1UndeployModelRequest: A
      GoogleCloudAiplatformV1UndeployModelRequest resource to be passed as the
      request body.
  """

  endpoint = _messages.StringField(1, required=True)
  googleCloudAiplatformV1UndeployModelRequest = _messages.MessageField('GoogleCloudAiplatformV1UndeployModelRequest', 2)


class AiplatformProjectsLocationsHyperparameterTuningJobsCancelRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsHyperparameterTuningJobsCancelRequest
  object.

  Fields:
    googleCloudAiplatformV1CancelHyperparameterTuningJobRequest: A
      GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest resource to
      be passed as the request body.
    name: Required. The name of the HyperparameterTuningJob to cancel. Format:
      `projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyper
      parameter_tuning_job}`
  """

  googleCloudAiplatformV1CancelHyperparameterTuningJobRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest', 1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsHyperparameterTuningJobsCreateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsHyperparameterTuningJobsCreateRequest
  object.

  Fields:
    googleCloudAiplatformV1HyperparameterTuningJob: A
      GoogleCloudAiplatformV1HyperparameterTuningJob resource to be passed as
      the request body.
    parent: Required. The resource name of the Location to create the
      HyperparameterTuningJob in. Format:
      `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1HyperparameterTuningJob = _messages.MessageField('GoogleCloudAiplatformV1HyperparameterTuningJob', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsHyperparameterTuningJobsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsHyperparameterTuningJobsDeleteRequest
  object.

  Fields:
    name: Required. The name of the HyperparameterTuningJob resource to be
      deleted. Format: `projects/{project}/locations/{location}/hyperparameter
      TuningJobs/{hyperparameter_tuning_job}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsHyperparameterTuningJobsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsHyperparameterTuningJobsGetRequest object.

  Fields:
    name: Required. The name of the HyperparameterTuningJob resource. Format:
      `projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyper
      parameter_tuning_job}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsHyperparameterTuningJobsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsHyperparameterTuningJobsListRequest object.

  Fields:
    filter: The standard list filter. Supported fields: * `display_name`
      supports = and !=. * `state` supports = and !=. Some examples of using
      the filter are: * `state="JOB_STATE_SUCCEEDED" AND
      display_name="my_job"` * `state="JOB_STATE_RUNNING" OR
      display_name="my_job"` * `NOT display_name="my_job"` *
      `state="JOB_STATE_FAILED"`
    pageSize: The standard list page size.
    pageToken: The standard list page token. Typically obtained via
      ListHyperparameterTuningJobsResponse.next_page_token of the previous
      JobService.ListHyperparameterTuningJobs call.
    parent: Required. The resource name of the Location to list the
      HyperparameterTuningJobs from. Format:
      `projects/{project}/locations/{location}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(3)
  parent = _messages.StringField(4, required=True)
  readMask = _messages.StringField(5)


class AiplatformProjectsLocationsMigratableResourcesBatchMigrateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsMigratableResourcesBatchMigrateRequest
  object.

  Fields:
    googleCloudAiplatformV1BatchMigrateResourcesRequest: A
      GoogleCloudAiplatformV1BatchMigrateResourcesRequest resource to be
      passed as the request body.
    parent: Required. The location of the migrated resource will live in.
      Format: `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1BatchMigrateResourcesRequest = _messages.MessageField('GoogleCloudAiplatformV1BatchMigrateResourcesRequest', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsMigratableResourcesSearchRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsMigratableResourcesSearchRequest object.

  Fields:
    googleCloudAiplatformV1SearchMigratableResourcesRequest: A
      GoogleCloudAiplatformV1SearchMigratableResourcesRequest resource to be
      passed as the request body.
    parent: Required. The location that the migratable resources should be
      searched from. It's the AI Platform location that the resources can be
      migrated to, not the resources' original location. Format:
      `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1SearchMigratableResourcesRequest = _messages.MessageField('GoogleCloudAiplatformV1SearchMigratableResourcesRequest', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsModelsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsDeleteRequest object.

  Fields:
    name: Required. The name of the Model resource to be deleted. Format:
      `projects/{project}/locations/{location}/models/{model}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsModelsEvaluationsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsEvaluationsGetRequest object.

  Fields:
    name: Required. The name of the ModelEvaluation resource. Format: `project
      s/{project}/locations/{location}/models/{model}/evaluations/{evaluation}
      `
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsModelsEvaluationsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsEvaluationsListRequest object.

  Fields:
    filter: The standard list filter.
    pageSize: The standard list page size.
    pageToken: The standard list page token. Typically obtained via
      ListModelEvaluationsResponse.next_page_token of the previous
      ModelService.ListModelEvaluations call.
    parent: Required. The resource name of the Model to list the
      ModelEvaluations from. Format:
      `projects/{project}/locations/{location}/models/{model}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(3)
  parent = _messages.StringField(4, required=True)
  readMask = _messages.StringField(5)


class AiplatformProjectsLocationsModelsEvaluationsSlicesGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsEvaluationsSlicesGetRequest object.

  Fields:
    name: Required. The name of the ModelEvaluationSlice resource. Format: `pr
      ojects/{project}/locations/{location}/models/{model}/evaluations/{evalua
      tion}/slices/{slice}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsModelsEvaluationsSlicesListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsEvaluationsSlicesListRequest object.

  Fields:
    filter: The standard list filter. * `slice.dimension` - for =.
    pageSize: The standard list page size.
    pageToken: The standard list page token. Typically obtained via
      ListModelEvaluationSlicesResponse.next_page_token of the previous
      ModelService.ListModelEvaluationSlices call.
    parent: Required. The resource name of the ModelEvaluation to list the
      ModelEvaluationSlices from. Format: `projects/{project}/locations/{locat
      ion}/models/{model}/evaluations/{evaluation}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(3)
  parent = _messages.StringField(4, required=True)
  readMask = _messages.StringField(5)


class AiplatformProjectsLocationsModelsExportRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsExportRequest object.

  Fields:
    googleCloudAiplatformV1ExportModelRequest: A
      GoogleCloudAiplatformV1ExportModelRequest resource to be passed as the
      request body.
    name: Required. The resource name of the Model to export. Format:
      `projects/{project}/locations/{location}/models/{model}`
  """

  googleCloudAiplatformV1ExportModelRequest = _messages.MessageField('GoogleCloudAiplatformV1ExportModelRequest', 1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsModelsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsGetRequest object.

  Fields:
    name: Required. The name of the Model resource. Format:
      `projects/{project}/locations/{location}/models/{model}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsModelsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsListRequest object.

  Fields:
    filter: An expression for filtering the results of the request. For field
      names both snake_case and camelCase are supported. * `model` supports =
      and !=. `model` represents the Model ID, i.e. the last segment of the
      Model's resource name. * `display_name` supports = and != * `labels`
      supports general map functions that is: * `labels.key=value` - key:value
      equality * `labels.key:* or labels:key - key existence * A key including
      a space must be quoted. `labels."a key"`. Some examples: * `model=1234`
      * `displayName="myDisplayName"` * `labels.myKey="myValue"`
    orderBy: A comma-separated list of fields to order by, sorted in ascending
      order. Use "desc" after a field name for descending. Supported fields: *
      `display_name` * `create_time` * `update_time` Example: `display_name,
      create_time desc`.
    pageSize: The standard list page size.
    pageToken: The standard list page token. Typically obtained via
      ListModelsResponse.next_page_token of the previous
      ModelService.ListModels call.
    parent: Required. The resource name of the Location to list the Models
      from. Format: `projects/{project}/locations/{location}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  orderBy = _messages.StringField(2)
  pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(4)
  parent = _messages.StringField(5, required=True)
  readMask = _messages.StringField(6)


class AiplatformProjectsLocationsModelsPatchRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsPatchRequest object.

  Fields:
    googleCloudAiplatformV1Model: A GoogleCloudAiplatformV1Model resource to
      be passed as the request body.
    name: The resource name of the Model.
    updateMask: Required. The update mask applies to the resource. For the
      `FieldMask` definition, see
      [FieldMask](https://tinyurl.com/protobufs/google.protobuf#fieldmask).
  """

  googleCloudAiplatformV1Model = _messages.MessageField('GoogleCloudAiplatformV1Model', 1)
  name = _messages.StringField(2, required=True)
  updateMask = _messages.StringField(3)


class AiplatformProjectsLocationsModelsUploadRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsModelsUploadRequest object.

  Fields:
    googleCloudAiplatformV1UploadModelRequest: A
      GoogleCloudAiplatformV1UploadModelRequest resource to be passed as the
      request body.
    parent: Required. The resource name of the Location into which to upload
      the Model. Format: `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1UploadModelRequest = _messages.MessageField('GoogleCloudAiplatformV1UploadModelRequest', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsOperationsCancelRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsOperationsCancelRequest object.

  Fields:
    name: The name of the operation resource to be cancelled.
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsOperationsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsOperationsDeleteRequest object.

  Fields:
    name: The name of the operation resource to be deleted.
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsOperationsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsOperationsGetRequest object.

  Fields:
    name: The name of the operation resource.
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsOperationsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsOperationsListRequest object.

  Fields:
    filter: The standard list filter.
    name: The name of the operation's parent resource.
    pageSize: The standard list page size.
    pageToken: The standard list page token.
  """

  filter = _messages.StringField(1)
  name = _messages.StringField(2, required=True)
  pageSize = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(4)


class AiplatformProjectsLocationsOperationsWaitRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsOperationsWaitRequest object.

  Fields:
    name: The name of the operation resource to wait on.
    timeout: The maximum duration to wait before timing out. If left blank,
      the wait will be at most the time permitted by the underlying HTTP/RPC
      protocol. If RPC context deadline is also specified, the shorter one
      will be used.
  """

  name = _messages.StringField(1, required=True)
  timeout = _messages.StringField(2)


class AiplatformProjectsLocationsSpecialistPoolsCreateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsSpecialistPoolsCreateRequest object.

  Fields:
    googleCloudAiplatformV1SpecialistPool: A
      GoogleCloudAiplatformV1SpecialistPool resource to be passed as the
      request body.
    parent: Required. The parent Project name for the new SpecialistPool. The
      form is `projects/{project}/locations/{location}`.
  """

  googleCloudAiplatformV1SpecialistPool = _messages.MessageField('GoogleCloudAiplatformV1SpecialistPool', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsSpecialistPoolsDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsSpecialistPoolsDeleteRequest object.

  Fields:
    force: If set to true, any specialist managers in this SpecialistPool will
      also be deleted. (Otherwise, the request will only work if the
      SpecialistPool has no specialist managers.)
    name: Required. The resource name of the SpecialistPool to delete. Format:
      `projects/{project}/locations/{location}/specialistPools/{specialist_poo
      l}`
  """

  force = _messages.BooleanField(1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsSpecialistPoolsGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsSpecialistPoolsGetRequest object.

  Fields:
    name: Required. The name of the SpecialistPool resource. The form is `proj
      ects/{project}/locations/{location}/specialistPools/{specialist_pool}`.
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsSpecialistPoolsListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsSpecialistPoolsListRequest object.

  Fields:
    pageSize: The standard list page size.
    pageToken: The standard list page token. Typically obtained by
      ListSpecialistPoolsResponse.next_page_token of the previous
      SpecialistPoolService.ListSpecialistPools call. Return first page if
      empty.
    parent: Required. The name of the SpecialistPool's parent resource.
      Format: `projects/{project}/locations/{location}`
    readMask: Mask specifying which fields to read. FieldMask represents a set
      of
  """

  pageSize = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(2)
  parent = _messages.StringField(3, required=True)
  readMask = _messages.StringField(4)


class AiplatformProjectsLocationsSpecialistPoolsPatchRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsSpecialistPoolsPatchRequest object.

  Fields:
    googleCloudAiplatformV1SpecialistPool: A
      GoogleCloudAiplatformV1SpecialistPool resource to be passed as the
      request body.
    name: Required. The resource name of the SpecialistPool.
    updateMask: Required. The update mask applies to the resource.
  """

  googleCloudAiplatformV1SpecialistPool = _messages.MessageField('GoogleCloudAiplatformV1SpecialistPool', 1)
  name = _messages.StringField(2, required=True)
  updateMask = _messages.StringField(3)


class AiplatformProjectsLocationsTrainingPipelinesCancelRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsTrainingPipelinesCancelRequest object.

  Fields:
    googleCloudAiplatformV1CancelTrainingPipelineRequest: A
      GoogleCloudAiplatformV1CancelTrainingPipelineRequest resource to be
      passed as the request body.
    name: Required. The name of the TrainingPipeline to cancel. Format: `proje
      cts/{project}/locations/{location}/trainingPipelines/{training_pipeline}
      `
  """

  googleCloudAiplatformV1CancelTrainingPipelineRequest = _messages.MessageField('GoogleCloudAiplatformV1CancelTrainingPipelineRequest', 1)
  name = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsTrainingPipelinesCreateRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsTrainingPipelinesCreateRequest object.

  Fields:
    googleCloudAiplatformV1TrainingPipeline: A
      GoogleCloudAiplatformV1TrainingPipeline resource to be passed as the
      request body.
    parent: Required. The resource name of the Location to create the
      TrainingPipeline in. Format: `projects/{project}/locations/{location}`
  """

  googleCloudAiplatformV1TrainingPipeline = _messages.MessageField('GoogleCloudAiplatformV1TrainingPipeline', 1)
  parent = _messages.StringField(2, required=True)


class AiplatformProjectsLocationsTrainingPipelinesDeleteRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsTrainingPipelinesDeleteRequest object.

  Fields:
    name: Required. The name of the TrainingPipeline resource to be deleted.
      Format: `projects/{project}/locations/{location}/trainingPipelines/{trai
      ning_pipeline}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsTrainingPipelinesGetRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsTrainingPipelinesGetRequest object.

  Fields:
    name: Required. The name of the TrainingPipeline resource. Format: `projec
      ts/{project}/locations/{location}/trainingPipelines/{training_pipeline}`
  """

  name = _messages.StringField(1, required=True)


class AiplatformProjectsLocationsTrainingPipelinesListRequest(_messages.Message):
  r"""A AiplatformProjectsLocationsTrainingPipelinesListRequest object.

  Fields:
    filter: The standard list filter. Supported fields: * `display_name`
      supports = and !=. * `state` supports = and !=. Some examples of using
      the filter are: * `state="PIPELINE_STATE_SUCCEEDED" AND
      display_name="my_pipeline"` * `state="PIPELINE_STATE_RUNNING" OR
      display_name="my_pipeline"` * `NOT display_name="my_pipeline"` *
      `state="PIPELINE_STATE_FAILED"`
    pageSize: The standard list page size.
    pageToken: The standard list page token. Typically obtained via
      ListTrainingPipelinesResponse.next_page_token of the previous
      PipelineService.ListTrainingPipelines call.
    parent: Required. The resource name of the Location to list the
      TrainingPipelines from. Format:
      `projects/{project}/locations/{location}`
    readMask: Mask specifying which fields to read.
  """

  filter = _messages.StringField(1)
  pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(3)
  parent = _messages.StringField(4, required=True)
  readMask = _messages.StringField(5)


class GoogleCloudAiplatformV1ActiveLearningConfig(_messages.Message):
  r"""Parameters that configure the active learning pipeline. Active learning
  will label the data incrementally by several iterations. For every
  iteration, it will select a batch of data based on the sampling strategy.

  Fields:
    maxDataItemCount: Max number of human labeled DataItems.
    maxDataItemPercentage: Max percent of total DataItems for human labeling.
    sampleConfig: Active learning data sampling config. For every active
      learning labeling iteration, it will select a batch of data based on the
      sampling strategy.
    trainingConfig: CMLE training config. For every active learning labeling
      iteration, system will train a machine learning model on CMLE. The
      trained model will be used by data sampling algorithm to select
      DataItems.
  """

  maxDataItemCount = _messages.IntegerField(1)
  maxDataItemPercentage = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  sampleConfig = _messages.MessageField('GoogleCloudAiplatformV1SampleConfig', 3)
  trainingConfig = _messages.MessageField('GoogleCloudAiplatformV1TrainingConfig', 4)


class GoogleCloudAiplatformV1Annotation(_messages.Message):
  r"""Used to assign specific AnnotationSpec to a particular area of a
  DataItem or the whole part of the DataItem.

  Messages:
    LabelsValue: Optional. The labels with user-defined metadata to organize
      your Annotations. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. No more than 64 user labels can be associated with one
      Annotation(System labels are excluded). See https://goo.gl/xmQnxf for
      more information and examples of labels. System reserved label keys are
      prefixed with "aiplatform.googleapis.com/" and are immutable. Following
      system labels exist for each Annotation: *
      "aiplatform.googleapis.com/annotation_set_name": optional, name of the
      UI's annotation set this Annotation belongs to. If not set, the
      Annotation is not visible in the UI. *
      "aiplatform.googleapis.com/payload_schema": output only, its value is
      the payload_schema's title.

  Fields:
    annotationSource: Output only. The source of the Annotation.
    createTime: Output only. Timestamp when this Annotation was created.
    etag: Optional. Used to perform consistent read-modify-write updates. If
      not set, a blind "overwrite" update happens.
    labels: Optional. The labels with user-defined metadata to organize your
      Annotations. Label keys and values can be no longer than 64 characters
      (Unicode codepoints), can only contain lowercase letters, numeric
      characters, underscores and dashes. International characters are
      allowed. No more than 64 user labels can be associated with one
      Annotation(System labels are excluded). See https://goo.gl/xmQnxf for
      more information and examples of labels. System reserved label keys are
      prefixed with "aiplatform.googleapis.com/" and are immutable. Following
      system labels exist for each Annotation: *
      "aiplatform.googleapis.com/annotation_set_name": optional, name of the
      UI's annotation set this Annotation belongs to. If not set, the
      Annotation is not visible in the UI. *
      "aiplatform.googleapis.com/payload_schema": output only, its value is
      the payload_schema's title.
    name: Output only. Resource name of the Annotation.
    payload: Required. The schema of the payload can be found in
      payload_schema.
    payloadSchemaUri: Required. Google Cloud Storage URI points to a YAML file
      describing payload. The schema is defined as an [OpenAPI 3.0.2 Schema
      Object](https://tinyurl.com/y538mdwt). The schema files that can be used
      here are found in gs://google-cloud-
      aiplatform/schema/dataset/annotation/, note that the chosen schema must
      be consistent with the parent Dataset's metadata.
    updateTime: Output only. Timestamp when this Annotation was last updated.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""Optional. The labels with user-defined metadata to organize your
    Annotations. Label keys and values can be no longer than 64 characters
    (Unicode codepoints), can only contain lowercase letters, numeric
    characters, underscores and dashes. International characters are allowed.
    No more than 64 user labels can be associated with one Annotation(System
    labels are excluded). See https://goo.gl/xmQnxf for more information and
    examples of labels. System reserved label keys are prefixed with
    "aiplatform.googleapis.com/" and are immutable. Following system labels
    exist for each Annotation: *
    "aiplatform.googleapis.com/annotation_set_name": optional, name of the
    UI's annotation set this Annotation belongs to. If not set, the Annotation
    is not visible in the UI. * "aiplatform.googleapis.com/payload_schema":
    output only, its value is the payload_schema's title.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  annotationSource = _messages.MessageField('GoogleCloudAiplatformV1UserActionReference', 1)
  createTime = _messages.StringField(2)
  etag = _messages.StringField(3)
  labels = _messages.MessageField('LabelsValue', 4)
  name = _messages.StringField(5)
  payload = _messages.MessageField('extra_types.JsonValue', 6)
  payloadSchemaUri = _messages.StringField(7)
  updateTime = _messages.StringField(8)


class GoogleCloudAiplatformV1AnnotationSpec(_messages.Message):
  r"""Identifies a concept with which DataItems may be annotated with.

  Fields:
    createTime: Output only. Timestamp when this AnnotationSpec was created.
    displayName: Required. The user-defined name of the AnnotationSpec. The
      name can be up to 128 characters long and can be consist of any UTF-8
      characters.
    etag: Optional. Used to perform consistent read-modify-write updates. If
      not set, a blind "overwrite" update happens.
    name: Output only. Resource name of the AnnotationSpec.
    updateTime: Output only. Timestamp when AnnotationSpec was last updated.
  """

  createTime = _messages.StringField(1)
  displayName = _messages.StringField(2)
  etag = _messages.StringField(3)
  name = _messages.StringField(4)
  updateTime = _messages.StringField(5)


class GoogleCloudAiplatformV1AutomaticResources(_messages.Message):
  r"""A description of resources that to large degree are decided by AI
  Platform, and require only a modest additional configuration. Each Model
  supporting these resources documents its specific guidelines.

  Fields:
    maxReplicaCount: Immutable. The maximum number of replicas this
      DeployedModel may be deployed on when the traffic against it increases.
      If the requested value is too large, the deployment will error, but if
      deployment succeeds then the ability to scale the model to that many
      replicas is guaranteed (barring service outages). If traffic against the
      DeployedModel increases beyond what its replicas at maximum may handle,
      a portion of the traffic will be dropped. If this value is not provided,
      a no upper bound for scaling under heavy traffic will be assume, though
      AI Platform may be unable to scale beyond certain replica number.
    minReplicaCount: Immutable. The minimum number of replicas this
      DeployedModel will be always deployed on. If traffic against it
      increases, it may dynamically be deployed onto more replicas up to
      max_replica_count, and as traffic decreases, some of these extra
      replicas may be freed. If the requested value is too large, the
      deployment will error.
  """

  maxReplicaCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  minReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1BatchDedicatedResources(_messages.Message):
  r"""A description of resources that are used for performing batch
  operations, are dedicated to a Model, and need manual configuration.

  Fields:
    machineSpec: Required. Immutable. The specification of a single machine.
    maxReplicaCount: Immutable. The maximum number of machine replicas the
      batch operation may be scaled to. The default value is 10.
    startingReplicaCount: Immutable. The number of machine replicas used at
      the start of the batch operation. If not set, AI Platform decides
      starting number, not greater than max_replica_count
  """

  machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 1)
  maxReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  startingReplicaCount = _messages.IntegerField(3, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadata(_messages.Message):
  r"""Runtime operation information for
  MigrationService.BatchMigrateResources.

  Fields:
    genericMetadata: The common part of the operation metadata.
    partialResults: Partial results that reflect the latest migration
      operation progress.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)
  partialResults = _messages.MessageField('GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult', 2, repeated=True)


class GoogleCloudAiplatformV1BatchMigrateResourcesOperationMetadataPartialResult(_messages.Message):
  r"""Represents a partial result in batch migration operation for one
  MigrateResourceRequest.

  Fields:
    dataset: Migrated dataset resource name.
    error: The error result of the migration request in case of failure.
    model: Migrated model resource name.
    request: It's the same as the value in
      MigrateResourceRequest.migrate_resource_requests.
  """

  dataset = _messages.StringField(1)
  error = _messages.MessageField('GoogleRpcStatus', 2)
  model = _messages.StringField(3)
  request = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequest', 4)


class GoogleCloudAiplatformV1BatchMigrateResourcesRequest(_messages.Message):
  r"""Request message for MigrationService.BatchMigrateResources.

  Fields:
    migrateResourceRequests: Required. The request messages specifying the
      resources to migrate. They must be in the same location as the
      destination. Up to 50 resources can be migrated in one batch.
  """

  migrateResourceRequests = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequest', 1, repeated=True)


class GoogleCloudAiplatformV1BatchMigrateResourcesResponse(_messages.Message):
  r"""Response message for MigrationService.BatchMigrateResources.

  Fields:
    migrateResourceResponses: Successfully migrated resources.
  """

  migrateResourceResponses = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceResponse', 1, repeated=True)


class GoogleCloudAiplatformV1BatchPredictionJob(_messages.Message):
  r"""A job that uses a Model to produce predictions on multiple input
  instances. If predictions for significant portion of the instances fail, the
  job may finish without attempting predictions for all remaining instances.

  Enums:
    StateValueValuesEnum: Output only. The detailed state of the job.

  Messages:
    LabelsValue: The labels with user-defined metadata to organize
      BatchPredictionJobs. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels.

  Fields:
    completionStats: Output only. Statistics on completed and failed
      prediction instances.
    createTime: Output only. Time when the BatchPredictionJob was created.
    dedicatedResources: The config of resources used by the Model during the
      batch prediction. If the Model supports DEDICATED_RESOURCES this config
      may be provided (and the job will use these resources), if the Model
      doesn't support AUTOMATIC_RESOURCES, this config must be provided.
    displayName: Required. The user-defined name of this BatchPredictionJob.
    encryptionSpec: Customer-managed encryption key options for a
      BatchPredictionJob. If this is set, then all resources created by the
      BatchPredictionJob will be encrypted with the provided encryption key.
    endTime: Output only. Time when the BatchPredictionJob entered any of the
      following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`,
      `JOB_STATE_CANCELLED`.
    error: Output only. Only populated when the job's state is
      JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    inputConfig: Required. Input configuration of the instances on which
      predictions are performed. The schema of any single instance may be
      specified via the Model's PredictSchemata's instance_schema_uri.
    labels: The labels with user-defined metadata to organize
      BatchPredictionJobs. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels.
    manualBatchTuningParameters: Immutable. Parameters configuring the batch
      behavior. Currently only applicable when dedicated_resources are used
      (in other cases AI Platform does the tuning itself).
    model: Required. The name of the Model that produces the predictions via
      this job, must share the same ancestor Location. Starting this job has
      no impact on any existing deployments of the Model and their resources.
    modelParameters: The parameters that govern the predictions. The schema of
      the parameters may be specified via the Model's PredictSchemata's
      parameters_schema_uri.
    name: Output only. Resource name of the BatchPredictionJob.
    outputConfig: Required. The Configuration specifying where output
      predictions should be written. The schema of any single prediction may
      be specified as a concatenation of Model's PredictSchemata's
      instance_schema_uri and prediction_schema_uri.
    outputInfo: Output only. Information further describing the output of this
      job.
    partialFailures: Output only. Partial failures encountered. For example,
      single files that can't be read. This field never exceeds 20 entries.
      Status details fields contain standard GCP error details.
    resourcesConsumed: Output only. Information about resources that had been
      consumed by this job. Provided in real time at best effort basis, as
      well as a final value once the job completes. Note: This field currently
      may be not populated for batch predictions that use AutoML Models.
    startTime: Output only. Time when the BatchPredictionJob for the first
      time entered the `JOB_STATE_RUNNING` state.
    state: Output only. The detailed state of the job.
    updateTime: Output only. Time when the BatchPredictionJob was most
      recently updated.
  """

  class StateValueValuesEnum(_messages.Enum):
    r"""Output only. The detailed state of the job.

    Values:
      JOB_STATE_UNSPECIFIED: The job state is unspecified.
      JOB_STATE_QUEUED: The job has been just created or resumed and
        processing has not yet begun.
      JOB_STATE_PENDING: The service is preparing to run the job.
      JOB_STATE_RUNNING: The job is in progress.
      JOB_STATE_SUCCEEDED: The job completed successfully.
      JOB_STATE_FAILED: The job failed.
      JOB_STATE_CANCELLING: The job is being cancelled. From this state the
        job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or
        `JOB_STATE_CANCELLED`.
      JOB_STATE_CANCELLED: The job has been cancelled.
      JOB_STATE_PAUSED: The job has been stopped, and can be resumed.
    """
    JOB_STATE_UNSPECIFIED = 0
    JOB_STATE_QUEUED = 1
    JOB_STATE_PENDING = 2
    JOB_STATE_RUNNING = 3
    JOB_STATE_SUCCEEDED = 4
    JOB_STATE_FAILED = 5
    JOB_STATE_CANCELLING = 6
    JOB_STATE_CANCELLED = 7
    JOB_STATE_PAUSED = 8

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""The labels with user-defined metadata to organize BatchPredictionJobs.
    Label keys and values can be no longer than 64 characters (Unicode
    codepoints), can only contain lowercase letters, numeric characters,
    underscores and dashes. International characters are allowed. See
    https://goo.gl/xmQnxf for more information and examples of labels.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  completionStats = _messages.MessageField('GoogleCloudAiplatformV1CompletionStats', 1)
  createTime = _messages.StringField(2)
  dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1BatchDedicatedResources', 3)
  displayName = _messages.StringField(4)
  encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 5)
  endTime = _messages.StringField(6)
  error = _messages.MessageField('GoogleRpcStatus', 7)
  inputConfig = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJobInputConfig', 8)
  labels = _messages.MessageField('LabelsValue', 9)
  manualBatchTuningParameters = _messages.MessageField('GoogleCloudAiplatformV1ManualBatchTuningParameters', 10)
  model = _messages.StringField(11)
  modelParameters = _messages.MessageField('extra_types.JsonValue', 12)
  name = _messages.StringField(13)
  outputConfig = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJobOutputConfig', 14)
  outputInfo = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJobOutputInfo', 15)
  partialFailures = _messages.MessageField('GoogleRpcStatus', 16, repeated=True)
  resourcesConsumed = _messages.MessageField('GoogleCloudAiplatformV1ResourcesConsumed', 17)
  startTime = _messages.StringField(18)
  state = _messages.EnumField('StateValueValuesEnum', 19)
  updateTime = _messages.StringField(20)


class GoogleCloudAiplatformV1BatchPredictionJobInputConfig(_messages.Message):
  r"""Configures the input to BatchPredictionJob. See
  Model.supported_input_storage_formats for Model's supported input formats,
  and how instances should be expressed via any of them.

  Fields:
    bigquerySource: The BigQuery location of the input table. The schema of
      the table should be in the format described by the given context OpenAPI
      Schema, if one is provided. The table may contain additional columns
      that are not described by the schema, and they will be ignored.
    gcsSource: The Cloud Storage location for the input instances.
    instancesFormat: Required. The format in which instances are given, must
      be one of the Model's supported_input_storage_formats.
  """

  bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1BigQuerySource', 1)
  gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 2)
  instancesFormat = _messages.StringField(3)


class GoogleCloudAiplatformV1BatchPredictionJobOutputConfig(_messages.Message):
  r"""Configures the output of BatchPredictionJob. See
  Model.supported_output_storage_formats for supported output formats, and how
  predictions are expressed via any of them.

  Fields:
    bigqueryDestination: The BigQuery project location where the output is to
      be written to. In the given project a new dataset is created with name
      `prediction__` where is made BigQuery-dataset-name compatible (for
      example, most special characters become underscores), and timestamp is
      in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset
      two tables will be created, `predictions`, and `errors`. If the Model
      has both instance and prediction schemata defined then the tables have
      columns as follows: The `predictions` table contains instances for which
      the prediction succeeded, it has columns as per a concatenation of the
      Model's instance and prediction schemata. The `errors` table contains
      rows for which the prediction has failed, it has instance columns, as
      per the instance schema, followed by a single "errors" column, which as
      values has [`google.rpc.Status`](Status) represented as a STRUCT, and
      containing only `code` and `message`.
    gcsDestination: The Cloud Storage location of the directory where the
      output is to be written to. In the given directory a new directory is
      created. Its name is `prediction--`, where timestamp is in YYYY-MM-
      DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files
      `predictions_0001.`, `predictions_0002.`, ..., `predictions_N.` are
      created where `` depends on chosen predictions_format, and N may equal
      0001 and depends on the total number of successfully predicted
      instances. If the Model has both instance and prediction schemata
      defined then each such file contains predictions as per the
      predictions_format. If prediction for any instance failed (partially or
      completely), then an additional `errors_0001.`, `errors_0002.`,...,
      `errors_N.` files are created (N depends on total number of failed
      predictions). These files contain the failed instances, as per their
      schema, followed by an additional `error` field which as value has
      [`google.rpc.Status`](Status) containing only `code` and `message`
      fields.
    predictionsFormat: Required. The format in which AI Platform gives the
      predictions, must be one of the Model's
      supported_output_storage_formats.
  """

  bigqueryDestination = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 1)
  gcsDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 2)
  predictionsFormat = _messages.StringField(3)


class GoogleCloudAiplatformV1BatchPredictionJobOutputInfo(_messages.Message):
  r"""Further describes this job's output. Supplements output_config.

  Fields:
    bigqueryOutputDataset: Output only. The path of the BigQuery dataset
      created, in `bq://projectId.bqDatasetId` format, into which the
      prediction output is written.
    gcsOutputDirectory: Output only. The full path of the Cloud Storage
      directory created, into which the prediction output is written.
  """

  bigqueryOutputDataset = _messages.StringField(1)
  gcsOutputDirectory = _messages.StringField(2)


class GoogleCloudAiplatformV1BigQueryDestination(_messages.Message):
  r"""The BigQuery location for the output content.

  Fields:
    outputUri: Required. BigQuery URI to a project or table, up to 2000
      characters long. When only the project is specified, the Dataset and
      Table is created. When the full table reference is specified, the
      Dataset must exist and table must not exist. Accepted forms: * BigQuery
      path. For example: `bq://projectId` or
      `bq://projectId.bqDatasetId.bqTableId`.
  """

  outputUri = _messages.StringField(1)


class GoogleCloudAiplatformV1BigQuerySource(_messages.Message):
  r"""The BigQuery location for the input content.

  Fields:
    inputUri: Required. BigQuery URI to a table, up to 2000 characters long.
      Accepted forms: * BigQuery path. For example:
      `bq://projectId.bqDatasetId.bqTableId`.
  """

  inputUri = _messages.StringField(1)


class GoogleCloudAiplatformV1CancelBatchPredictionJobRequest(_messages.Message):
  r"""Request message for JobService.CancelBatchPredictionJob."""


class GoogleCloudAiplatformV1CancelCustomJobRequest(_messages.Message):
  r"""Request message for JobService.CancelCustomJob."""


class GoogleCloudAiplatformV1CancelDataLabelingJobRequest(_messages.Message):
  r"""Request message for DataLabelingJobService.CancelDataLabelingJob."""


class GoogleCloudAiplatformV1CancelHyperparameterTuningJobRequest(_messages.Message):
  r"""Request message for JobService.CancelHyperparameterTuningJob."""


class GoogleCloudAiplatformV1CancelTrainingPipelineRequest(_messages.Message):
  r"""Request message for PipelineService.CancelTrainingPipeline."""


class GoogleCloudAiplatformV1CompletionStats(_messages.Message):
  r"""Success and error statistics of processing multiple entities (for
  example, DataItems or structured data rows) in batch.

  Fields:
    failedCount: Output only. The number of entities for which any error was
      encountered.
    incompleteCount: Output only. In cases when enough errors are encountered
      a job, pipeline, or operation may be failed as a whole. Below is the
      number of entities for which the processing had not been finished
      (either in successful or failed state). Set to -1 if the number is
      unknown (for example, the operation failed before the total entity
      number could be collected).
    successfulCount: Output only. The number of entities that had been
      processed successfully.
  """

  failedCount = _messages.IntegerField(1)
  incompleteCount = _messages.IntegerField(2)
  successfulCount = _messages.IntegerField(3)


class GoogleCloudAiplatformV1ContainerRegistryDestination(_messages.Message):
  r"""The Container Registry location for the container image.

  Fields:
    outputUri: Required. Container Registry URI of a container image. Only
      Google Container Registry and Artifact Registry are supported now.
      Accepted forms: * Google Container Registry path. For example:
      `gcr.io/projectId/imageName:tag`. * Artifact Registry path. For example:
      `us-central1-docker.pkg.dev/projectId/repoName/imageName:tag`. If a tag
      is not specified, "latest" will be used as the default tag.
  """

  outputUri = _messages.StringField(1)


class GoogleCloudAiplatformV1ContainerSpec(_messages.Message):
  r"""The spec of a Container.

  Fields:
    args: The arguments to be passed when starting the container.
    command: The command to be invoked when the container is started. It
      overrides the entrypoint instruction in Dockerfile when provided.
    env: Environment variables to be passed to the container.
    imageUri: Required. The URI of a container image in the Container Registry
      that is to be run on each worker replica.
  """

  args = _messages.StringField(1, repeated=True)
  command = _messages.StringField(2, repeated=True)
  env = _messages.MessageField('GoogleCloudAiplatformV1EnvVar', 3, repeated=True)
  imageUri = _messages.StringField(4)


class GoogleCloudAiplatformV1CreateDatasetOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.CreateDataset.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1CreateEndpointOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.CreateEndpoint.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1CreateSpecialistPoolOperationMetadata(_messages.Message):
  r"""Runtime operation information for
  SpecialistPoolService.CreateSpecialistPool.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1CustomJob(_messages.Message):
  r"""Represents a job that runs custom workloads such as a Docker container
  or a Python package. A CustomJob can have multiple worker pools and each
  worker pool can have its own machine and input spec. A CustomJob will be
  cleaned up once the job enters terminal state (failed or succeeded).

  Enums:
    StateValueValuesEnum: Output only. The detailed state of the job.

  Messages:
    LabelsValue: The labels with user-defined metadata to organize CustomJobs.
      Label keys and values can be no longer than 64 characters (Unicode
      codepoints), can only contain lowercase letters, numeric characters,
      underscores and dashes. International characters are allowed. See
      https://goo.gl/xmQnxf for more information and examples of labels.

  Fields:
    createTime: Output only. Time when the CustomJob was created.
    displayName: Required. The display name of the CustomJob. The name can be
      up to 128 characters long and can be consist of any UTF-8 characters.
    encryptionSpec: Customer-managed encryption key options for a CustomJob.
      If this is set, then all resources created by the CustomJob will be
      encrypted with the provided encryption key.
    endTime: Output only. Time when the CustomJob entered any of the following
      states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`,
      `JOB_STATE_CANCELLED`.
    error: Output only. Only populated when job's state is `JOB_STATE_FAILED`
      or `JOB_STATE_CANCELLED`.
    jobSpec: Required. Job spec.
    labels: The labels with user-defined metadata to organize CustomJobs.
      Label keys and values can be no longer than 64 characters (Unicode
      codepoints), can only contain lowercase letters, numeric characters,
      underscores and dashes. International characters are allowed. See
      https://goo.gl/xmQnxf for more information and examples of labels.
    name: Output only. Resource name of a CustomJob.
    startTime: Output only. Time when the CustomJob for the first time entered
      the `JOB_STATE_RUNNING` state.
    state: Output only. The detailed state of the job.
    updateTime: Output only. Time when the CustomJob was most recently
      updated.
  """

  class StateValueValuesEnum(_messages.Enum):
    r"""Output only. The detailed state of the job.

    Values:
      JOB_STATE_UNSPECIFIED: The job state is unspecified.
      JOB_STATE_QUEUED: The job has been just created or resumed and
        processing has not yet begun.
      JOB_STATE_PENDING: The service is preparing to run the job.
      JOB_STATE_RUNNING: The job is in progress.
      JOB_STATE_SUCCEEDED: The job completed successfully.
      JOB_STATE_FAILED: The job failed.
      JOB_STATE_CANCELLING: The job is being cancelled. From this state the
        job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or
        `JOB_STATE_CANCELLED`.
      JOB_STATE_CANCELLED: The job has been cancelled.
      JOB_STATE_PAUSED: The job has been stopped, and can be resumed.
    """
    JOB_STATE_UNSPECIFIED = 0
    JOB_STATE_QUEUED = 1
    JOB_STATE_PENDING = 2
    JOB_STATE_RUNNING = 3
    JOB_STATE_SUCCEEDED = 4
    JOB_STATE_FAILED = 5
    JOB_STATE_CANCELLING = 6
    JOB_STATE_CANCELLED = 7
    JOB_STATE_PAUSED = 8

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""The labels with user-defined metadata to organize CustomJobs. Label
    keys and values can be no longer than 64 characters (Unicode codepoints),
    can only contain lowercase letters, numeric characters, underscores and
    dashes. International characters are allowed. See https://goo.gl/xmQnxf
    for more information and examples of labels.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  createTime = _messages.StringField(1)
  displayName = _messages.StringField(2)
  encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3)
  endTime = _messages.StringField(4)
  error = _messages.MessageField('GoogleRpcStatus', 5)
  jobSpec = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 6)
  labels = _messages.MessageField('LabelsValue', 7)
  name = _messages.StringField(8)
  startTime = _messages.StringField(9)
  state = _messages.EnumField('StateValueValuesEnum', 10)
  updateTime = _messages.StringField(11)


class GoogleCloudAiplatformV1CustomJobSpec(_messages.Message):
  r"""Represents the spec of a CustomJob.

  Fields:
    baseOutputDirectory: The Cloud Storage location to store the output of
      this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob,
      the baseOutputDirectory of each child CustomJob backing a Trial is set
      to a subdirectory of name id under its parent HyperparameterTuningJob's
      baseOutputDirectory. The following AI Platform environment variables
      will be passed to containers or python modules when this field is set:
      For CustomJob: * AIP_MODEL_DIR = `/model/` * AIP_CHECKPOINT_DIR =
      `/checkpoints/` * AIP_TENSORBOARD_LOG_DIR = `/logs/` For CustomJob
      backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = `//model/`
      * AIP_CHECKPOINT_DIR = `//checkpoints/` * AIP_TENSORBOARD_LOG_DIR =
      `//logs/`
    network: The full name of the Compute Engine
      [network](/compute/docs/networks-and-firewalls#networks) to which the
      Job should be peered. For example,
      `projects/12345/global/networks/myVPC`.
      [Format](/compute/docs/reference/rest/v1/networks/insert) is of the form
      `projects/{project}/global/networks/{network}`. Where {project} is a
      project number, as in `12345`, and {network} is a network name. Private
      services access must already be configured for the network. If left
      unspecified, the job is not peered with any network.
    scheduling: Scheduling options for a CustomJob.
    serviceAccount: Specifies the service account for workload run-as account.
      Users submitting jobs must have act-as permission on this run-as
      account. If unspecified, the AI Platform Custom Code Service Agent for
      the CustomJob's project is used.
    workerPoolSpecs: Required. The spec of the worker pools including machine
      type and Docker image. All worker pools except the first one are
      optional and can be skipped by providing an empty value.
  """

  baseOutputDirectory = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 1)
  network = _messages.StringField(2)
  scheduling = _messages.MessageField('GoogleCloudAiplatformV1Scheduling', 3)
  serviceAccount = _messages.StringField(4)
  workerPoolSpecs = _messages.MessageField('GoogleCloudAiplatformV1WorkerPoolSpec', 5, repeated=True)


class GoogleCloudAiplatformV1DataItem(_messages.Message):
  r"""A piece of data in a Dataset. Could be an image, a video, a document or
  plain text.

  Messages:
    LabelsValue: Optional. The labels with user-defined metadata to organize
      your DataItems. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. No more than 64 user labels can be associated with one
      DataItem(System labels are excluded). See https://goo.gl/xmQnxf for more
      information and examples of labels. System reserved label keys are
      prefixed with "aiplatform.googleapis.com/" and are immutable.

  Fields:
    createTime: Output only. Timestamp when this DataItem was created.
    etag: Optional. Used to perform consistent read-modify-write updates. If
      not set, a blind "overwrite" update happens.
    labels: Optional. The labels with user-defined metadata to organize your
      DataItems. Label keys and values can be no longer than 64 characters
      (Unicode codepoints), can only contain lowercase letters, numeric
      characters, underscores and dashes. International characters are
      allowed. No more than 64 user labels can be associated with one
      DataItem(System labels are excluded). See https://goo.gl/xmQnxf for more
      information and examples of labels. System reserved label keys are
      prefixed with "aiplatform.googleapis.com/" and are immutable.
    name: Output only. The resource name of the DataItem.
    payload: Required. The data that the DataItem represents (for example, an
      image or a text snippet). The schema of the payload is stored in the
      parent Dataset's metadata schema's dataItemSchemaUri field.
    updateTime: Output only. Timestamp when this DataItem was last updated.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""Optional. The labels with user-defined metadata to organize your
    DataItems. Label keys and values can be no longer than 64 characters
    (Unicode codepoints), can only contain lowercase letters, numeric
    characters, underscores and dashes. International characters are allowed.
    No more than 64 user labels can be associated with one DataItem(System
    labels are excluded). See https://goo.gl/xmQnxf for more information and
    examples of labels. System reserved label keys are prefixed with
    "aiplatform.googleapis.com/" and are immutable.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  createTime = _messages.StringField(1)
  etag = _messages.StringField(2)
  labels = _messages.MessageField('LabelsValue', 3)
  name = _messages.StringField(4)
  payload = _messages.MessageField('extra_types.JsonValue', 5)
  updateTime = _messages.StringField(6)


class GoogleCloudAiplatformV1DataLabelingJob(_messages.Message):
  r"""DataLabelingJob is used to trigger a human labeling job on unlabeled
  data from the following Dataset:

  Enums:
    StateValueValuesEnum: Output only. The detailed state of the job.

  Messages:
    AnnotationLabelsValue: Labels to assign to annotations generated by this
      DataLabelingJob. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels. System reserved label keys are prefixed with
      "aiplatform.googleapis.com/" and are immutable.
    LabelsValue: The labels with user-defined metadata to organize your
      DataLabelingJobs. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels. System reserved label keys are prefixed with
      "aiplatform.googleapis.com/" and are immutable. Following system labels
      exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema":
      output only, its value is the inputs_schema's title.

  Fields:
    activeLearningConfig: Parameters that configure the active learning
      pipeline. Active learning will label the data incrementally via several
      iterations. For every iteration, it will select a batch of data based on
      the sampling strategy.
    annotationLabels: Labels to assign to annotations generated by this
      DataLabelingJob. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels. System reserved label keys are prefixed with
      "aiplatform.googleapis.com/" and are immutable.
    createTime: Output only. Timestamp when this DataLabelingJob was created.
    currentSpend: Output only. Estimated cost(in US dollars) that the
      DataLabelingJob has incurred to date.
    datasets: Required. Dataset resource names. Right now we only support
      labeling from a single Dataset. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`
    displayName: Required. The user-defined name of the DataLabelingJob. The
      name can be up to 128 characters long and can be consist of any UTF-8
      characters. Display name of a DataLabelingJob.
    encryptionSpec: Customer-managed encryption key spec for a
      DataLabelingJob. If set, this DataLabelingJob will be secured by this
      key. Note: Annotations created in the DataLabelingJob are associated
      with the EncryptionSpec of the Dataset they are exported to.
    error: Output only. DataLabelingJob errors. It is only populated when
      job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
    inputs: Required. Input config parameters for the DataLabelingJob.
    inputsSchemaUri: Required. Points to a YAML file stored on Google Cloud
      Storage describing the config for a specific type of DataLabelingJob.
      The schema files that can be used here are found in the
      https://storage.googleapis.com/google-cloud-aiplatform bucket in the
      /schema/datalabelingjob/inputs/ folder.
    instructionUri: Required. The Google Cloud Storage location of the
      instruction pdf. This pdf is shared with labelers, and provides detailed
      description on how to label DataItems in Datasets.
    labelerCount: Required. Number of labelers to work on each DataItem.
    labelingProgress: Output only. Current labeling job progress percentage
      scaled in interval [0, 100], indicating the percentage of DataItems that
      has been finished.
    labels: The labels with user-defined metadata to organize your
      DataLabelingJobs. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels. System reserved label keys are prefixed with
      "aiplatform.googleapis.com/" and are immutable. Following system labels
      exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema":
      output only, its value is the inputs_schema's title.
    name: Output only. Resource name of the DataLabelingJob.
    specialistPools: The SpecialistPools' resource names associated with this
      job.
    state: Output only. The detailed state of the job.
    updateTime: Output only. Timestamp when this DataLabelingJob was updated
      most recently.
  """

  class StateValueValuesEnum(_messages.Enum):
    r"""Output only. The detailed state of the job.

    Values:
      JOB_STATE_UNSPECIFIED: The job state is unspecified.
      JOB_STATE_QUEUED: The job has been just created or resumed and
        processing has not yet begun.
      JOB_STATE_PENDING: The service is preparing to run the job.
      JOB_STATE_RUNNING: The job is in progress.
      JOB_STATE_SUCCEEDED: The job completed successfully.
      JOB_STATE_FAILED: The job failed.
      JOB_STATE_CANCELLING: The job is being cancelled. From this state the
        job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or
        `JOB_STATE_CANCELLED`.
      JOB_STATE_CANCELLED: The job has been cancelled.
      JOB_STATE_PAUSED: The job has been stopped, and can be resumed.
    """
    JOB_STATE_UNSPECIFIED = 0
    JOB_STATE_QUEUED = 1
    JOB_STATE_PENDING = 2
    JOB_STATE_RUNNING = 3
    JOB_STATE_SUCCEEDED = 4
    JOB_STATE_FAILED = 5
    JOB_STATE_CANCELLING = 6
    JOB_STATE_CANCELLED = 7
    JOB_STATE_PAUSED = 8

  @encoding.MapUnrecognizedFields('additionalProperties')
  class AnnotationLabelsValue(_messages.Message):
    r"""Labels to assign to annotations generated by this DataLabelingJob.
    Label keys and values can be no longer than 64 characters (Unicode
    codepoints), can only contain lowercase letters, numeric characters,
    underscores and dashes. International characters are allowed. See
    https://goo.gl/xmQnxf for more information and examples of labels. System
    reserved label keys are prefixed with "aiplatform.googleapis.com/" and are
    immutable.

    Messages:
      AdditionalProperty: An additional property for a AnnotationLabelsValue
        object.

    Fields:
      additionalProperties: Additional properties of type
        AnnotationLabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a AnnotationLabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""The labels with user-defined metadata to organize your
    DataLabelingJobs. Label keys and values can be no longer than 64
    characters (Unicode codepoints), can only contain lowercase letters,
    numeric characters, underscores and dashes. International characters are
    allowed. See https://goo.gl/xmQnxf for more information and examples of
    labels. System reserved label keys are prefixed with
    "aiplatform.googleapis.com/" and are immutable. Following system labels
    exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema":
    output only, its value is the inputs_schema's title.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  activeLearningConfig = _messages.MessageField('GoogleCloudAiplatformV1ActiveLearningConfig', 1)
  annotationLabels = _messages.MessageField('AnnotationLabelsValue', 2)
  createTime = _messages.StringField(3)
  currentSpend = _messages.MessageField('GoogleTypeMoney', 4)
  datasets = _messages.StringField(5, repeated=True)
  displayName = _messages.StringField(6)
  encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 7)
  error = _messages.MessageField('GoogleRpcStatus', 8)
  inputs = _messages.MessageField('extra_types.JsonValue', 9)
  inputsSchemaUri = _messages.StringField(10)
  instructionUri = _messages.StringField(11)
  labelerCount = _messages.IntegerField(12, variant=_messages.Variant.INT32)
  labelingProgress = _messages.IntegerField(13, variant=_messages.Variant.INT32)
  labels = _messages.MessageField('LabelsValue', 14)
  name = _messages.StringField(15)
  specialistPools = _messages.StringField(16, repeated=True)
  state = _messages.EnumField('StateValueValuesEnum', 17)
  updateTime = _messages.StringField(18)


class GoogleCloudAiplatformV1Dataset(_messages.Message):
  r"""A collection of DataItems and Annotations on them.

  Messages:
    LabelsValue: The labels with user-defined metadata to organize your
      Datasets. Label keys and values can be no longer than 64 characters
      (Unicode codepoints), can only contain lowercase letters, numeric
      characters, underscores and dashes. International characters are
      allowed. No more than 64 user labels can be associated with one Dataset
      (System labels are excluded). See https://goo.gl/xmQnxf for more
      information and examples of labels. System reserved label keys are
      prefixed with "aiplatform.googleapis.com/" and are immutable. Following
      system labels exist for each Dataset: *
      "aiplatform.googleapis.com/dataset_metadata_schema": output only, its
      value is the metadata_schema's title.

  Fields:
    createTime: Output only. Timestamp when this Dataset was created.
    displayName: Required. The user-defined name of the Dataset. The name can
      be up to 128 characters long and can be consist of any UTF-8 characters.
    encryptionSpec: Customer-managed encryption key spec for a Dataset. If
      set, this Dataset and all sub-resources of this Dataset will be secured
      by this key.
    etag: Used to perform consistent read-modify-write updates. If not set, a
      blind "overwrite" update happens.
    labels: The labels with user-defined metadata to organize your Datasets.
      Label keys and values can be no longer than 64 characters (Unicode
      codepoints), can only contain lowercase letters, numeric characters,
      underscores and dashes. International characters are allowed. No more
      than 64 user labels can be associated with one Dataset (System labels
      are excluded). See https://goo.gl/xmQnxf for more information and
      examples of labels. System reserved label keys are prefixed with
      "aiplatform.googleapis.com/" and are immutable. Following system labels
      exist for each Dataset: *
      "aiplatform.googleapis.com/dataset_metadata_schema": output only, its
      value is the metadata_schema's title.
    metadata: Required. Additional information about the Dataset.
    metadataSchemaUri: Required. Points to a YAML file stored on Google Cloud
      Storage describing additional information about the Dataset. The schema
      is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can
      be used here are found in gs://google-cloud-
      aiplatform/schema/dataset/metadata/.
    name: Output only. The resource name of the Dataset.
    updateTime: Output only. Timestamp when this Dataset was last updated.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""The labels with user-defined metadata to organize your Datasets. Label
    keys and values can be no longer than 64 characters (Unicode codepoints),
    can only contain lowercase letters, numeric characters, underscores and
    dashes. International characters are allowed. No more than 64 user labels
    can be associated with one Dataset (System labels are excluded). See
    https://goo.gl/xmQnxf for more information and examples of labels. System
    reserved label keys are prefixed with "aiplatform.googleapis.com/" and are
    immutable. Following system labels exist for each Dataset: *
    "aiplatform.googleapis.com/dataset_metadata_schema": output only, its
    value is the metadata_schema's title.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  createTime = _messages.StringField(1)
  displayName = _messages.StringField(2)
  encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3)
  etag = _messages.StringField(4)
  labels = _messages.MessageField('LabelsValue', 5)
  metadata = _messages.MessageField('extra_types.JsonValue', 6)
  metadataSchemaUri = _messages.StringField(7)
  name = _messages.StringField(8)
  updateTime = _messages.StringField(9)


class GoogleCloudAiplatformV1DedicatedResources(_messages.Message):
  r"""A description of resources that are dedicated to a DeployedModel, and
  that need a higher degree of manual configuration.

  Fields:
    machineSpec: Required. Immutable. The specification of a single machine
      used by the prediction.
    maxReplicaCount: Immutable. The maximum number of replicas this
      DeployedModel may be deployed on when the traffic against it increases.
      If the requested value is too large, the deployment will error, but if
      deployment succeeds then the ability to scale the model to that many
      replicas is guaranteed (barring service outages). If traffic against the
      DeployedModel increases beyond what its replicas at maximum may handle,
      a portion of the traffic will be dropped. If this value is not provided,
      will use min_replica_count as the default value.
    minReplicaCount: Required. Immutable. The minimum number of machine
      replicas this DeployedModel will be always deployed on. If traffic
      against it increases, it may dynamically be deployed onto more replicas,
      and as traffic decreases, some of these extra replicas may be freed.
      Note: if machine_spec.accelerator_count is above 0, currently the model
      will be always deployed precisely on min_replica_count.
  """

  machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 1)
  maxReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  minReplicaCount = _messages.IntegerField(3, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1DeleteOperationMetadata(_messages.Message):
  r"""Details of operations that perform deletes of any entities.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1DeployModelOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.DeployModel.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1DeployModelRequest(_messages.Message):
  r"""Request message for EndpointService.DeployModel.

  Messages:
    TrafficSplitValue: A map from a DeployedModel's ID to the percentage of
      this Endpoint's traffic that should be forwarded to that DeployedModel.
      If this field is non-empty, then the Endpoint's traffic_split will be
      overwritten with it. To refer to the ID of the just being deployed
      Model, a "0" should be used, and the actual ID of the new DeployedModel
      will be filled in its place by this method. The traffic percentage
      values must add up to 100. If this field is empty, then the Endpoint's
      traffic_split is not updated.

  Fields:
    deployedModel: Required. The DeployedModel to be created within the
      Endpoint. Note that Endpoint.traffic_split must be updated for the
      DeployedModel to start receiving traffic, either as part of this call,
      or via EndpointService.UpdateEndpoint.
    trafficSplit: A map from a DeployedModel's ID to the percentage of this
      Endpoint's traffic that should be forwarded to that DeployedModel. If
      this field is non-empty, then the Endpoint's traffic_split will be
      overwritten with it. To refer to the ID of the just being deployed
      Model, a "0" should be used, and the actual ID of the new DeployedModel
      will be filled in its place by this method. The traffic percentage
      values must add up to 100. If this field is empty, then the Endpoint's
      traffic_split is not updated.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class TrafficSplitValue(_messages.Message):
    r"""A map from a DeployedModel's ID to the percentage of this Endpoint's
    traffic that should be forwarded to that DeployedModel. If this field is
    non-empty, then the Endpoint's traffic_split will be overwritten with it.
    To refer to the ID of the just being deployed Model, a "0" should be used,
    and the actual ID of the new DeployedModel will be filled in its place by
    this method. The traffic percentage values must add up to 100. If this
    field is empty, then the Endpoint's traffic_split is not updated.

    Messages:
      AdditionalProperty: An additional property for a TrafficSplitValue
        object.

    Fields:
      additionalProperties: Additional properties of type TrafficSplitValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a TrafficSplitValue object.

      Fields:
        key: Name of the additional property.
        value: A integer attribute.
      """

      key = _messages.StringField(1)
      value = _messages.IntegerField(2, variant=_messages.Variant.INT32)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  deployedModel = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 1)
  trafficSplit = _messages.MessageField('TrafficSplitValue', 2)


class GoogleCloudAiplatformV1DeployModelResponse(_messages.Message):
  r"""Response message for EndpointService.DeployModel.

  Fields:
    deployedModel: The DeployedModel that had been deployed in the Endpoint.
  """

  deployedModel = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 1)


class GoogleCloudAiplatformV1DeployedModel(_messages.Message):
  r"""A deployment of a Model. Endpoints contain one or more DeployedModels.

  Fields:
    automaticResources: A description of resources that to large degree are
      decided by AI Platform, and require only a modest additional
      configuration.
    createTime: Output only. Timestamp when the DeployedModel was created.
    dedicatedResources: A description of resources that are dedicated to the
      DeployedModel, and that need a higher degree of manual configuration.
    disableContainerLogging: For custom-trained Models and AutoML Tabular
      Models, the container of the DeployedModel instances will send `stderr`
      and `stdout` streams to Stackdriver Logging by default. Please note that
      the logs incur cost, which are subject to [Cloud Logging
      pricing](https://cloud.google.com/stackdriver/pricing). User can disable
      container logging by setting this flag to true.
    displayName: The display name of the DeployedModel. If not provided upon
      creation, the Model's display_name is used.
    enableAccessLogging: These logs are like standard server access logs,
      containing information like timestamp and latency for each prediction
      request. Note that Stackdriver logs may incur a cost, especially if your
      project receives prediction requests at a high queries per second rate
      (QPS). Estimate your costs before enabling this option.
    id: Output only. The ID of the DeployedModel.
    model: Required. The name of the Model that this is the deployment of.
      Note that the Model may be in a different location than the
      DeployedModel's Endpoint.
    serviceAccount: The service account that the DeployedModel's container
      runs as. Specify the email address of the service account. If this
      service account is not specified, the container runs as a service
      account that doesn't have access to the resource project. Users
      deploying the Model must have the `iam.serviceAccounts.actAs` permission
      on this service account.
  """

  automaticResources = _messages.MessageField('GoogleCloudAiplatformV1AutomaticResources', 1)
  createTime = _messages.StringField(2)
  dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1DedicatedResources', 3)
  disableContainerLogging = _messages.BooleanField(4)
  displayName = _messages.StringField(5)
  enableAccessLogging = _messages.BooleanField(6)
  id = _messages.StringField(7)
  model = _messages.StringField(8)
  serviceAccount = _messages.StringField(9)


class GoogleCloudAiplatformV1DeployedModelRef(_messages.Message):
  r"""Points to a DeployedModel.

  Fields:
    deployedModelId: Immutable. An ID of a DeployedModel in the above
      Endpoint.
    endpoint: Immutable. A resource name of an Endpoint.
  """

  deployedModelId = _messages.StringField(1)
  endpoint = _messages.StringField(2)


class GoogleCloudAiplatformV1DiskSpec(_messages.Message):
  r"""Represents the spec of disk options.

  Fields:
    bootDiskSizeGb: Size in GB of the boot disk (default is 100GB).
    bootDiskType: Type of the boot disk (default is "pd-ssd"). Valid values:
      "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard"
      (Persistent Disk Hard Disk Drive).
  """

  bootDiskSizeGb = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  bootDiskType = _messages.StringField(2)


class GoogleCloudAiplatformV1EncryptionSpec(_messages.Message):
  r"""Represents a customer-managed encryption key spec that can be applied to
  a top-level resource.

  Fields:
    kmsKeyName: Required. The Cloud KMS resource identifier of the customer
      managed encryption key used to protect a resource. Has the form:
      `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-
      key`. The key needs to be in the same region as where the compute
      resource is created.
  """

  kmsKeyName = _messages.StringField(1)


class GoogleCloudAiplatformV1Endpoint(_messages.Message):
  r"""Models are deployed into it, and afterwards Endpoint is called to obtain
  predictions and explanations.

  Messages:
    LabelsValue: The labels with user-defined metadata to organize your
      Endpoints. Label keys and values can be no longer than 64 characters
      (Unicode codepoints), can only contain lowercase letters, numeric
      characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels.
    TrafficSplitValue: A map from a DeployedModel's ID to the percentage of
      this Endpoint's traffic that should be forwarded to that DeployedModel.
      If a DeployedModel's ID is not listed in this map, then it receives no
      traffic. The traffic percentage values must add up to 100, or map must
      be empty if the Endpoint is to not accept any traffic at a moment.

  Fields:
    createTime: Output only. Timestamp when this Endpoint was created.
    deployedModels: Output only. The models deployed in this Endpoint. To add
      or remove DeployedModels use EndpointService.DeployModel and
      EndpointService.UndeployModel respectively.
    description: The description of the Endpoint.
    displayName: Required. The display name of the Endpoint. The name can be
      up to 128 characters long and can be consist of any UTF-8 characters.
    encryptionSpec: Customer-managed encryption key spec for an Endpoint. If
      set, this Endpoint and all sub-resources of this Endpoint will be
      secured by this key.
    etag: Used to perform consistent read-modify-write updates. If not set, a
      blind "overwrite" update happens.
    labels: The labels with user-defined metadata to organize your Endpoints.
      Label keys and values can be no longer than 64 characters (Unicode
      codepoints), can only contain lowercase letters, numeric characters,
      underscores and dashes. International characters are allowed. See
      https://goo.gl/xmQnxf for more information and examples of labels.
    name: Output only. The resource name of the Endpoint.
    trafficSplit: A map from a DeployedModel's ID to the percentage of this
      Endpoint's traffic that should be forwarded to that DeployedModel. If a
      DeployedModel's ID is not listed in this map, then it receives no
      traffic. The traffic percentage values must add up to 100, or map must
      be empty if the Endpoint is to not accept any traffic at a moment.
    updateTime: Output only. Timestamp when this Endpoint was last updated.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""The labels with user-defined metadata to organize your Endpoints.
    Label keys and values can be no longer than 64 characters (Unicode
    codepoints), can only contain lowercase letters, numeric characters,
    underscores and dashes. International characters are allowed. See
    https://goo.gl/xmQnxf for more information and examples of labels.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  @encoding.MapUnrecognizedFields('additionalProperties')
  class TrafficSplitValue(_messages.Message):
    r"""A map from a DeployedModel's ID to the percentage of this Endpoint's
    traffic that should be forwarded to that DeployedModel. If a
    DeployedModel's ID is not listed in this map, then it receives no traffic.
    The traffic percentage values must add up to 100, or map must be empty if
    the Endpoint is to not accept any traffic at a moment.

    Messages:
      AdditionalProperty: An additional property for a TrafficSplitValue
        object.

    Fields:
      additionalProperties: Additional properties of type TrafficSplitValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a TrafficSplitValue object.

      Fields:
        key: Name of the additional property.
        value: A integer attribute.
      """

      key = _messages.StringField(1)
      value = _messages.IntegerField(2, variant=_messages.Variant.INT32)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  createTime = _messages.StringField(1)
  deployedModels = _messages.MessageField('GoogleCloudAiplatformV1DeployedModel', 2, repeated=True)
  description = _messages.StringField(3)
  displayName = _messages.StringField(4)
  encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 5)
  etag = _messages.StringField(6)
  labels = _messages.MessageField('LabelsValue', 7)
  name = _messages.StringField(8)
  trafficSplit = _messages.MessageField('TrafficSplitValue', 9)
  updateTime = _messages.StringField(10)


class GoogleCloudAiplatformV1EnvVar(_messages.Message):
  r"""Represents an environment variable present in a Container or Python
  Module.

  Fields:
    name: Required. Name of the environment variable. Must be a valid C
      identifier.
    value: Required. Variables that reference a $(VAR_NAME) are expanded using
      the previous defined environment variables in the container and any
      service environment variables. If a variable cannot be resolved, the
      reference in the input string will be unchanged. The $(VAR_NAME) syntax
      can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references
      will never be expanded, regardless of whether the variable exists or
      not.
  """

  name = _messages.StringField(1)
  value = _messages.StringField(2)


class GoogleCloudAiplatformV1ExportDataConfig(_messages.Message):
  r"""Describes what part of the Dataset is to be exported, the destination of
  the export and how to export.

  Fields:
    annotationsFilter: A filter on Annotations of the Dataset. Only
      Annotations on to-be-exported DataItems(specified by data_items_filter)
      that match this filter will be exported. The filter syntax is the same
      as in ListAnnotations.
    gcsDestination: The Google Cloud Storage location where the output is to
      be written to. 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. Inside that directory, annotations with the same schema
      will be grouped into sub directories which are named with the
      corresponding annotations' schema title. Inside these sub directories, a
      schema.yaml will be created to describe the output format.
  """

  annotationsFilter = _messages.StringField(1)
  gcsDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 2)


class GoogleCloudAiplatformV1ExportDataOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.ExportData.

  Fields:
    gcsOutputDirectory: A Google Cloud Storage directory which path ends with
      '/'. The exported data is stored in the directory.
    genericMetadata: The common part of the operation metadata.
  """

  gcsOutputDirectory = _messages.StringField(1)
  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 2)


class GoogleCloudAiplatformV1ExportDataRequest(_messages.Message):
  r"""Request message for DatasetService.ExportData.

  Fields:
    exportConfig: Required. The desired output location.
  """

  exportConfig = _messages.MessageField('GoogleCloudAiplatformV1ExportDataConfig', 1)


class GoogleCloudAiplatformV1ExportDataResponse(_messages.Message):
  r"""Response message for DatasetService.ExportData.

  Fields:
    exportedFiles: All of the files that are exported in this export
      operation.
  """

  exportedFiles = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1ExportModelOperationMetadata(_messages.Message):
  r"""Details of ModelService.ExportModel operation.

  Fields:
    genericMetadata: The common part of the operation metadata.
    outputInfo: Output only. Information further describing the output of this
      Model export.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)
  outputInfo = _messages.MessageField('GoogleCloudAiplatformV1ExportModelOperationMetadataOutputInfo', 2)


class GoogleCloudAiplatformV1ExportModelOperationMetadataOutputInfo(_messages.Message):
  r"""Further describes the output of the ExportModel. Supplements
  ExportModelRequest.OutputConfig.

  Fields:
    artifactOutputUri: Output only. If the Model artifact is being exported to
      Google Cloud Storage this is the full path of the directory created,
      into which the Model files are being written to.
    imageOutputUri: Output only. If the Model image is being exported to
      Google Container Registry or Artifact Registry this is the full path of
      the image created.
  """

  artifactOutputUri = _messages.StringField(1)
  imageOutputUri = _messages.StringField(2)


class GoogleCloudAiplatformV1ExportModelRequest(_messages.Message):
  r"""Request message for ModelService.ExportModel.

  Fields:
    outputConfig: Required. The desired output location and configuration.
  """

  outputConfig = _messages.MessageField('GoogleCloudAiplatformV1ExportModelRequestOutputConfig', 1)


class GoogleCloudAiplatformV1ExportModelRequestOutputConfig(_messages.Message):
  r"""Output configuration for the Model export.

  Fields:
    artifactDestination: The Cloud Storage location where the Model artifact
      is to be written to. 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 should only be
      set when the `exportableContent` field of the
      [Model.supported_export_formats] object contains `ARTIFACT`.
    exportFormatId: The ID of the format in which the Model must be exported.
      Each Model lists the export formats it supports. If no value is provided
      here, then the first from the list of the Model's supported formats is
      used by default.
    imageDestination: The Google Container Registry or Artifact Registry uri
      where the Model container image will be copied to. This field should
      only be set when the `exportableContent` field of the
      [Model.supported_export_formats] object contains `IMAGE`.
  """

  artifactDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 1)
  exportFormatId = _messages.StringField(2)
  imageDestination = _messages.MessageField('GoogleCloudAiplatformV1ContainerRegistryDestination', 3)


class GoogleCloudAiplatformV1ExportModelResponse(_messages.Message):
  r"""Response message of ModelService.ExportModel operation."""


class GoogleCloudAiplatformV1FilterSplit(_messages.Message):
  r"""Assigns input data to training, validation, and test sets based on the
  given filters, data pieces not matched by any filter are ignored. Currently
  only supported for Datasets containing DataItems. If any of the filters in
  this message are to match nothing, then they can be set as '-' (the minus
  sign). Supported only for unstructured Datasets.

  Fields:
    testFilter: Required. A filter on DataItems of the Dataset. DataItems that
      match this filter are used to test the Model. A filter with same syntax
      as the one used in DatasetService.ListDataItems may be used. If a single
      DataItem is matched by more than one of the FilterSplit filters, then it
      is assigned to the first set that applies to it in the training,
      validation, test order.
    trainingFilter: Required. A filter on DataItems of the Dataset. DataItems
      that match this filter are used to train the Model. A filter with same
      syntax as the one used in DatasetService.ListDataItems may be used. If a
      single DataItem is matched by more than one of the FilterSplit filters,
      then it is assigned to the first set that applies to it in the training,
      validation, test order.
    validationFilter: Required. A filter on DataItems of the Dataset.
      DataItems that match this filter are used to validate the Model. A
      filter with same syntax as the one used in DatasetService.ListDataItems
      may be used. If a single DataItem is matched by more than one of the
      FilterSplit filters, then it is assigned to the first set that applies
      to it in the training, validation, test order.
  """

  testFilter = _messages.StringField(1)
  trainingFilter = _messages.StringField(2)
  validationFilter = _messages.StringField(3)


class GoogleCloudAiplatformV1FractionSplit(_messages.Message):
  r"""Assigns the input data to training, validation, and test sets as per the
  given fractions. Any of `training_fraction`, `validation_fraction` and
  `test_fraction` may optionally be provided, they must sum to up to 1. If the
  provided ones sum to less than 1, the remainder is assigned to sets as
  decided by AI Platform. If none of the fractions are set, by default roughly
  80% of data is used for training, 10% for validation, and 10% for test.

  Fields:
    testFraction: The fraction of the input data that is to be used to
      evaluate the Model.
    trainingFraction: The fraction of the input data that is to be used to
      train the Model.
    validationFraction: The fraction of the input data that is to be used to
      validate the Model.
  """

  testFraction = _messages.FloatField(1)
  trainingFraction = _messages.FloatField(2)
  validationFraction = _messages.FloatField(3)


class GoogleCloudAiplatformV1GcsDestination(_messages.Message):
  r"""The Google Cloud Storage location where the output is to be written to.

  Fields:
    outputUriPrefix: Required. Google Cloud Storage URI to output directory.
      If the uri doesn't end with '/', a '/' will be automatically appended.
      The directory is created if it doesn't exist.
  """

  outputUriPrefix = _messages.StringField(1)


class GoogleCloudAiplatformV1GcsSource(_messages.Message):
  r"""The Google Cloud Storage location for the input content.

  Fields:
    uris: Required. Google Cloud Storage URI(-s) to the input file(s). May
      contain wildcards. For more information on wildcards, see
      https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
  """

  uris = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1GenericOperationMetadata(_messages.Message):
  r"""Generic Metadata shared by all operations.

  Fields:
    createTime: Output only. Time when the operation was created.
    partialFailures: Output only. Partial failures encountered. E.g. single
      files that couldn't be read. This field should never exceed 20 entries.
      Status details field will contain standard GCP error details.
    updateTime: Output only. Time when the operation was updated for the last
      time. If the operation has finished (successfully or not), this is the
      finish time.
  """

  createTime = _messages.StringField(1)
  partialFailures = _messages.MessageField('GoogleRpcStatus', 2, repeated=True)
  updateTime = _messages.StringField(3)


class GoogleCloudAiplatformV1HyperparameterTuningJob(_messages.Message):
  r"""Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a
  Study specification and multiple CustomJobs with identical CustomJob
  specification.

  Enums:
    StateValueValuesEnum: Output only. The detailed state of the job.

  Messages:
    LabelsValue: The labels with user-defined metadata to organize
      HyperparameterTuningJobs. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels.

  Fields:
    createTime: Output only. Time when the HyperparameterTuningJob was
      created.
    displayName: Required. The display name of the HyperparameterTuningJob.
      The name can be up to 128 characters long and can be consist of any
      UTF-8 characters.
    encryptionSpec: Customer-managed encryption key options for a
      HyperparameterTuningJob. If this is set, then all resources created by
      the HyperparameterTuningJob will be encrypted with the provided
      encryption key.
    endTime: Output only. Time when the HyperparameterTuningJob entered any of
      the following states: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`,
      `JOB_STATE_CANCELLED`.
    error: Output only. Only populated when job's state is JOB_STATE_FAILED or
      JOB_STATE_CANCELLED.
    labels: The labels with user-defined metadata to organize
      HyperparameterTuningJobs. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels.
    maxFailedTrialCount: The number of failed Trials that need to be seen
      before failing the HyperparameterTuningJob. If set to 0, AI Platform
      decides how many Trials must fail before the whole job fails.
    maxTrialCount: Required. The desired total number of Trials.
    name: Output only. Resource name of the HyperparameterTuningJob.
    parallelTrialCount: Required. The desired number of Trials to run in
      parallel.
    startTime: Output only. Time when the HyperparameterTuningJob for the
      first time entered the `JOB_STATE_RUNNING` state.
    state: Output only. The detailed state of the job.
    studySpec: Required. Study configuration of the HyperparameterTuningJob.
    trialJobSpec: Required. The spec of a trial job. The same spec applies to
      the CustomJobs created in all the trials.
    trials: Output only. Trials of the HyperparameterTuningJob.
    updateTime: Output only. Time when the HyperparameterTuningJob was most
      recently updated.
  """

  class StateValueValuesEnum(_messages.Enum):
    r"""Output only. The detailed state of the job.

    Values:
      JOB_STATE_UNSPECIFIED: The job state is unspecified.
      JOB_STATE_QUEUED: The job has been just created or resumed and
        processing has not yet begun.
      JOB_STATE_PENDING: The service is preparing to run the job.
      JOB_STATE_RUNNING: The job is in progress.
      JOB_STATE_SUCCEEDED: The job completed successfully.
      JOB_STATE_FAILED: The job failed.
      JOB_STATE_CANCELLING: The job is being cancelled. From this state the
        job may only go to either `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED` or
        `JOB_STATE_CANCELLED`.
      JOB_STATE_CANCELLED: The job has been cancelled.
      JOB_STATE_PAUSED: The job has been stopped, and can be resumed.
    """
    JOB_STATE_UNSPECIFIED = 0
    JOB_STATE_QUEUED = 1
    JOB_STATE_PENDING = 2
    JOB_STATE_RUNNING = 3
    JOB_STATE_SUCCEEDED = 4
    JOB_STATE_FAILED = 5
    JOB_STATE_CANCELLING = 6
    JOB_STATE_CANCELLED = 7
    JOB_STATE_PAUSED = 8

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""The labels with user-defined metadata to organize
    HyperparameterTuningJobs. Label keys and values can be no longer than 64
    characters (Unicode codepoints), can only contain lowercase letters,
    numeric characters, underscores and dashes. International characters are
    allowed. See https://goo.gl/xmQnxf for more information and examples of
    labels.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  createTime = _messages.StringField(1)
  displayName = _messages.StringField(2)
  encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3)
  endTime = _messages.StringField(4)
  error = _messages.MessageField('GoogleRpcStatus', 5)
  labels = _messages.MessageField('LabelsValue', 6)
  maxFailedTrialCount = _messages.IntegerField(7, variant=_messages.Variant.INT32)
  maxTrialCount = _messages.IntegerField(8, variant=_messages.Variant.INT32)
  name = _messages.StringField(9)
  parallelTrialCount = _messages.IntegerField(10, variant=_messages.Variant.INT32)
  startTime = _messages.StringField(11)
  state = _messages.EnumField('StateValueValuesEnum', 12)
  studySpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpec', 13)
  trialJobSpec = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 14)
  trials = _messages.MessageField('GoogleCloudAiplatformV1Trial', 15, repeated=True)
  updateTime = _messages.StringField(16)


class GoogleCloudAiplatformV1ImportDataConfig(_messages.Message):
  r"""Describes the location from where we import data into a Dataset,
  together with the labels that will be applied to the DataItems and the
  Annotations.

  Messages:
    DataItemLabelsValue: Labels that will be applied to newly imported
      DataItems. If an identical DataItem as one being imported already exists
      in the Dataset, then these labels will be appended to these of the
      already existing one, and if labels with identical key is imported
      before, the old label value will be overwritten. If two DataItems are
      identical in the same import data operation, the labels will be combined
      and if key collision happens in this case, one of the values will be
      picked randomly. Two DataItems are considered identical if their content
      bytes are identical (e.g. image bytes or pdf bytes). These labels will
      be overridden by Annotation labels specified inside index file
      referenced by import_schema_uri, e.g. jsonl file.

  Fields:
    dataItemLabels: Labels that will be applied to newly imported DataItems.
      If an identical DataItem as one being imported already exists in the
      Dataset, then these labels will be appended to these of the already
      existing one, and if labels with identical key is imported before, the
      old label value will be overwritten. If two DataItems are identical in
      the same import data operation, the labels will be combined and if key
      collision happens in this case, one of the values will be picked
      randomly. Two DataItems are considered identical if their content bytes
      are identical (e.g. image bytes or pdf bytes). These labels will be
      overridden by Annotation labels specified inside index file referenced
      by import_schema_uri, e.g. jsonl file.
    gcsSource: The Google Cloud Storage location for the input content.
    importSchemaUri: Required. Points to a YAML file stored on Google Cloud
      Storage describing the import format. Validation will be done against
      the schema. The schema is defined as an [OpenAPI 3.0.2 Schema
      Object](https://tinyurl.com/y538mdwt).
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class DataItemLabelsValue(_messages.Message):
    r"""Labels that will be applied to newly imported DataItems. If an
    identical DataItem as one being imported already exists in the Dataset,
    then these labels will be appended to these of the already existing one,
    and if labels with identical key is imported before, the old label value
    will be overwritten. If two DataItems are identical in the same import
    data operation, the labels will be combined and if key collision happens
    in this case, one of the values will be picked randomly. Two DataItems are
    considered identical if their content bytes are identical (e.g. image
    bytes or pdf bytes). These labels will be overridden by Annotation labels
    specified inside index file referenced by import_schema_uri, e.g. jsonl
    file.

    Messages:
      AdditionalProperty: An additional property for a DataItemLabelsValue
        object.

    Fields:
      additionalProperties: Additional properties of type DataItemLabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a DataItemLabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  dataItemLabels = _messages.MessageField('DataItemLabelsValue', 1)
  gcsSource = _messages.MessageField('GoogleCloudAiplatformV1GcsSource', 2)
  importSchemaUri = _messages.StringField(3)


class GoogleCloudAiplatformV1ImportDataOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.ImportData.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1ImportDataRequest(_messages.Message):
  r"""Request message for DatasetService.ImportData.

  Fields:
    importConfigs: Required. The desired input locations. The contents of all
      input locations will be imported in one batch.
  """

  importConfigs = _messages.MessageField('GoogleCloudAiplatformV1ImportDataConfig', 1, repeated=True)


class GoogleCloudAiplatformV1ImportDataResponse(_messages.Message):
  r"""Response message for DatasetService.ImportData."""


class GoogleCloudAiplatformV1InputDataConfig(_messages.Message):
  r"""Specifies AI Platform owned input data to be used for training, and
  possibly evaluating, the Model.

  Fields:
    annotationSchemaUri: Applicable only to custom training with Datasets that
      have DataItems and Annotations. Cloud Storage URI that points to a YAML
      file describing the annotation schema. The schema is defined as an
      OpenAPI 3.0.2 [Schema Object](https://tinyurl.com/y538mdwt#schema-
      object). The schema files that can be used here are found in
      gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the
      chosen schema must be consistent with metadata of the Dataset specified
      by dataset_id. Only Annotations that both match this schema and belong
      to DataItems not ignored by the split method are used in respectively
      training, validation or test role, depending on the role of the DataItem
      they are on. When used in conjunction with annotations_filter, the
      Annotations used for training are filtered by both annotations_filter
      and annotation_schema_uri.
    annotationsFilter: Applicable only to Datasets that have DataItems and
      Annotations. A filter on Annotations of the Dataset. Only Annotations
      that both match this filter and belong to DataItems not ignored by the
      split method are used in respectively training, validation or test role,
      depending on the role of the DataItem they are on (for the auto-assigned
      that role is decided by AI Platform). A filter with same syntax as the
      one used in ListAnnotations may be used, but note here it filters across
      all Annotations of the Dataset, and not just within a single DataItem.
    bigqueryDestination: Only applicable to custom training with tabular
      Dataset with BigQuery source. The BigQuery project location where the
      training data is to be written to. In the given project a new dataset is
      created with name `dataset___` where timestamp is in
      YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into
      that dataset. In the dataset three tables are created, `training`,
      `validation` and `test`. * AIP_DATA_FORMAT = "bigquery". *
      AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" *
      AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" *
      AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
    datasetId: Required. The ID of the Dataset in the same Project and
      Location which data will be used to train the Model. The Dataset must
      use schema compatible with Model being trained, and what is compatible
      should be described in the used TrainingPipeline's
      training_task_definition. For tabular Datasets, all their data is
      exported to training, to pick and choose from.
    filterSplit: Split based on the provided filters for each set.
    fractionSplit: Split based on fractions defining the size of each set.
    gcsDestination: The Cloud Storage location where the training data is to
      be written to. In the given directory a new directory is created with
      name: `dataset---` where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ
      ISO-8601 format. All training input data is written into that directory.
      The AI Platform environment variables representing Cloud Storage data
      URIs are represented in the Cloud Storage wildcard format to support
      sharded data. e.g.: "gs://.../training-*.jsonl" * AIP_DATA_FORMAT =
      "jsonl" for non-tabular data, "csv" for tabular data *
      AIP_TRAINING_DATA_URI =
      "gcs_destination/dataset---/training-*.${AIP_DATA_FORMAT}" *
      AIP_VALIDATION_DATA_URI =
      "gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}" *
      AIP_TEST_DATA_URI =
      "gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}"
    predefinedSplit: Supported only for tabular Datasets. Split based on a
      predefined key.
    timestampSplit: Supported only for tabular Datasets. Split based on the
      timestamp of the input data pieces.
  """

  annotationSchemaUri = _messages.StringField(1)
  annotationsFilter = _messages.StringField(2)
  bigqueryDestination = _messages.MessageField('GoogleCloudAiplatformV1BigQueryDestination', 3)
  datasetId = _messages.StringField(4)
  filterSplit = _messages.MessageField('GoogleCloudAiplatformV1FilterSplit', 5)
  fractionSplit = _messages.MessageField('GoogleCloudAiplatformV1FractionSplit', 6)
  gcsDestination = _messages.MessageField('GoogleCloudAiplatformV1GcsDestination', 7)
  predefinedSplit = _messages.MessageField('GoogleCloudAiplatformV1PredefinedSplit', 8)
  timestampSplit = _messages.MessageField('GoogleCloudAiplatformV1TimestampSplit', 9)


class GoogleCloudAiplatformV1ListAnnotationsResponse(_messages.Message):
  r"""Response message for DatasetService.ListAnnotations.

  Fields:
    annotations: A list of Annotations that matches the specified filter in
      the request.
    nextPageToken: The standard List next-page token.
  """

  annotations = _messages.MessageField('GoogleCloudAiplatformV1Annotation', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListBatchPredictionJobsResponse(_messages.Message):
  r"""Response message for JobService.ListBatchPredictionJobs

  Fields:
    batchPredictionJobs: List of BatchPredictionJobs in the requested page.
    nextPageToken: A token to retrieve the next page of results. Pass to
      ListBatchPredictionJobsRequest.page_token to obtain that page.
  """

  batchPredictionJobs = _messages.MessageField('GoogleCloudAiplatformV1BatchPredictionJob', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListCustomJobsResponse(_messages.Message):
  r"""Response message for JobService.ListCustomJobs

  Fields:
    customJobs: List of CustomJobs in the requested page.
    nextPageToken: A token to retrieve the next page of results. Pass to
      ListCustomJobsRequest.page_token to obtain that page.
  """

  customJobs = _messages.MessageField('GoogleCloudAiplatformV1CustomJob', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListDataItemsResponse(_messages.Message):
  r"""Response message for DatasetService.ListDataItems.

  Fields:
    dataItems: A list of DataItems that matches the specified filter in the
      request.
    nextPageToken: The standard List next-page token.
  """

  dataItems = _messages.MessageField('GoogleCloudAiplatformV1DataItem', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListDataLabelingJobsResponse(_messages.Message):
  r"""Response message for JobService.ListDataLabelingJobs.

  Fields:
    dataLabelingJobs: A list of DataLabelingJobs that matches the specified
      filter in the request.
    nextPageToken: The standard List next-page token.
  """

  dataLabelingJobs = _messages.MessageField('GoogleCloudAiplatformV1DataLabelingJob', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListDatasetsResponse(_messages.Message):
  r"""Response message for DatasetService.ListDatasets.

  Fields:
    datasets: A list of Datasets that matches the specified filter in the
      request.
    nextPageToken: The standard List next-page token.
  """

  datasets = _messages.MessageField('GoogleCloudAiplatformV1Dataset', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListEndpointsResponse(_messages.Message):
  r"""Response message for EndpointService.ListEndpoints.

  Fields:
    endpoints: List of Endpoints in the requested page.
    nextPageToken: A token to retrieve the next page of results. Pass to
      ListEndpointsRequest.page_token to obtain that page.
  """

  endpoints = _messages.MessageField('GoogleCloudAiplatformV1Endpoint', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListHyperparameterTuningJobsResponse(_messages.Message):
  r"""Response message for JobService.ListHyperparameterTuningJobs

  Fields:
    hyperparameterTuningJobs: List of HyperparameterTuningJobs in the
      requested page. HyperparameterTuningJob.trials of the jobs will be not
      be returned.
    nextPageToken: A token to retrieve the next page of results. Pass to
      ListHyperparameterTuningJobsRequest.page_token to obtain that page.
  """

  hyperparameterTuningJobs = _messages.MessageField('GoogleCloudAiplatformV1HyperparameterTuningJob', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListModelEvaluationSlicesResponse(_messages.Message):
  r"""Response message for ModelService.ListModelEvaluationSlices.

  Fields:
    modelEvaluationSlices: List of ModelEvaluations in the requested page.
    nextPageToken: A token to retrieve next page of results. Pass to
      ListModelEvaluationSlicesRequest.page_token to obtain that page.
  """

  modelEvaluationSlices = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSlice', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListModelEvaluationsResponse(_messages.Message):
  r"""Response message for ModelService.ListModelEvaluations.

  Fields:
    modelEvaluations: List of ModelEvaluations in the requested page.
    nextPageToken: A token to retrieve next page of results. Pass to
      ListModelEvaluationsRequest.page_token to obtain that page.
  """

  modelEvaluations = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluation', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListModelsResponse(_messages.Message):
  r"""Response message for ModelService.ListModels

  Fields:
    models: List of Models in the requested page.
    nextPageToken: A token to retrieve next page of results. Pass to
      ListModelsRequest.page_token to obtain that page.
  """

  models = _messages.MessageField('GoogleCloudAiplatformV1Model', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1ListSpecialistPoolsResponse(_messages.Message):
  r"""Response message for SpecialistPoolService.ListSpecialistPools.

  Fields:
    nextPageToken: The standard List next-page token.
    specialistPools: A list of SpecialistPools that matches the specified
      filter in the request.
  """

  nextPageToken = _messages.StringField(1)
  specialistPools = _messages.MessageField('GoogleCloudAiplatformV1SpecialistPool', 2, repeated=True)


class GoogleCloudAiplatformV1ListTrainingPipelinesResponse(_messages.Message):
  r"""Response message for PipelineService.ListTrainingPipelines

  Fields:
    nextPageToken: A token to retrieve the next page of results. Pass to
      ListTrainingPipelinesRequest.page_token to obtain that page.
    trainingPipelines: List of TrainingPipelines in the requested page.
  """

  nextPageToken = _messages.StringField(1)
  trainingPipelines = _messages.MessageField('GoogleCloudAiplatformV1TrainingPipeline', 2, repeated=True)


class GoogleCloudAiplatformV1MachineSpec(_messages.Message):
  r"""Specification of a single machine.

  Enums:
    AcceleratorTypeValueValuesEnum: Immutable. The type of accelerator(s) that
      may be attached to the machine as per accelerator_count.

  Fields:
    acceleratorCount: The number of accelerators to attach to the machine.
    acceleratorType: Immutable. The type of accelerator(s) that may be
      attached to the machine as per accelerator_count.
    machineType: Immutable. The type of the machine. For the machine types
      supported for prediction, see https://tinyurl.com/aip-
      docs/predictions/machine-types. For machine types supported for creating
      a custom training job, see https://tinyurl.com/aip-
      docs/training/configure-compute. For DeployedModel this field is
      optional, and the default value is `n1-standard-2`. For
      BatchPredictionJob or as part of WorkerPoolSpec this field is required.
  """

  class AcceleratorTypeValueValuesEnum(_messages.Enum):
    r"""Immutable. The type of accelerator(s) that may be attached to the
    machine as per accelerator_count.

    Values:
      ACCELERATOR_TYPE_UNSPECIFIED: Unspecified accelerator type, which means
        no accelerator.
      NVIDIA_TESLA_K80: Nvidia Tesla K80 GPU.
      NVIDIA_TESLA_P100: Nvidia Tesla P100 GPU.
      NVIDIA_TESLA_V100: Nvidia Tesla V100 GPU.
      NVIDIA_TESLA_P4: Nvidia Tesla P4 GPU.
      NVIDIA_TESLA_T4: Nvidia Tesla T4 GPU.
      TPU_V2: TPU v2.
      TPU_V3: TPU v3.
    """
    ACCELERATOR_TYPE_UNSPECIFIED = 0
    NVIDIA_TESLA_K80 = 1
    NVIDIA_TESLA_P100 = 2
    NVIDIA_TESLA_V100 = 3
    NVIDIA_TESLA_P4 = 4
    NVIDIA_TESLA_T4 = 5
    TPU_V2 = 6
    TPU_V3 = 7

  acceleratorCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  acceleratorType = _messages.EnumField('AcceleratorTypeValueValuesEnum', 2)
  machineType = _messages.StringField(3)


class GoogleCloudAiplatformV1ManualBatchTuningParameters(_messages.Message):
  r"""Manual batch tuning parameters.

  Fields:
    batchSize: Immutable. The number of the records (e.g. instances) of the
      operation given in each batch to a machine replica. Machine type, and
      size of a single record should be considered when setting this
      parameter, higher value speeds up the batch operation's execution, but
      too high value will result in a whole batch not fitting in a machine's
      memory, and the whole operation will fail. The default value is 4.
  """

  batchSize = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1Measurement(_messages.Message):
  r"""A message representing a Measurement of a Trial. A Measurement contains
  the Metrics got by executing a Trial using suggested hyperparameter values.

  Fields:
    metrics: Output only. A list of metrics got by evaluating the objective
      functions using suggested Parameter values.
    stepCount: Output only. The number of steps the machine learning model has
      been trained for. Must be non-negative.
  """

  metrics = _messages.MessageField('GoogleCloudAiplatformV1MeasurementMetric', 1, repeated=True)
  stepCount = _messages.IntegerField(2)


class GoogleCloudAiplatformV1MeasurementMetric(_messages.Message):
  r"""A message representing a metric in the measurement.

  Fields:
    metricId: Output only. The ID of the Metric. The Metric should be defined
      in StudySpec's Metrics.
    value: Output only. The value for this metric.
  """

  metricId = _messages.StringField(1)
  value = _messages.FloatField(2)


class GoogleCloudAiplatformV1MigratableResource(_messages.Message):
  r"""Represents one resource that exists in automl.googleapis.com,
  datalabeling.googleapis.com or ml.googleapis.com.

  Fields:
    automlDataset: Output only. Represents one Dataset in
      automl.googleapis.com.
    automlModel: Output only. Represents one Model in automl.googleapis.com.
    dataLabelingDataset: Output only. Represents one Dataset in
      datalabeling.googleapis.com.
    lastMigrateTime: Output only. Timestamp when the last migration attempt on
      this MigratableResource started. Will not be set if there's no migration
      attempt on this MigratableResource.
    lastUpdateTime: Output only. Timestamp when this MigratableResource was
      last updated.
    mlEngineModelVersion: Output only. Represents one Version in
      ml.googleapis.com.
  """

  automlDataset = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceAutomlDataset', 1)
  automlModel = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceAutomlModel', 2)
  dataLabelingDataset = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceDataLabelingDataset', 3)
  lastMigrateTime = _messages.StringField(4)
  lastUpdateTime = _messages.StringField(5)
  mlEngineModelVersion = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceMlEngineModelVersion', 6)


class GoogleCloudAiplatformV1MigratableResourceAutomlDataset(_messages.Message):
  r"""Represents one Dataset in automl.googleapis.com.

  Fields:
    dataset: Full resource name of automl Dataset. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`.
    datasetDisplayName: The Dataset's display name in automl.googleapis.com.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1MigratableResourceAutomlModel(_messages.Message):
  r"""Represents one Model in automl.googleapis.com.

  Fields:
    model: Full resource name of automl Model. Format:
      `projects/{project}/locations/{location}/models/{model}`.
    modelDisplayName: The Model's display name in automl.googleapis.com.
  """

  model = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1MigratableResourceDataLabelingDataset(_messages.Message):
  r"""Represents one Dataset in datalabeling.googleapis.com.

  Fields:
    dataLabelingAnnotatedDatasets: The migratable AnnotatedDataset in
      datalabeling.googleapis.com belongs to the data labeling Dataset.
    dataset: Full resource name of data labeling Dataset. Format:
      `projects/{project}/datasets/{dataset}`.
    datasetDisplayName: The Dataset's display name in
      datalabeling.googleapis.com.
  """

  dataLabelingAnnotatedDatasets = _messages.MessageField('GoogleCloudAiplatformV1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset', 1, repeated=True)
  dataset = _messages.StringField(2)
  datasetDisplayName = _messages.StringField(3)


class GoogleCloudAiplatformV1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset(_messages.Message):
  r"""Represents one AnnotatedDataset in datalabeling.googleapis.com.

  Fields:
    annotatedDataset: Full resource name of data labeling AnnotatedDataset.
      Format: `projects/{project}/datasets/{dataset}/annotatedDatasets/{annota
      ted_dataset}`.
    annotatedDatasetDisplayName: The AnnotatedDataset's display name in
      datalabeling.googleapis.com.
  """

  annotatedDataset = _messages.StringField(1)
  annotatedDatasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1MigratableResourceMlEngineModelVersion(_messages.Message):
  r"""Represents one model Version in ml.googleapis.com.

  Fields:
    endpoint: The ml.googleapis.com endpoint that this model Version currently
      lives in. Example values: * ml.googleapis.com * us-centrall-
      ml.googleapis.com * europe-west4-ml.googleapis.com * asia-
      east1-ml.googleapis.com
    version: Full resource name of ml engine model Version. Format:
      `projects/{project}/models/{model}/versions/{version}`.
  """

  endpoint = _messages.StringField(1)
  version = _messages.StringField(2)


class GoogleCloudAiplatformV1MigrateResourceRequest(_messages.Message):
  r"""Config of migrating one resource from automl.googleapis.com,
  datalabeling.googleapis.com and ml.googleapis.com to AI Platform.

  Fields:
    migrateAutomlDatasetConfig: Config for migrating Dataset in
      automl.googleapis.com to AI Platform's Dataset.
    migrateAutomlModelConfig: Config for migrating Model in
      automl.googleapis.com to AI Platform's Model.
    migrateDataLabelingDatasetConfig: Config for migrating Dataset in
      datalabeling.googleapis.com to AI Platform's Dataset.
    migrateMlEngineModelVersionConfig: Config for migrating Version in
      ml.googleapis.com to AI Platform's Model.
  """

  migrateAutomlDatasetConfig = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateAutomlDatasetConfig', 1)
  migrateAutomlModelConfig = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateAutomlModelConfig', 2)
  migrateDataLabelingDatasetConfig = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateDataLabelingDatasetConfig', 3)
  migrateMlEngineModelVersionConfig = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateMlEngineModelVersionConfig', 4)


class GoogleCloudAiplatformV1MigrateResourceRequestMigrateAutomlDatasetConfig(_messages.Message):
  r"""Config for migrating Dataset in automl.googleapis.com to AI Platform's
  Dataset.

  Fields:
    dataset: Required. Full resource name of automl Dataset. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`.
    datasetDisplayName: Required. Display name of the Dataset in AI Platform.
      System will pick a display name if unspecified.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1MigrateResourceRequestMigrateAutomlModelConfig(_messages.Message):
  r"""Config for migrating Model in automl.googleapis.com to AI Platform's
  Model.

  Fields:
    model: Required. Full resource name of automl Model. Format:
      `projects/{project}/locations/{location}/models/{model}`.
    modelDisplayName: Optional. Display name of the model in AI Platform.
      System will pick a display name if unspecified.
  """

  model = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1MigrateResourceRequestMigrateDataLabelingDatasetConfig(_messages.Message):
  r"""Config for migrating Dataset in datalabeling.googleapis.com to AI
  Platform's Dataset.

  Fields:
    dataset: Required. Full resource name of data labeling Dataset. Format:
      `projects/{project}/datasets/{dataset}`.
    datasetDisplayName: Optional. Display name of the Dataset in AI Platform.
      System will pick a display name if unspecified.
    migrateDataLabelingAnnotatedDatasetConfigs: Optional. Configs for
      migrating AnnotatedDataset in datalabeling.googleapis.com to AI
      Platform's SavedQuery. The specified AnnotatedDatasets have to belong to
      the datalabeling Dataset.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)
  migrateDataLabelingAnnotatedDatasetConfigs = _messages.MessageField('GoogleCloudAiplatformV1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig', 3, repeated=True)


class GoogleCloudAiplatformV1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig(_messages.Message):
  r"""Config for migrating AnnotatedDataset in datalabeling.googleapis.com to
  AI Platform's SavedQuery.

  Fields:
    annotatedDataset: Required. Full resource name of data labeling
      AnnotatedDataset. Format: `projects/{project}/datasets/{dataset}/annotat
      edDatasets/{annotated_dataset}`.
  """

  annotatedDataset = _messages.StringField(1)


class GoogleCloudAiplatformV1MigrateResourceRequestMigrateMlEngineModelVersionConfig(_messages.Message):
  r"""Config for migrating version in ml.googleapis.com to AI Platform's
  Model.

  Fields:
    endpoint: Required. The ml.googleapis.com endpoint that this model version
      should be migrated from. Example values: * ml.googleapis.com * us-
      centrall-ml.googleapis.com * europe-west4-ml.googleapis.com * asia-
      east1-ml.googleapis.com
    modelDisplayName: Required. Display name of the model in AI Platform.
      System will pick a display name if unspecified.
    modelVersion: Required. Full resource name of ml engine model version.
      Format: `projects/{project}/models/{model}/versions/{version}`.
  """

  endpoint = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)
  modelVersion = _messages.StringField(3)


class GoogleCloudAiplatformV1MigrateResourceResponse(_messages.Message):
  r"""Describes a successfully migrated resource.

  Fields:
    dataset: Migrated Dataset's resource name.
    migratableResource: Before migration, the identifier in ml.googleapis.com,
      automl.googleapis.com or datalabeling.googleapis.com.
    model: Migrated Model's resource name.
  """

  dataset = _messages.StringField(1)
  migratableResource = _messages.MessageField('GoogleCloudAiplatformV1MigratableResource', 2)
  model = _messages.StringField(3)


class GoogleCloudAiplatformV1Model(_messages.Message):
  r"""A trained machine learning Model.

  Enums:
    SupportedDeploymentResourcesTypesValueListEntryValuesEnum:

  Messages:
    LabelsValue: The labels with user-defined metadata to organize your
      Models. Label keys and values can be no longer than 64 characters
      (Unicode codepoints), can only contain lowercase letters, numeric
      characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels.

  Fields:
    artifactUri: Immutable. The path to the directory containing the Model
      artifact and any of its supporting files. Not present for AutoML Models.
    containerSpec: Input only. The specification of the container that is to
      be used when deploying this Model. The specification is ingested upon
      ModelService.UploadModel, and all binaries it contains are copied and
      stored internally by AI Platform. Not present for AutoML Models.
    createTime: Output only. Timestamp when this Model was uploaded into AI
      Platform.
    deployedModels: Output only. The pointers to DeployedModels created from
      this Model. Note that Model could have been deployed to Endpoints in
      different Locations.
    description: The description of the Model.
    displayName: Required. The display name of the Model. The name can be up
      to 128 characters long and can be consist of any UTF-8 characters.
    encryptionSpec: Customer-managed encryption key spec for a Model. If set,
      this Model and all sub-resources of this Model will be secured by this
      key.
    etag: Used to perform consistent read-modify-write updates. If not set, a
      blind "overwrite" update happens.
    labels: The labels with user-defined metadata to organize your Models.
      Label keys and values can be no longer than 64 characters (Unicode
      codepoints), can only contain lowercase letters, numeric characters,
      underscores and dashes. International characters are allowed. See
      https://goo.gl/xmQnxf for more information and examples of labels.
    metadata: Immutable. An additional information about the Model; the schema
      of the metadata can be found in metadata_schema. Unset if the Model does
      not have any additional information.
    metadataSchemaUri: Immutable. Points to a YAML file stored on Google Cloud
      Storage describing additional information about the Model, that is
      specific to it. Unset if the Model does not have any additional
      information. The schema is defined as an OpenAPI 3.0.2 [Schema
      Object](https://tinyurl.com/y538mdwt#schema-object). AutoML Models
      always have this field populated by AI Platform, if no additional
      metadata is needed, this field is set to an empty string. Note: The URI
      given on output will be immutable and probably different, including the
      URI scheme, than the one given on input. The output URI will point to a
      location where the user only has a read access.
    name: The resource name of the Model.
    predictSchemata: The schemata that describe formats of the Model's
      predictions and explanations as given and returned via
      PredictionService.Predict and PredictionService.Explain.
    supportedDeploymentResourcesTypes: Output only. When this Model is
      deployed, its prediction resources are described by the
      `prediction_resources` field of the Endpoint.deployed_models object.
      Because not all Models support all resource configuration types, the
      configuration types this Model supports are listed here. If no
      configuration types are listed, the Model cannot be deployed to an
      Endpoint and does not support online predictions
      (PredictionService.Predict or PredictionService.Explain). Such a Model
      can serve predictions by using a BatchPredictionJob, if it has at least
      one entry each in supported_input_storage_formats and
      supported_output_storage_formats.
    supportedExportFormats: Output only. The formats in which this Model may
      be exported. If empty, this Model is not available for export.
    supportedInputStorageFormats: Output only. The formats this Model supports
      in BatchPredictionJob.input_config. If
      PredictSchemata.instance_schema_uri exists, the instances should be
      given as per that schema. The possible formats are: * `jsonl` The JSON
      Lines format, where each instance is a single line. Uses GcsSource. *
      `csv` The CSV format, where each instance is a single comma-separated
      line. The first line in the file is the header, containing comma-
      separated field names. Uses GcsSource. * `tf-record` The TFRecord
      format, where each instance is a single record in tfrecord syntax. Uses
      GcsSource. * `tf-record-gzip` Similar to `tf-record`, but the file is
      gzipped. Uses GcsSource. * `bigquery` Each instance is a single row in
      BigQuery. Uses BigQuerySource. * `file-list` Each line of the file is
      the location of an instance to process, uses `gcs_source` field of the
      InputConfig object. If this Model doesn't support any of these formats
      it means it cannot be used with a BatchPredictionJob. However, if it has
      supported_deployment_resources_types, it could serve online predictions
      by using PredictionService.Predict or PredictionService.Explain.
    supportedOutputStorageFormats: Output only. The formats this Model
      supports in BatchPredictionJob.output_config. If both
      PredictSchemata.instance_schema_uri and
      PredictSchemata.prediction_schema_uri exist, the predictions are
      returned together with their instances. In other words, the prediction
      has the original instance data first, followed by the actual prediction
      content (as per the schema). The possible formats are: * `jsonl` The
      JSON Lines format, where each prediction is a single line. Uses
      GcsDestination. * `csv` The CSV format, where each prediction is a
      single comma-separated line. The first line in the file is the header,
      containing comma-separated field names. Uses GcsDestination. *
      `bigquery` Each prediction is a single row in a BigQuery table, uses
      BigQueryDestination . If this Model doesn't support any of these formats
      it means it cannot be used with a BatchPredictionJob. However, if it has
      supported_deployment_resources_types, it could serve online predictions
      by using PredictionService.Predict or PredictionService.Explain.
    trainingPipeline: Output only. The resource name of the TrainingPipeline
      that uploaded this Model, if any.
    updateTime: Output only. Timestamp when this Model was most recently
      updated.
  """

  class SupportedDeploymentResourcesTypesValueListEntryValuesEnum(_messages.Enum):
    r"""SupportedDeploymentResourcesTypesValueListEntryValuesEnum enum type.

    Values:
      DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED: Should not be used.
      DEDICATED_RESOURCES: Resources that are dedicated to the DeployedModel,
        and that need a higher degree of manual configuration.
      AUTOMATIC_RESOURCES: Resources that to large degree are decided by AI
        Platform, and require only a modest additional configuration.
    """
    DEPLOYMENT_RESOURCES_TYPE_UNSPECIFIED = 0
    DEDICATED_RESOURCES = 1
    AUTOMATIC_RESOURCES = 2

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""The labels with user-defined metadata to organize your Models. Label
    keys and values can be no longer than 64 characters (Unicode codepoints),
    can only contain lowercase letters, numeric characters, underscores and
    dashes. International characters are allowed. See https://goo.gl/xmQnxf
    for more information and examples of labels.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  artifactUri = _messages.StringField(1)
  containerSpec = _messages.MessageField('GoogleCloudAiplatformV1ModelContainerSpec', 2)
  createTime = _messages.StringField(3)
  deployedModels = _messages.MessageField('GoogleCloudAiplatformV1DeployedModelRef', 4, repeated=True)
  description = _messages.StringField(5)
  displayName = _messages.StringField(6)
  encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 7)
  etag = _messages.StringField(8)
  labels = _messages.MessageField('LabelsValue', 9)
  metadata = _messages.MessageField('extra_types.JsonValue', 10)
  metadataSchemaUri = _messages.StringField(11)
  name = _messages.StringField(12)
  predictSchemata = _messages.MessageField('GoogleCloudAiplatformV1PredictSchemata', 13)
  supportedDeploymentResourcesTypes = _messages.EnumField('SupportedDeploymentResourcesTypesValueListEntryValuesEnum', 14, repeated=True)
  supportedExportFormats = _messages.MessageField('GoogleCloudAiplatformV1ModelExportFormat', 15, repeated=True)
  supportedInputStorageFormats = _messages.StringField(16, repeated=True)
  supportedOutputStorageFormats = _messages.StringField(17, repeated=True)
  trainingPipeline = _messages.StringField(18)
  updateTime = _messages.StringField(19)


class GoogleCloudAiplatformV1ModelContainerSpec(_messages.Message):
  r"""Specification of a container for serving predictions. This message is a
  subset of the Kubernetes Container v1 core
  [specification](https://tinyurl.com/k8s-io-api/v1.18/#container-v1-core).

  Fields:
    args: Immutable. Specifies arguments for the command that runs when the
      container starts. This overrides the container's
      [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd). Specify
      this field as an array of executable and arguments, similar to a Docker
      `CMD`'s "default parameters" form. If you don't specify this field but
      do specify the command field, then the command from the `command` field
      runs without any additional arguments. See the [Kubernetes
      documentation](https://tinyurl.com/y8bvllf4) about how the `command` and
      `args` fields interact with a container's `ENTRYPOINT` and `CMD`. If you
      don't specify this field and don't specify the `command` field, then the
      container's
      [`ENTRYPOINT`](https://docs.docker.com/engine/reference/builder/#cmd)
      and `CMD` determine what runs based on their default behavior. See the
      Docker documentation about how `CMD` and `ENTRYPOINT`
      [interact](https://tinyurl.com/h3kdcgs). In this field, you can
      reference environment variables [set by AI
      Platform](https://tinyurl.com/cust-cont-reqs#aip-variables) and
      environment variables set in the env field. You cannot reference
      environment variables set in the Docker image. In order for environment
      variables to be expanded, reference them by using the following syntax:
      $(VARIABLE_NAME) Note that this differs from Bash variable expansion,
      which does not use parentheses. If a variable cannot be resolved, the
      reference in the input string is used unchanged. To avoid variable
      expansion, you can escape this syntax with `$$`; for example:
      $$(VARIABLE_NAME) This field corresponds to the `args` field of the
      Kubernetes Containers [v1 core API](https://tinyurl.com/k8s-io-
      api/v1.18/#container-v1-core).
    command: Immutable. Specifies the command that runs when the container
      starts. This overrides the container's [ENTRYPOINT](https://docs.docker.
      com/engine/reference/builder/#entrypoint). Specify this field as an
      array of executable and arguments, similar to a Docker `ENTRYPOINT`'s
      "exec" form, not its "shell" form. If you do not specify this field,
      then the container's `ENTRYPOINT` runs, in conjunction with the args
      field or the container's
      [`CMD`](https://docs.docker.com/engine/reference/builder/#cmd), if
      either exists. If this field is not specified and the container does not
      have an `ENTRYPOINT`, then refer to the Docker documentation about how
      `CMD` and `ENTRYPOINT` [interact](https://tinyurl.com/h3kdcgs). If you
      specify this field, then you can also specify the `args` field to
      provide additional arguments for this command. However, if you specify
      this field, then the container's `CMD` is ignored. See the [Kubernetes
      documentation](https://tinyurl.com/y8bvllf4) about how the `command` and
      `args` fields interact with a container's `ENTRYPOINT` and `CMD`. In
      this field, you can reference environment variables [set by AI
      Platform](https://tinyurl.com/cust-cont-reqs#aip-variables) and
      environment variables set in the env field. You cannot reference
      environment variables set in the Docker image. In order for environment
      variables to be expanded, reference them by using the following syntax:
      $(VARIABLE_NAME) Note that this differs from Bash variable expansion,
      which does not use parentheses. If a variable cannot be resolved, the
      reference in the input string is used unchanged. To avoid variable
      expansion, you can escape this syntax with `$$`; for example:
      $$(VARIABLE_NAME) This field corresponds to the `command` field of the
      Kubernetes Containers [v1 core API](https://tinyurl.com/k8s-io-
      api/v1.18/#container-v1-core).
    env: Immutable. List of environment variables to set in the container.
      After the container starts running, code running in the container can
      read these environment variables. Additionally, the command and args
      fields can reference these variables. Later entries in this list can
      also reference earlier entries. For example, the following example sets
      the variable `VAR_2` to have the value `foo bar`: ```json [ { "name":
      "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" }
      ] ``` If you switch the order of the variables in the example, then the
      expansion does not occur. This field corresponds to the `env` field of
      the Kubernetes Containers [v1 core API](https://tinyurl.com/k8s-io-
      api/v1.18/#container-v1-core).
    healthRoute: Immutable. HTTP path on the container to send health checks
      to. AI Platform intermittently sends GET requests to this path on the
      container's IP address and port to check that the container is healthy.
      Read more about [health checks](https://tinyurl.com/cust-cont-
      reqs#checks). For example, if you set this field to `/bar`, then AI
      Platform intermittently sends a GET request to the `/bar` path on the
      port of your container specified by the first value of this
      `ModelContainerSpec`'s ports field. If you don't specify this field, it
      defaults to the following value when you deploy this Model to an
      Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL :predict
      The placeholders in this value are replaced as follows: * ENDPOINT: The
      last segment (following `endpoints/`)of the Endpoint.name][] field of
      the Endpoint where this Model has been deployed. (AI Platform makes this
      value available to your container code as the
      [`AIP_ENDPOINT_ID`](https://tinyurl.com/cust-cont-reqs#aip-variables)
      environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the
      `DeployedModel`. (AI Platform makes this value available to your
      container code as the
      [`AIP_DEPLOYED_MODEL_ID`](https://tinyurl.com/cust-cont-reqs#aip-
      variables) environment variable.)
    imageUri: Required. Immutable. URI of the Docker image to be used as the
      custom container for serving predictions. This URI must identify an
      image in Artifact Registry or Container Registry. Learn more about the
      container publishing requirements, including permissions requirements
      for the AI Platform Service Agent, [here](https://tinyurl.com/cust-cont-
      reqs#publishing). The container image is ingested upon
      ModelService.UploadModel, stored internally, and this original path is
      afterwards not used. To learn about the requirements for the Docker
      image itself, see [Custom container
      requirements](https://tinyurl.com/cust-cont-reqs).
    ports: Immutable. List of ports to expose from the container. AI Platform
      sends any prediction requests that it receives to the first port on this
      list. AI Platform also sends [liveness and health
      checks](https://tinyurl.com/cust-cont-reqs#health) to this port. If you
      do not specify this field, it defaults to following value: ```json [ {
      "containerPort": 8080 } ] ``` AI Platform does not use ports other than
      the first one listed. This field corresponds to the `ports` field of the
      Kubernetes Containers [v1 core API](https://tinyurl.com/k8s-io-
      api/v1.18/#container-v1-core).
    predictRoute: Immutable. HTTP path on the container to send prediction
      requests to. AI Platform forwards requests sent using
      projects.locations.endpoints.predict to this path on the container's IP
      address and port. AI Platform then returns the container's response in
      the API response. For example, if you set this field to `/foo`, then
      when AI Platform receives a prediction request, it forwards the request
      body in a POST request to the `/foo` path on the port of your container
      specified by the first value of this `ModelContainerSpec`'s ports field.
      If you don't specify this field, it defaults to the following value when
      you deploy this Model to an Endpoint:
      /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The
      placeholders in this value are replaced as follows: * ENDPOINT: The last
      segment (following `endpoints/`)of the Endpoint.name][] field of the
      Endpoint where this Model has been deployed. (AI Platform makes this
      value available to your container code as the
      [`AIP_ENDPOINT_ID`](https://tinyurl.com/cust-cont-reqs#aip-variables)
      environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the
      `DeployedModel`. (AI Platform makes this value available to your
      container code as the [`AIP_DEPLOYED_MODEL_ID` environment
      variable](https://tinyurl.com/cust-cont-reqs#aip-variables).)
  """

  args = _messages.StringField(1, repeated=True)
  command = _messages.StringField(2, repeated=True)
  env = _messages.MessageField('GoogleCloudAiplatformV1EnvVar', 3, repeated=True)
  healthRoute = _messages.StringField(4)
  imageUri = _messages.StringField(5)
  ports = _messages.MessageField('GoogleCloudAiplatformV1Port', 6, repeated=True)
  predictRoute = _messages.StringField(7)


class GoogleCloudAiplatformV1ModelEvaluation(_messages.Message):
  r"""A collection of metrics calculated by comparing Model's predictions on
  all of the test data against annotations from the test data.

  Fields:
    createTime: Output only. Timestamp when this ModelEvaluation was created.
    metrics: Output only. Evaluation metrics of the Model. The schema of the
      metrics is stored in metrics_schema_uri
    metricsSchemaUri: Output only. Points to a YAML file stored on Google
      Cloud Storage describing the metrics of this ModelEvaluation. The schema
      is defined as an OpenAPI 3.0.2 [Schema
      Object](https://tinyurl.com/y538mdwt#schema-object).
    name: Output only. The resource name of the ModelEvaluation.
    sliceDimensions: Output only. All possible dimensions of
      ModelEvaluationSlices. The dimensions can be used as the filter of the
      ModelService.ListModelEvaluationSlices request, in the form of
      `slice.dimension = `.
  """

  createTime = _messages.StringField(1)
  metrics = _messages.MessageField('extra_types.JsonValue', 2)
  metricsSchemaUri = _messages.StringField(3)
  name = _messages.StringField(4)
  sliceDimensions = _messages.StringField(5, repeated=True)


class GoogleCloudAiplatformV1ModelEvaluationSlice(_messages.Message):
  r"""A collection of metrics calculated by comparing Model's predictions on a
  slice of the test data against ground truth annotations.

  Fields:
    createTime: Output only. Timestamp when this ModelEvaluationSlice was
      created.
    metrics: Output only. Sliced evaluation metrics of the Model. The schema
      of the metrics is stored in metrics_schema_uri
    metricsSchemaUri: Output only. Points to a YAML file stored on Google
      Cloud Storage describing the metrics of this ModelEvaluationSlice. The
      schema is defined as an OpenAPI 3.0.2 [Schema
      Object](https://tinyurl.com/y538mdwt#schema-object).
    name: Output only. The resource name of the ModelEvaluationSlice.
    slice: Output only. The slice of the test data that is used to evaluate
      the Model.
  """

  createTime = _messages.StringField(1)
  metrics = _messages.MessageField('extra_types.JsonValue', 2)
  metricsSchemaUri = _messages.StringField(3)
  name = _messages.StringField(4)
  slice = _messages.MessageField('GoogleCloudAiplatformV1ModelEvaluationSliceSlice', 5)


class GoogleCloudAiplatformV1ModelEvaluationSliceSlice(_messages.Message):
  r"""Definition of a slice.

  Fields:
    dimension: Output only. The dimension of the slice. Well-known dimensions
      are: * `annotationSpec`: This slice is on the test data that has either
      ground truth or prediction with AnnotationSpec.display_name equals to
      value.
    value: Output only. The value of the dimension in this slice.
  """

  dimension = _messages.StringField(1)
  value = _messages.StringField(2)


class GoogleCloudAiplatformV1ModelExportFormat(_messages.Message):
  r"""Represents export format supported by the Model. All formats export to
  Google Cloud Storage.

  Enums:
    ExportableContentsValueListEntryValuesEnum:

  Fields:
    exportableContents: Output only. The content of this Model that may be
      exported.
    id: Output only. The ID of the export format. The possible format IDs are:
      * `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. * `core-ml` Used for iOS
      mobile devices. * `custom-trained` A Model that was uploaded or trained
      by custom code.
  """

  class ExportableContentsValueListEntryValuesEnum(_messages.Enum):
    r"""ExportableContentsValueListEntryValuesEnum enum type.

    Values:
      EXPORTABLE_CONTENT_UNSPECIFIED: Should not be used.
      ARTIFACT: Model artifact and any of its supported files. Will be
        exported to the location specified by the `artifactDestination` field
        of the ExportModelRequest.output_config object.
      IMAGE: The container image that is to be used when deploying this Model.
        Will be exported to the location specified by the `imageDestination`
        field of the ExportModelRequest.output_config object.
    """
    EXPORTABLE_CONTENT_UNSPECIFIED = 0
    ARTIFACT = 1
    IMAGE = 2

  exportableContents = _messages.EnumField('ExportableContentsValueListEntryValuesEnum', 1, repeated=True)
  id = _messages.StringField(2)


class GoogleCloudAiplatformV1Port(_messages.Message):
  r"""Represents a network port in a container.

  Fields:
    containerPort: The number of the port to expose on the pod's IP address.
      Must be a valid port number, between 1 and 65535 inclusive.
  """

  containerPort = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1PredefinedSplit(_messages.Message):
  r"""Assigns input data to training, validation, and test sets based on the
  value of a provided key. Supported only for tabular Datasets.

  Fields:
    key: Required. The key is a name of one of the Dataset's data columns. The
      value of the key (either the label's value or value in the column) must
      be one of {`training`, `validation`, `test`}, and it defines to which
      set the given piece of data is assigned. If for a piece of data the key
      is not present or has an invalid value, that piece is ignored by the
      pipeline.
  """

  key = _messages.StringField(1)


class GoogleCloudAiplatformV1PredictRequest(_messages.Message):
  r"""Request message for PredictionService.Predict.

  Fields:
    instances: Required. The instances that are the input to the prediction
      call. A DeployedModel may have an upper limit on the number of instances
      it supports per request, and when it is exceeded the prediction call
      errors in case of AutoML Models, or, in case of customer created Models,
      the behaviour is as documented by that Model. The schema of any single
      instance may be specified via Endpoint's DeployedModels' Model's
      PredictSchemata's instance_schema_uri.
    parameters: The parameters that govern the prediction. The schema of the
      parameters may be specified via Endpoint's DeployedModels' Model's
      PredictSchemata's parameters_schema_uri.
  """

  instances = _messages.MessageField('extra_types.JsonValue', 1, repeated=True)
  parameters = _messages.MessageField('extra_types.JsonValue', 2)


class GoogleCloudAiplatformV1PredictResponse(_messages.Message):
  r"""Response message for PredictionService.Predict.

  Fields:
    deployedModelId: ID of the Endpoint's DeployedModel that served this
      prediction.
    predictions: The predictions that are the output of the predictions call.
      The schema of any single prediction may be specified via Endpoint's
      DeployedModels' Model's PredictSchemata's prediction_schema_uri.
  """

  deployedModelId = _messages.StringField(1)
  predictions = _messages.MessageField('extra_types.JsonValue', 2, repeated=True)


class GoogleCloudAiplatformV1PredictSchemata(_messages.Message):
  r"""Contains the schemata used in Model's predictions and explanations via
  PredictionService.Predict, PredictionService.Explain and BatchPredictionJob.

  Fields:
    instanceSchemaUri: Immutable. Points to a YAML file stored on Google Cloud
      Storage describing the format of a single instance, which are used in
      PredictRequest.instances, ExplainRequest.instances and
      BatchPredictionJob.input_config. The schema is defined as an OpenAPI
      3.0.2 [Schema Object](https://tinyurl.com/y538mdwt#schema-object).
      AutoML Models always have this field populated by AI Platform. Note: The
      URI given on output will be immutable and probably different, including
      the URI scheme, than the one given on input. The output URI will point
      to a location where the user only has a read access.
    parametersSchemaUri: Immutable. Points to a YAML file stored on Google
      Cloud Storage describing the parameters of prediction and explanation
      via PredictRequest.parameters, ExplainRequest.parameters and
      BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI
      3.0.2 [Schema Object](https://tinyurl.com/y538mdwt#schema-object).
      AutoML Models always have this field populated by AI Platform, if no
      parameters are supported, then it is set to an empty string. Note: The
      URI given on output will be immutable and probably different, including
      the URI scheme, than the one given on input. The output URI will point
      to a location where the user only has a read access.
    predictionSchemaUri: Immutable. Points to a YAML file stored on Google
      Cloud Storage describing the format of a single prediction produced by
      this Model, which are returned via PredictResponse.predictions,
      ExplainResponse.explanations, and BatchPredictionJob.output_config. The
      schema is defined as an OpenAPI 3.0.2 [Schema
      Object](https://tinyurl.com/y538mdwt#schema-object). AutoML Models
      always have this field populated by AI Platform. Note: The URI given on
      output will be immutable and probably different, including the URI
      scheme, than the one given on input. The output URI will point to a
      location where the user only has a read access.
  """

  instanceSchemaUri = _messages.StringField(1)
  parametersSchemaUri = _messages.StringField(2)
  predictionSchemaUri = _messages.StringField(3)


class GoogleCloudAiplatformV1PythonPackageSpec(_messages.Message):
  r"""The spec of a Python packaged code.

  Fields:
    args: Command line arguments to be passed to the Python task.
    env: Environment variables to be passed to the python module.
    executorImageUri: Required. The URI of a container image in the Container
      Registry that will run the provided python package. AI Platform provides
      wide range of executor images with pre-installed packages to meet users'
      various use cases. Only one of the provided images can be set here.
    packageUris: Required. The Google Cloud Storage location of the Python
      package files which are the training program and its dependent packages.
      The maximum number of package URIs is 100.
    pythonModule: Required. The Python module name to run after installing the
      packages.
  """

  args = _messages.StringField(1, repeated=True)
  env = _messages.MessageField('GoogleCloudAiplatformV1EnvVar', 2, repeated=True)
  executorImageUri = _messages.StringField(3)
  packageUris = _messages.StringField(4, repeated=True)
  pythonModule = _messages.StringField(5)


class GoogleCloudAiplatformV1ResourcesConsumed(_messages.Message):
  r"""Statistics information about resource consumption.

  Fields:
    replicaHours: Output only. The number of replica hours used. Note that
      many replicas may run in parallel, and additionally any given work may
      be queued for some time. Therefore this value is not strictly related to
      wall time.
  """

  replicaHours = _messages.FloatField(1)


class GoogleCloudAiplatformV1SampleConfig(_messages.Message):
  r"""Active learning data sampling config. For every active learning labeling
  iteration, it will select a batch of data based on the sampling strategy.

  Enums:
    SampleStrategyValueValuesEnum: Field to choose sampling strategy. Sampling
      strategy will decide which data should be selected for human labeling in
      every batch.

  Fields:
    followingBatchSamplePercentage: The percentage of data needed to be
      labeled in each following batch (except the first batch).
    initialBatchSamplePercentage: The percentage of data needed to be labeled
      in the first batch.
    sampleStrategy: Field to choose sampling strategy. Sampling strategy will
      decide which data should be selected for human labeling in every batch.
  """

  class SampleStrategyValueValuesEnum(_messages.Enum):
    r"""Field to choose sampling strategy. Sampling strategy will decide which
    data should be selected for human labeling in every batch.

    Values:
      SAMPLE_STRATEGY_UNSPECIFIED: Default will be treated as UNCERTAINTY.
      UNCERTAINTY: Sample the most uncertain data to label.
    """
    SAMPLE_STRATEGY_UNSPECIFIED = 0
    UNCERTAINTY = 1

  followingBatchSamplePercentage = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  initialBatchSamplePercentage = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  sampleStrategy = _messages.EnumField('SampleStrategyValueValuesEnum', 3)


class GoogleCloudAiplatformV1Scheduling(_messages.Message):
  r"""All parameters related to queuing and scheduling of custom jobs.

  Fields:
    restartJobOnWorkerRestart: Restarts the entire CustomJob if a worker gets
      restarted. This feature can be used by distributed training jobs that
      are not resilient to workers leaving and joining a job.
    timeout: The maximum job running time. The default is 7 days.
  """

  restartJobOnWorkerRestart = _messages.BooleanField(1)
  timeout = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaAnnotationSpecColor(_messages.Message):
  r"""An entry of mapping between color and AnnotationSpec. The mapping is
  used in segmentation mask.

  Fields:
    color: The color of the AnnotationSpec in a segmentation mask.
    displayName: The display name of the AnnotationSpec represented by the
      color in the segmentation mask.
    id: The ID of the AnnotationSpec represented by the color in the
      segmentation mask.
  """

  color = _messages.MessageField('GoogleTypeColor', 1)
  displayName = _messages.StringField(2)
  id = _messages.StringField(3)


class GoogleCloudAiplatformV1SchemaImageBoundingBoxAnnotation(_messages.Message):
  r"""Annotation details specific to image object detection.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    xMax: The rightmost coordinate of the bounding box.
    xMin: The leftmost coordinate of the bounding box.
    yMax: The bottommost coordinate of the bounding box.
    yMin: The topmost coordinate of the bounding box.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  xMax = _messages.FloatField(3)
  xMin = _messages.FloatField(4)
  yMax = _messages.FloatField(5)
  yMin = _messages.FloatField(6)


class GoogleCloudAiplatformV1SchemaImageClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to image classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaImageDataItem(_messages.Message):
  r"""Payload of Image DataItem.

  Fields:
    gcsUri: Required. Google Cloud Storage URI points to the original image in
      user's bucket. The image is up to 30MB in size.
    mimeType: Output only. The mime type of the content of the image. Only the
      images in below listed mime types are supported. - image/jpeg -
      image/gif - image/png - image/webp - image/bmp - image/tiff -
      image/vnd.microsoft.icon
  """

  gcsUri = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaImageDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Image DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Image DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaImageSegmentationAnnotation(_messages.Message):
  r"""Annotation details specific to image segmentation.

  Fields:
    maskAnnotation: Mask based segmentation annotation. Only one mask
      annotation can exist for one image.
    polygonAnnotation: Polygon annotation.
    polylineAnnotation: Polyline annotation.
  """

  maskAnnotation = _messages.MessageField('GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationMaskAnnotation', 1)
  polygonAnnotation = _messages.MessageField('GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationPolygonAnnotation', 2)
  polylineAnnotation = _messages.MessageField('GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationPolylineAnnotation', 3)


class GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationMaskAnnotation(_messages.Message):
  r"""The mask based segmentation annotation.

  Fields:
    annotationSpecColors: The mapping between color and AnnotationSpec for
      this Annotation.
    maskGcsUri: Google Cloud Storage URI that points to the mask image. The
      image must be in PNG format. It must have the same size as the
      DataItem's image. Each pixel in the image mask represents the
      AnnotationSpec which the pixel in the image DataItem belong to. Each
      color is mapped to one AnnotationSpec based on annotation_spec_colors.
  """

  annotationSpecColors = _messages.MessageField('GoogleCloudAiplatformV1SchemaAnnotationSpecColor', 1, repeated=True)
  maskGcsUri = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationPolygonAnnotation(_messages.Message):
  r"""Represents a polygon in image.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    vertexes: The vertexes are connected one by one and the last vertex is
      connected to the first one to represent a polygon.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  vertexes = _messages.MessageField('GoogleCloudAiplatformV1SchemaVertex', 3, repeated=True)


class GoogleCloudAiplatformV1SchemaImageSegmentationAnnotationPolylineAnnotation(_messages.Message):
  r"""Represents a polyline in image.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    vertexes: The vertexes are connected one by one and the last vertex in not
      connected to the first one.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  vertexes = _messages.MessageField('GoogleCloudAiplatformV1SchemaVertex', 3, repeated=True)


class GoogleCloudAiplatformV1SchemaPredictInstanceImageClassificationPredictionInstance(_messages.Message):
  r"""Prediction input format for Image Classification.

  Fields:
    content: The image bytes or GCS URI to make the prediction on.
    mimeType: The MIME type of the content of the image. Only the images in
      below listed MIME types are supported. - image/jpeg - image/gif -
      image/png - image/webp - image/bmp - image/tiff -
      image/vnd.microsoft.icon
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaPredictInstanceImageObjectDetectionPredictionInstance(_messages.Message):
  r"""Prediction input format for Image Object Detection.

  Fields:
    content: The image bytes or GCS URI to make the prediction on.
    mimeType: The MIME type of the content of the image. Only the images in
      below listed MIME types are supported. - image/jpeg - image/gif -
      image/png - image/webp - image/bmp - image/tiff -
      image/vnd.microsoft.icon
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaPredictInstanceImageSegmentationPredictionInstance(_messages.Message):
  r"""Prediction input format for Image Segmentation.

  Fields:
    content: The image bytes to make the predictions on.
    mimeType: The MIME type of the content of the image. Only the images in
      below listed MIME types are supported. - image/jpeg - image/png
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaPredictInstanceTextClassificationPredictionInstance(_messages.Message):
  r"""Prediction input format for Text Classification.

  Fields:
    content: The text snippet to make the predictions on.
    mimeType: The MIME type of the text snippet. The supported MIME types are
      listed below. - text/plain
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaPredictInstanceTextExtractionPredictionInstance(_messages.Message):
  r"""Prediction input format for Text Extraction.

  Fields:
    content: The text snippet to make the predictions on.
    key: This field is only used for batch prediction. If a key is provided,
      the batch prediction result will by mapped to this key. If omitted, then
      the batch prediction result will contain the entire input instance. AI
      Platform will not check if keys in the request are duplicates, so it is
      up to the caller to ensure the keys are unique.
    mimeType: The MIME type of the text snippet. The supported MIME types are
      listed below. - text/plain
  """

  content = _messages.StringField(1)
  key = _messages.StringField(2)
  mimeType = _messages.StringField(3)


class GoogleCloudAiplatformV1SchemaPredictInstanceTextSentimentPredictionInstance(_messages.Message):
  r"""Prediction input format for Text Sentiment.

  Fields:
    content: The text snippet to make the predictions on.
    mimeType: The MIME type of the text snippet. The supported MIME types are
      listed below. - text/plain
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaPredictInstanceVideoActionRecognitionPredictionInstance(_messages.Message):
  r"""Prediction input format for Video Action Recognition.

  Fields:
    content: The Google Cloud Storage location of the video on which to
      perform the prediction.
    mimeType: The MIME type of the content of the video. Only the following
      are supported: video/mp4 video/avi video/quicktime
    timeSegmentEnd: The end, exclusive, of the video's time segment on which
      to perform the prediction. Expressed as a number of seconds as measured
      from the start of the video, with "s" appended at the end. Fractions are
      allowed, up to a microsecond precision, and "inf" or "Infinity" is
      allowed, which means the end of the video.
    timeSegmentStart: The beginning, inclusive, of the video's time segment on
      which to perform the prediction. Expressed as a number of seconds as
      measured from the start of the video, with "s" appended at the end.
      Fractions are allowed, up to a microsecond precision.
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)
  timeSegmentEnd = _messages.StringField(3)
  timeSegmentStart = _messages.StringField(4)


class GoogleCloudAiplatformV1SchemaPredictInstanceVideoClassificationPredictionInstance(_messages.Message):
  r"""Prediction input format for Video Classification.

  Fields:
    content: The Google Cloud Storage location of the video on which to
      perform the prediction.
    mimeType: The MIME type of the content of the video. Only the following
      are supported: video/mp4 video/avi video/quicktime
    timeSegmentEnd: The end, exclusive, of the video's time segment on which
      to perform the prediction. Expressed as a number of seconds as measured
      from the start of the video, with "s" appended at the end. Fractions are
      allowed, up to a microsecond precision, and "inf" or "Infinity" is
      allowed, which means the end of the video.
    timeSegmentStart: The beginning, inclusive, of the video's time segment on
      which to perform the prediction. Expressed as a number of seconds as
      measured from the start of the video, with "s" appended at the end.
      Fractions are allowed, up to a microsecond precision.
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)
  timeSegmentEnd = _messages.StringField(3)
  timeSegmentStart = _messages.StringField(4)


class GoogleCloudAiplatformV1SchemaPredictInstanceVideoObjectTrackingPredictionInstance(_messages.Message):
  r"""Prediction input format for Video Object Tracking.

  Fields:
    content: The Google Cloud Storage location of the video on which to
      perform the prediction.
    mimeType: The MIME type of the content of the video. Only the following
      are supported: video/mp4 video/avi video/quicktime
    timeSegmentEnd: The end, exclusive, of the video's time segment on which
      to perform the prediction. Expressed as a number of seconds as measured
      from the start of the video, with "s" appended at the end. Fractions are
      allowed, up to a microsecond precision, and "inf" or "Infinity" is
      allowed, which means the end of the video.
    timeSegmentStart: The beginning, inclusive, of the video's time segment on
      which to perform the prediction. Expressed as a number of seconds as
      measured from the start of the video, with "s" appended at the end.
      Fractions are allowed, up to a microsecond precision.
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)
  timeSegmentEnd = _messages.StringField(3)
  timeSegmentStart = _messages.StringField(4)


class GoogleCloudAiplatformV1SchemaPredictParamsImageClassificationPredictionParams(_messages.Message):
  r"""Prediction model parameters for Image Classification.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The Model only returns up to that many top, by confidence
      score, predictions per instance. If this number is very high, the Model
      may return fewer predictions. Default value is 10.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1SchemaPredictParamsImageObjectDetectionPredictionParams(_messages.Message):
  r"""Prediction model parameters for Image Object Detection.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The Model only returns up to that many top, by confidence
      score, predictions per instance. Note that number of returned
      predictions is also limited by metadata's predictionsLimit. Default
      value is 10.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1SchemaPredictParamsImageSegmentationPredictionParams(_messages.Message):
  r"""Prediction model parameters for Image Segmentation.

  Fields:
    confidenceThreshold: When the model predicts category of pixels of the
      image, it will only provide predictions for pixels that it is at least
      this much confident about. All other pixels will be classified as
      background. Default value is 0.5.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1SchemaPredictParamsVideoActionRecognitionPredictionParams(_messages.Message):
  r"""Prediction model parameters for Video Action Recognition.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The model only returns up to that many top, by confidence
      score, predictions per frame of the video. If this number is very high,
      the Model may return fewer predictions per frame. Default value is 50.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1SchemaPredictParamsVideoClassificationPredictionParams(_messages.Message):
  r"""Prediction model parameters for Video Classification.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The Model only returns up to that many top, by confidence
      score, predictions per instance. If this number is very high, the Model
      may return fewer predictions. Default value is 10,000.
    oneSecIntervalClassification: Set to true to request classification for a
      video at one-second intervals. AI Platform returns labels and their
      confidence scores for each second of the entire time segment of the
      video that user specified in the input WARNING: Model evaluation is not
      done for this classification type, the quality of it depends on the
      training data, but there are no metrics provided to describe that
      quality. Default value is false
    segmentClassification: Set to true to request segment-level
      classification. AI Platform returns labels and their confidence scores
      for the entire time segment of the video that user specified in the
      input instance. Default value is true
    shotClassification: Set to true to request shot-level classification. AI
      Platform determines the boundaries for each camera shot in the entire
      time segment of the video that user specified in the input instance. AI
      Platform then returns labels and their confidence scores for each
      detected shot, along with the start and end time of the shot. WARNING:
      Model evaluation is not done for this classification type, the quality
      of it depends on the training data, but there are no metrics provided to
      describe that quality. Default value is false
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  oneSecIntervalClassification = _messages.BooleanField(3)
  segmentClassification = _messages.BooleanField(4)
  shotClassification = _messages.BooleanField(5)


class GoogleCloudAiplatformV1SchemaPredictParamsVideoObjectTrackingPredictionParams(_messages.Message):
  r"""Prediction model parameters for Video Object Tracking.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The model only returns up to that many top, by confidence
      score, predictions per frame of the video. If this number is very high,
      the Model may return fewer predictions per frame. Default value is 50.
    minBoundingBoxSize: Only bounding boxes with shortest edge at least that
      long as a relative value of video frame size are returned. Default value
      is 0.0.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  minBoundingBoxSize = _messages.FloatField(3, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1SchemaPredictPredictionClassificationPredictionResult(_messages.Message):
  r"""Prediction output format for Image and Text Classification.

  Fields:
    confidences: The Model's confidences in correctness of the predicted IDs,
      higher value means higher confidence. Order matches the Ids.
    displayNames: The display names of the AnnotationSpecs that had been
      identified, order matches the IDs.
    ids: The resource IDs of the AnnotationSpecs that had been identified,
      ordered by the confidence score descendingly.
  """

  confidences = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT)
  displayNames = _messages.StringField(2, repeated=True)
  ids = _messages.IntegerField(3, repeated=True)


class GoogleCloudAiplatformV1SchemaPredictPredictionImageObjectDetectionPredictionResult(_messages.Message):
  r"""Prediction output format for Image Object Detection.

  Messages:
    BboxesValueListEntry: Single entry in a BboxesValue.

  Fields:
    bboxes: Bounding boxes, i.e. the rectangles over the image, that pinpoint
      the found AnnotationSpecs. Given in order that matches the IDs. Each
      bounding box is an array of 4 numbers `xMin`, `xMax`, `yMin`, and
      `yMax`, which represent the extremal coordinates of the box. They are
      relative to the image size, and the point 0,0 is in the top left of the
      image.
    confidences: The Model's confidences in correctness of the predicted IDs,
      higher value means higher confidence. Order matches the Ids.
    displayNames: The display names of the AnnotationSpecs that had been
      identified, order matches the IDs.
    ids: The resource IDs of the AnnotationSpecs that had been identified,
      ordered by the confidence score descendingly.
  """

  class BboxesValueListEntry(_messages.Message):
    r"""Single entry in a BboxesValue.

    Fields:
      entry: A extra_types.JsonValue attribute.
    """

    entry = _messages.MessageField('extra_types.JsonValue', 1, repeated=True)

  bboxes = _messages.MessageField('BboxesValueListEntry', 1, repeated=True)
  confidences = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT)
  displayNames = _messages.StringField(3, repeated=True)
  ids = _messages.IntegerField(4, repeated=True)


class GoogleCloudAiplatformV1SchemaPredictPredictionImageSegmentationPredictionResult(_messages.Message):
  r"""Prediction output format for Image Segmentation.

  Fields:
    categoryMask: A PNG image where each pixel in the mask represents the
      category in which the pixel in the original image was predicted to
      belong to. The size of this image will be the same as the original
      image. The mapping between the AnntoationSpec and the color can be found
      in model's metadata. The model will choose the most likely category and
      if none of the categories reach the confidence threshold, the pixel will
      be marked as background.
    confidenceMask: A one channel image which is encoded as an 8bit lossless
      PNG. The size of the image will be the same as the original image. For a
      specific pixel, darker color means less confidence in correctness of the
      cateogry in the categoryMask for the corresponding pixel. Black means no
      confidence and white means complete confidence.
  """

  categoryMask = _messages.StringField(1)
  confidenceMask = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaPredictPredictionTabularClassificationPredictionResult(_messages.Message):
  r"""Prediction output format for Tabular Classification.

  Fields:
    classes: The name of the classes being classified, contains all possible
      values of the target column.
    scores: The model's confidence in each class being correct, higher value
      means higher confidence. The N-th score corresponds to the N-th class in
      classes.
  """

  classes = _messages.StringField(1, repeated=True)
  scores = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1SchemaPredictPredictionTabularRegressionPredictionResult(_messages.Message):
  r"""Prediction output format for Tabular Regression.

  Fields:
    lowerBound: The lower bound of the prediction interval.
    upperBound: The upper bound of the prediction interval.
    value: The regression value.
  """

  lowerBound = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  upperBound = _messages.FloatField(2, variant=_messages.Variant.FLOAT)
  value = _messages.FloatField(3, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1SchemaPredictPredictionTextExtractionPredictionResult(_messages.Message):
  r"""Prediction output format for Text Extraction.

  Fields:
    confidences: The Model's confidences in correctness of the predicted IDs,
      higher value means higher confidence. Order matches the Ids.
    displayNames: The display names of the AnnotationSpecs that had been
      identified, order matches the IDs.
    ids: The resource IDs of the AnnotationSpecs that had been identified,
      ordered by the confidence score descendingly.
    textSegmentEndOffsets: The end offsets, inclusive, of the text segment in
      which the AnnotationSpec has been identified. Expressed as a zero-based
      number of characters as measured from the start of the text snippet.
    textSegmentStartOffsets: The start offsets, inclusive, of the text segment
      in which the AnnotationSpec has been identified. Expressed as a zero-
      based number of characters as measured from the start of the text
      snippet.
  """

  confidences = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT)
  displayNames = _messages.StringField(2, repeated=True)
  ids = _messages.IntegerField(3, repeated=True)
  textSegmentEndOffsets = _messages.IntegerField(4, repeated=True)
  textSegmentStartOffsets = _messages.IntegerField(5, repeated=True)


class GoogleCloudAiplatformV1SchemaPredictPredictionTextSentimentPredictionResult(_messages.Message):
  r"""Prediction output format for Text Sentiment

  Fields:
    sentiment: The integer sentiment labels between 0 (inclusive) and
      sentimentMax label (inclusive), while 0 maps to the least positive
      sentiment and sentimentMax maps to the most positive one. The higher the
      score is, the more positive the sentiment in the text snippet is. Note:
      sentimentMax is an integer value between 1 (inclusive) and 10
      (inclusive).
  """

  sentiment = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1SchemaPredictPredictionTimeSeriesForecastingPredictionResult(_messages.Message):
  r"""Prediction output format for Time Series Forecasting.

  Fields:
    value: The regression value.
  """

  value = _messages.FloatField(1, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1SchemaPredictPredictionVideoActionRecognitionPredictionResult(_messages.Message):
  r"""Prediction output format for Video Action Recognition.

  Fields:
    confidence: The Model's confidence in correction of this prediction,
      higher value means higher confidence.
    displayName: The display name of the AnnotationSpec that had been
      identified.
    id: The resource ID of the AnnotationSpec that had been identified.
    timeSegmentEnd: The end, exclusive, of the video's time segment in which
      the AnnotationSpec has been identified. Expressed as a number of seconds
      as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end.
    timeSegmentStart: The beginning, inclusive, of the video's time segment in
      which the AnnotationSpec has been identified. Expressed as a number of
      seconds as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end.
  """

  confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  displayName = _messages.StringField(2)
  id = _messages.StringField(3)
  timeSegmentEnd = _messages.StringField(4)
  timeSegmentStart = _messages.StringField(5)


class GoogleCloudAiplatformV1SchemaPredictPredictionVideoClassificationPredictionResult(_messages.Message):
  r"""Prediction output format for Video Classification.

  Fields:
    confidence: The Model's confidence in correction of this prediction,
      higher value means higher confidence.
    displayName: The display name of the AnnotationSpec that had been
      identified.
    id: The resource ID of the AnnotationSpec that had been identified.
    timeSegmentEnd: The end, exclusive, of the video's time segment in which
      the AnnotationSpec has been identified. Expressed as a number of seconds
      as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end. Note that for
      'segment-classification' prediction type, this equals the original
      'timeSegmentEnd' from the input instance, for other types it is the end
      of a shot or a 1 second interval respectively.
    timeSegmentStart: The beginning, inclusive, of the video's time segment in
      which the AnnotationSpec has been identified. Expressed as a number of
      seconds as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end. Note that for
      'segment-classification' prediction type, this equals the original
      'timeSegmentStart' from the input instance, for other types it is the
      start of a shot or a 1 second interval respectively.
    type: The type of the prediction. The requested types can be configured
      via parameters. This will be one of - segment-classification - shot-
      classification - one-sec-interval-classification
  """

  confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  displayName = _messages.StringField(2)
  id = _messages.StringField(3)
  timeSegmentEnd = _messages.StringField(4)
  timeSegmentStart = _messages.StringField(5)
  type = _messages.StringField(6)


class GoogleCloudAiplatformV1SchemaPredictPredictionVideoObjectTrackingPredictionResult(_messages.Message):
  r"""Prediction output format for Video Object Tracking.

  Fields:
    confidence: The Model's confidence in correction of this prediction,
      higher value means higher confidence.
    displayName: The display name of the AnnotationSpec that had been
      identified.
    frames: All of the frames of the video in which a single object instance
      has been detected. The bounding boxes in the frames identify the same
      object.
    id: The resource ID of the AnnotationSpec that had been identified.
    timeSegmentEnd: The end, inclusive, of the video's time segment in which
      the object instance has been detected. Expressed as a number of seconds
      as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end.
    timeSegmentStart: The beginning, inclusive, of the video's time segment in
      which the object instance has been detected. Expressed as a number of
      seconds as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end.
  """

  confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  displayName = _messages.StringField(2)
  frames = _messages.MessageField('GoogleCloudAiplatformV1SchemaPredictPredictionVideoObjectTrackingPredictionResultFrame', 3, repeated=True)
  id = _messages.StringField(4)
  timeSegmentEnd = _messages.StringField(5)
  timeSegmentStart = _messages.StringField(6)


class GoogleCloudAiplatformV1SchemaPredictPredictionVideoObjectTrackingPredictionResultFrame(_messages.Message):
  r"""The fields `xMin`, `xMax`, `yMin`, and `yMax` refer to a bounding box,
  i.e. the rectangle over the video frame pinpointing the found
  AnnotationSpec. The coordinates are relative to the frame size, and the
  point 0,0 is in the top left of the frame.

  Fields:
    timeOffset: A time (frame) of a video in which the object has been
      detected. Expressed as a number of seconds as measured from the start of
      the video, with fractions up to a microsecond precision, and with "s"
      appended at the end.
    xMax: The rightmost coordinate of the bounding box.
    xMin: The leftmost coordinate of the bounding box.
    yMax: The bottommost coordinate of the bounding box.
    yMin: The topmost coordinate of the bounding box.
  """

  timeOffset = _messages.StringField(1)
  xMax = _messages.FloatField(2, variant=_messages.Variant.FLOAT)
  xMin = _messages.FloatField(3, variant=_messages.Variant.FLOAT)
  yMax = _messages.FloatField(4, variant=_messages.Variant.FLOAT)
  yMin = _messages.FloatField(5, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1SchemaPredictionResult(_messages.Message):
  r"""Represents a line of JSONL in the batch prediction output file.

  Messages:
    InstanceValue: User's input instance. Struct is used here instead of Any
      so that JsonFormat does not append an extra "@type" field when we
      convert the proto to JSON.

  Fields:
    error: The error result. Do not set prediction if this is set.
    instance: User's input instance. Struct is used here instead of Any so
      that JsonFormat does not append an extra "@type" field when we convert
      the proto to JSON.
    key: Optional user-provided key from the input instance.
    prediction: The prediction result. Value is used here instead of Any so
      that JsonFormat does not append an extra "@type" field when we convert
      the proto to JSON and so we can represent array of objects. Do not set
      error if this is set.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class InstanceValue(_messages.Message):
    r"""User's input instance. Struct is used here instead of Any so that
    JsonFormat does not append an extra "@type" field when we convert the
    proto to JSON.

    Messages:
      AdditionalProperty: An additional property for a InstanceValue object.

    Fields:
      additionalProperties: Properties of the object.
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a InstanceValue object.

      Fields:
        key: Name of the additional property.
        value: A extra_types.JsonValue attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('extra_types.JsonValue', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  error = _messages.MessageField('GoogleCloudAiplatformV1SchemaPredictionResultError', 1)
  instance = _messages.MessageField('InstanceValue', 2)
  key = _messages.StringField(3)
  prediction = _messages.MessageField('extra_types.JsonValue', 4)


class GoogleCloudAiplatformV1SchemaPredictionResultError(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaPredictionResultError object.

  Enums:
    StatusValueValuesEnum: Error status. This will be serialized into the enum
      name e.g. "NOT_FOUND".

  Fields:
    message: Error message with additional details.
    status: Error status. This will be serialized into the enum name e.g.
      "NOT_FOUND".
  """

  class StatusValueValuesEnum(_messages.Enum):
    r"""Error status. This will be serialized into the enum name e.g.
    "NOT_FOUND".

    Values:
      OK: Not an error; returned on success HTTP Mapping: 200 OK
      CANCELLED: The operation was cancelled, typically by the caller. HTTP
        Mapping: 499 Client Closed Request
      UNKNOWN: Unknown error. For example, this error may be returned when a
        `Status` value received from another address space belongs to an error
        space that is not known in this address space. Also errors raised by
        APIs that do not return enough error information may be converted to
        this error. HTTP Mapping: 500 Internal Server Error
      INVALID_ARGUMENT: The client specified an invalid argument. Note that
        this differs from `FAILED_PRECONDITION`. `INVALID_ARGUMENT` indicates
        arguments that are problematic regardless of the state of the system
        (e.g., a malformed file name). HTTP Mapping: 400 Bad Request
      DEADLINE_EXCEEDED: The deadline expired before the operation could
        complete. For operations that change the state of the system, this
        error may be returned even if the operation has completed
        successfully. For example, a successful response from a server could
        have been delayed long enough for the deadline to expire. HTTP
        Mapping: 504 Gateway Timeout
      NOT_FOUND: Some requested entity (e.g., file or directory) was not
        found. Note to server developers: if a request is denied for an entire
        class of users, such as gradual feature rollout or undocumented
        allowlist, `NOT_FOUND` may be used. If a request is denied for some
        users within a class of users, such as user-based access control,
        `PERMISSION_DENIED` must be used. HTTP Mapping: 404 Not Found
      ALREADY_EXISTS: The entity that a client attempted to create (e.g., file
        or directory) already exists. HTTP Mapping: 409 Conflict
      PERMISSION_DENIED: The caller does not have permission to execute the
        specified operation. `PERMISSION_DENIED` must not be used for
        rejections caused by exhausting some resource (use
        `RESOURCE_EXHAUSTED` instead for those errors). `PERMISSION_DENIED`
        must not be used if the caller can not be identified (use
        `UNAUTHENTICATED` instead for those errors). This error code does not
        imply the request is valid or the requested entity exists or satisfies
        other pre-conditions. HTTP Mapping: 403 Forbidden
      UNAUTHENTICATED: The request does not have valid authentication
        credentials for the operation. HTTP Mapping: 401 Unauthorized
      RESOURCE_EXHAUSTED: Some resource has been exhausted, perhaps a per-user
        quota, or perhaps the entire file system is out of space. HTTP
        Mapping: 429 Too Many Requests
      FAILED_PRECONDITION: The operation was rejected because the system is
        not in a state required for the operation's execution. For example,
        the directory to be deleted is non-empty, an rmdir operation is
        applied to a non-directory, etc. Service implementors can use the
        following guidelines to decide between `FAILED_PRECONDITION`,
        `ABORTED`, and `UNAVAILABLE`: (a) Use `UNAVAILABLE` if the client can
        retry just the failing call. (b) Use `ABORTED` if the client should
        retry at a higher level (e.g., when a client-specified test-and-set
        fails, indicating the client should restart a read-modify-write
        sequence). (c) Use `FAILED_PRECONDITION` if the client should not
        retry until the system state has been explicitly fixed. E.g., if an
        "rmdir" fails because the directory is non-empty,
        `FAILED_PRECONDITION` should be returned since the client should not
        retry unless the files are deleted from the directory. HTTP Mapping:
        400 Bad Request
      ABORTED: The operation was aborted, typically due to a concurrency issue
        such as a sequencer check failure or transaction abort. See the
        guidelines above for deciding between `FAILED_PRECONDITION`,
        `ABORTED`, and `UNAVAILABLE`. HTTP Mapping: 409 Conflict
      OUT_OF_RANGE: The operation was attempted past the valid range. E.g.,
        seeking or reading past end-of-file. Unlike `INVALID_ARGUMENT`, this
        error indicates a problem that may be fixed if the system state
        changes. For example, a 32-bit file system will generate
        `INVALID_ARGUMENT` if asked to read at an offset that is not in the
        range [0,2^32-1], but it will generate `OUT_OF_RANGE` if asked to read
        from an offset past the current file size. There is a fair bit of
        overlap between `FAILED_PRECONDITION` and `OUT_OF_RANGE`. We recommend
        using `OUT_OF_RANGE` (the more specific error) when it applies so that
        callers who are iterating through a space can easily look for an
        `OUT_OF_RANGE` error to detect when they are done. HTTP Mapping: 400
        Bad Request
      UNIMPLEMENTED: The operation is not implemented or is not
        supported/enabled in this service. HTTP Mapping: 501 Not Implemented
      INTERNAL: Internal errors. This means that some invariants expected by
        the underlying system have been broken. This error code is reserved
        for serious errors. HTTP Mapping: 500 Internal Server Error
      UNAVAILABLE: The service is currently unavailable. This is most likely a
        transient condition, which can be corrected by retrying with a
        backoff. Note that it is not always safe to retry non-idempotent
        operations. See the guidelines above for deciding between
        `FAILED_PRECONDITION`, `ABORTED`, and `UNAVAILABLE`. HTTP Mapping: 503
        Service Unavailable
      DATA_LOSS: Unrecoverable data loss or corruption. HTTP Mapping: 500
        Internal Server Error
    """
    OK = 0
    CANCELLED = 1
    UNKNOWN = 2
    INVALID_ARGUMENT = 3
    DEADLINE_EXCEEDED = 4
    NOT_FOUND = 5
    ALREADY_EXISTS = 6
    PERMISSION_DENIED = 7
    UNAUTHENTICATED = 8
    RESOURCE_EXHAUSTED = 9
    FAILED_PRECONDITION = 10
    ABORTED = 11
    OUT_OF_RANGE = 12
    UNIMPLEMENTED = 13
    INTERNAL = 14
    UNAVAILABLE = 15
    DATA_LOSS = 16

  message = _messages.StringField(1)
  status = _messages.EnumField('StatusValueValuesEnum', 2)


class GoogleCloudAiplatformV1SchemaTablesDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain tables data.

  Fields:
    inputConfig: A
      GoogleCloudAiplatformV1SchemaTablesDatasetMetadataInputConfig attribute.
  """

  inputConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTablesDatasetMetadataInputConfig', 1)


class GoogleCloudAiplatformV1SchemaTablesDatasetMetadataBigQuerySource(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTablesDatasetMetadataBigQuerySource
  object.

  Fields:
    uri: The URI of a BigQuery table. e.g.
      bq://projectId.bqDatasetId.bqTableId
  """

  uri = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTablesDatasetMetadataGcsSource(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTablesDatasetMetadataGcsSource object.

  Fields:
    uri: Cloud Storage URI of one or more files. Only CSV files are supported.
      The first line of the CSV file is used as the header. If there are
      multiple files, the header is the first line of the lexicographically
      first file, the other files must either contain the exact same header or
      omit the header.
  """

  uri = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1SchemaTablesDatasetMetadataInputConfig(_messages.Message):
  r"""The tables Dataset's data source. The Dataset doesn't store the data
  directly, but only pointer(s) to its data.

  Fields:
    bigquerySource: A
      GoogleCloudAiplatformV1SchemaTablesDatasetMetadataBigQuerySource
      attribute.
    gcsSource: A GoogleCloudAiplatformV1SchemaTablesDatasetMetadataGcsSource
      attribute.
  """

  bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1SchemaTablesDatasetMetadataBigQuerySource', 1)
  gcsSource = _messages.MessageField('GoogleCloudAiplatformV1SchemaTablesDatasetMetadataGcsSource', 2)


class GoogleCloudAiplatformV1SchemaTextClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to text classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaTextDataItem(_messages.Message):
  r"""Payload of Text DataItem.

  Fields:
    gcsUri: Output only. Google Cloud Storage URI points to the original text
      in user's bucket. The text file is up to 10MB in size.
  """

  gcsUri = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTextDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Text DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Text DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaTextExtractionAnnotation(_messages.Message):
  r"""Annotation details specific to text extraction.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    textSegment: The segment of the text content.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  textSegment = _messages.MessageField('GoogleCloudAiplatformV1SchemaTextSegment', 3)


class GoogleCloudAiplatformV1SchemaTextSegment(_messages.Message):
  r"""The text segment inside of DataItem.

  Fields:
    content: The text content in the segment for output only.
    endOffset: Zero-based character index of the first character past the end
      of the text segment (counting character from the beginning of the text).
      The character at the end_offset is NOT included in the text segment.
    startOffset: Zero-based character index of the first character of the text
      segment (counting characters from the beginning of the text).
  """

  content = _messages.StringField(1)
  endOffset = _messages.IntegerField(2, variant=_messages.Variant.UINT64)
  startOffset = _messages.IntegerField(3, variant=_messages.Variant.UINT64)


class GoogleCloudAiplatformV1SchemaTextSentimentAnnotation(_messages.Message):
  r"""Annotation details specific to text sentiment.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    sentiment: The sentiment score for text.
    sentimentMax: The sentiment max score for text.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  sentiment = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  sentimentMax = _messages.IntegerField(4, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1SchemaTextSentimentSavedQueryMetadata(_messages.Message):
  r"""The metadata of SavedQuery contains TextSentiment Annotations.

  Fields:
    sentimentMax: The maximum sentiment of sentiment Anntoation in this
      SavedQuery.
  """

  sentimentMax = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1SchemaTimeSegment(_messages.Message):
  r"""A time period inside of a DataItem that has a time dimension (e.g.
  video).

  Fields:
    endTimeOffset: End of the time segment (exclusive), represented as the
      duration since the start of the DataItem.
    startTimeOffset: Start of the time segment (inclusive), represented as the
      duration since the start of the DataItem.
  """

  endTimeOffset = _messages.StringField(1)
  startTimeOffset = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain time series data.

  Fields:
    inputConfig: A
      GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataInputConfig
      attribute.
    timeColumn: The column name of the time column that identifies time order
      in the time series.
    timeSeriesIdentifierColumn: The column name of the time series identifier
      column that identifies the time series.
  """

  inputConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataInputConfig', 1)
  timeColumn = _messages.StringField(2)
  timeSeriesIdentifierColumn = _messages.StringField(3)


class GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataBigQuerySource(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataBigQuerySource
  object.

  Fields:
    uri: The URI of a BigQuery table.
  """

  uri = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataGcsSource(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataGcsSource
  object.

  Fields:
    uri: Cloud Storage URI of one or more files. Only CSV files are supported.
      The first line of the CSV file is used as the header. If there are
      multiple files, the header is the first line of the lexicographically
      first file, the other files must either contain the exact same header or
      omit the header.
  """

  uri = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataInputConfig(_messages.Message):
  r"""The time series Dataset's data source. The Dataset doesn't store the
  data directly, but only pointer(s) to its data.

  Fields:
    bigquerySource: A
      GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataBigQuerySource
      attribute.
    gcsSource: A
      GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataGcsSource
      attribute.
  """

  bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataBigQuerySource', 1)
  gcsSource = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSeriesDatasetMetadataGcsSource', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecasting(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Forecasting Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingMetadata', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputs
  object.

  Fields:
    exportEvaluatedDataItemsConfig: Configuration for exporting test set
      predictions to a BigQuery table. If this configuration is absent, then
      the export is not performed.
    forecastWindowEnd: The number of periods offset into the future as the end
      of the forecast window (the window of future values to predict, relative
      to the present.), where each period is one unit of granularity as
      defined by the `period` field above. Inclusive.
    forecastWindowStart: The number of periods offset into the future as the
      start of the forecast window (the window of future values to predict,
      relative to the present.), where each period is one unit of granularity
      as defined by the `period` field above. Default to 0. Inclusive.
    optimizationObjective: Objective function the model is optimizing towards.
      The training process creates a model that optimizes the value of the
      objective function over the validation set. The supported optimization
      objectives: "minimize-rmse" (default) - Minimize root-mean-squared error
      (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-
      rmsle" - Minimize root-mean-squared log error (RMSLE). "minimize-rmspe"
      - Minimize root-mean-squared percentage error (RMSPE). "minimize-wape-
      mae" - Minimize the combination of weighted absolute percentage error
      (WAPE) and mean-absolute-error (MAE). "minimize-quantile-loss" -
      Minimize the quantile loss at the quantiles defined in `quantiles`.
    pastHorizon: The number of periods offset into the past to restrict past
      sequence, where each period is one unit of granularity as defined by the
      `period`. Default value 0 means that it lets algorithm to define the
      value. Inclusive.
    period: Expected difference in time granularity between rows in the data.
      If it is not set, the period is inferred from data.
    quantiles: Quantiles to use for minimize-quantile-loss
      `optimization_objective`. Up to 5 quantiles are allowed of values
      between 0 and 1, exclusive. Required if the value of
      optimization_objective is minimize-quantile-loss. Represents the percent
      quantiles to use for that objective. Quantiles must be unique.
    staticColumns: Column names that should be used as static columns. The
      value of these columns are static per time series.
    targetColumn: The name of the column that the model is to predict.
    timeColumn: The name of the column that identifies time order in the time
      series.
    timeSeriesIdentifierColumn: The name of the column that identifies the
      time series.
    timeVariantPastAndFutureColumns: Column names that should be used as time
      variant past and future columns. This column contains information for
      the given entity (identified by the key column) that is known for the
      past and the future
    timeVariantPastOnlyColumns: Column names that should be used as time
      variant past only columns. This column contains information for the
      given entity (identified by the time_series_identifier_column) that is
      known for the past but not the future (e.g. population of a city in a
      given year, or weather on a given day).
    trainBudgetMilliNodeHours: Required. The train budget of creating this
      model, expressed in milli node hours i.e. 1,000 value in this field
      means 1 node hour. The training cost of the model will not exceed this
      budget. The final cost will be attempted to be close to the budget,
      though may end up being (even) noticeably smaller - at the backend's
      discretion. This especially may happen when further model training
      ceases to provide any improvements. If the budget is set to a value
      known to be insufficient to train a model for the given dataset, the
      training won't be attempted and will error. The train budget must be
      between 1,000 and 72,000 milli node hours, inclusive.
    transformations: Each transformation will apply transform function to
      given input column. And the result will be used for training. When
      creating transformation for BigQuery Struct column, the column should be
      flattened using "." as the delimiter.
    validationOptions: Validation options for the data validation component.
      The available options are: "fail-pipeline" - default, will validate
      against the validation and fail the pipeline if it fails. "ignore-
      validation" - ignore the results of the validation and continue
    weightColumn: Column name that should be used as the weight column. Higher
      values in this column give more importance to the row during model
      training. The column must have numeric values between 0 and 10000
      inclusively; 0 means the row is ignored for training. If weight column
      field is not set, then all rows are assumed to have equal weight of 1.
  """

  exportEvaluatedDataItemsConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig', 1)
  forecastWindowEnd = _messages.IntegerField(2)
  forecastWindowStart = _messages.IntegerField(3)
  optimizationObjective = _messages.StringField(4)
  pastHorizon = _messages.IntegerField(5)
  period = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsPeriod', 6)
  quantiles = _messages.FloatField(7, repeated=True)
  staticColumns = _messages.StringField(8, repeated=True)
  targetColumn = _messages.StringField(9)
  timeColumn = _messages.StringField(10)
  timeSeriesIdentifierColumn = _messages.StringField(11)
  timeVariantPastAndFutureColumns = _messages.StringField(12, repeated=True)
  timeVariantPastOnlyColumns = _messages.StringField(13, repeated=True)
  trainBudgetMilliNodeHours = _messages.IntegerField(14)
  transformations = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation', 15, repeated=True)
  validationOptions = _messages.StringField(16)
  weightColumn = _messages.StringField(17)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsPeriod(_messages.Message):
  r"""A duration of time expressed in time granularity units.

  Fields:
    quantity: The number of units per period, e.g. 3 weeks or 2 months.
    unit: The time granularity unit of this time period. The supported unit
      are: "minute" "hour" "day" "week" "month" "year"
  """

  quantity = _messages.IntegerField(1)
  unit = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInp
  utsTransformation object.

  Fields:
    auto: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastin
      gInputsTransformationAutoTransformation attribute.
    categorical: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlFor
      ecastingInputsTransformationCategoricalTransformation attribute.
    numeric: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecas
      tingInputsTransformationNumericTransformation attribute.
    repeatedCategorical: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionA
      utoMlForecastingInputsTransformationCategoricalArrayTransformation
      attribute.
    repeatedNumeric: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoM
      lForecastingInputsTransformationNumericArrayTransformation attribute.
    repeatedText: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlFo
      recastingInputsTransformationTextArrayTransformation attribute.
    text: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastin
      gInputsTransformationTextTransformation attribute.
    timestamp: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForec
      astingInputsTransformationTimestampTransformation attribute.
  """

  auto = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation', 1)
  categorical = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation', 2)
  numeric = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation', 3)
  repeatedCategorical = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalArrayTransformation', 4)
  repeatedNumeric = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericArrayTransformation', 5)
  repeatedText = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextArrayTransformation', 6)
  text = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation', 7)
  timestamp = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation', 8)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation(_messages.Message):
  r"""Training pipeline will infer the proper transformation based on the
  statistic of dataset.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalArrayTransformation(_messages.Message):
  r"""Treats the column as categorical array and performs following
  transformation functions. * For each element in the array, convert the
  category name to a dictionary lookup index and generate an embedding for
  each index. Combine the embedding of all elements into a single embedding
  using the mean. * Empty arrays treated as an embedding of zeroes.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  categorical string as is--no change to case, punctuation, spelling, tense,
  and so on. * Convert the category name to a dictionary lookup index and
  generate an embedding for each index. * Categories that appear less than 5
  times in the training dataset are treated as the "unknown" category. The
  "unknown" category gets its own special lookup index and resulting
  embedding.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericArrayTransformation(_messages.Message):
  r"""Treats the column as numerical array and performs following
  transformation functions. * All transformations for Numerical types applied
  to the average of the all elements. * The average of empty arrays is treated
  as zero.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  value converted to float32. * The z_score of the value. * log(value+1) when
  the value is greater than or equal to 0. Otherwise, this transformation is
  not applied and the value is considered a missing value. * z_score of
  log(value+1) when the value is greater than or equal to 0. Otherwise, this
  transformation is not applied and the value is considered a missing value. *
  A boolean value that indicates whether the value is valid.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextArrayTransformation(_messages.Message):
  r"""Treats the column as text array and performs following transformation
  functions. * Concatenate all text values in the array into a single text
  value using a space (" ") as a delimiter, and then treat the result as a
  single text value. Apply the transformations for Text columns. * Empty
  arrays treated as an empty text.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  text as is--no change to case, punctuation, spelling, tense, and so on. *
  Tokenize text to words. Convert each words to a dictionary lookup index and
  generate an embedding for each index. Combine the embedding of all elements
  into a single embedding using the mean. * Tokenization is based on unicode
  script boundaries. * Missing values get their own lookup index and resulting
  embedding. * Stop-words receive no special treatment and are not removed.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. *
  Apply the transformation functions for Numerical columns. * Determine the
  year, month, day,and weekday. Treat each value from the * timestamp as a
  Categorical column. * Invalid numerical values (for example, values that
  fall outside of a typical timestamp range, or are extreme values) receive no
  special treatment and are not removed.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
    timeFormat: The format in which that time field is expressed. The
      time_format must either be one of: * `unix-seconds` * `unix-
      milliseconds` * `unix-microseconds` * `unix-nanoseconds` (for
      respectively number of seconds, milliseconds, microseconds and
      nanoseconds since start of the Unix epoch); or be written in `strftime`
      syntax. If time_format is not set, then the default format is RFC 3339
      `date-time` format, where `time-offset` = `"Z"` (e.g.
      1985-04-12T23:20:50.52Z)
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)
  timeFormat = _messages.StringField(3)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlForecastingMetadata(_messages.Message):
  r"""Model metadata specific to AutoML Forecasting.

  Fields:
    trainCostMilliNodeHours: Output only. The actual training cost of the
      model, expressed in milli node hours, i.e. 1,000 value in this field
      means 1 node hour. Guaranteed to not exceed the train budget.
  """

  trainCostMilliNodeHours = _messages.IntegerField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassification(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Image Classification
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationMetadata', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassific
  ationInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    baseModelId: The ID of the `base` model. If it is specified, the new model
      will be trained based on the `base` model. Otherwise, the new model will
      be trained from scratch. The `base` model must be in the same Project
      and Location as the new Model to train, and have the same modelType.
    budgetMilliNodeHours: The training budget of creating this model,
      expressed in milli node hours i.e. 1,000 value in this field means 1
      node hour. The actual metadata.costMilliNodeHours will be equal or less
      than this value. If further model training ceases to provide any
      improvements, it will stop without using the full budget and the
      metadata.successfulStopReason will be `model-converged`. Note, node_hour
      = actual_hour * number_of_nodes_involved. For modelType
      `cloud`(default), the budget must be between 8,000 and 800,000 milli
      node hours, inclusive. The default value is 192,000 which represents one
      day in wall time, considering 8 nodes are used. For model types `mobile-
      tf-low-latency-1`, `mobile-tf-versatile-1`, `mobile-tf-high-accuracy-1`,
      the training budget must be between 1,000 and 100,000 milli node hours,
      inclusive. The default value is 24,000 which represents one day in wall
      time on a single node that is used.
    disableEarlyStopping: Use the entire training budget. This disables the
      early stopping feature. When false the early stopping feature is
      enabled, which means that AutoML Image Classification might stop
      training before the entire training budget has been used.
    modelType: A ModelTypeValueValuesEnum attribute.
    multiLabel: If false, a single-label (multi-class) Model will be trained
      (i.e. assuming that for each image just up to one annotation may be
      applicable). If true, a multi-label Model will be trained (i.e. assuming
      that for each image multiple annotations may be applicable).
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD: A Model best tailored to be used within Google Cloud, and which
        cannot be exported. Default.
      MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) as TensorFlow or Core ML model and used on a
        mobile or edge device afterwards. Expected to have low latency, but
        may have lower prediction quality than other mobile models.
      MOBILE_TF_VERSATILE_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) as TensorFlow or Core ML model and used on a
        mobile or edge device with afterwards.
      MOBILE_TF_HIGH_ACCURACY_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) as TensorFlow or Core ML model and used on a
        mobile or edge device afterwards. Expected to have a higher latency,
        but should also have a higher prediction quality than other mobile
        models.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD = 1
    MOBILE_TF_LOW_LATENCY_1 = 2
    MOBILE_TF_VERSATILE_1 = 3
    MOBILE_TF_HIGH_ACCURACY_1 = 4

  baseModelId = _messages.StringField(1)
  budgetMilliNodeHours = _messages.IntegerField(2)
  disableEarlyStopping = _messages.BooleanField(3)
  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 4)
  multiLabel = _messages.BooleanField(5)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassificationMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageClassific
  ationMetadata object.

  Enums:
    SuccessfulStopReasonValueValuesEnum: For successful job completions, this
      is the reason why the job has finished.

  Fields:
    costMilliNodeHours: The actual training cost of creating this model,
      expressed in milli node hours, i.e. 1,000 value in this field means 1
      node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
    successfulStopReason: For successful job completions, this is the reason
      why the job has finished.
  """

  class SuccessfulStopReasonValueValuesEnum(_messages.Enum):
    r"""For successful job completions, this is the reason why the job has
    finished.

    Values:
      SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set.
      BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached.
      MODEL_CONVERGED: Further training of the Model ceased to increase its
        quality, since it already has converged.
    """
    SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
    BUDGET_REACHED = 1
    MODEL_CONVERGED = 2

  costMilliNodeHours = _messages.IntegerField(1)
  successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetection(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Image Object Detection
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionMetadata', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDet
  ectionInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    budgetMilliNodeHours: The training budget of creating this model,
      expressed in milli node hours i.e. 1,000 value in this field means 1
      node hour. The actual metadata.costMilliNodeHours will be equal or less
      than this value. If further model training ceases to provide any
      improvements, it will stop without using the full budget and the
      metadata.successfulStopReason will be `model-converged`. Note, node_hour
      = actual_hour * number_of_nodes_involved. For modelType
      `cloud`(default), the budget must be between 20,000 and 900,000 milli
      node hours, inclusive. The default value is 216,000 which represents one
      day in wall time, considering 9 nodes are used. For model types `mobile-
      tf-low-latency-1`, `mobile-tf-versatile-1`, `mobile-tf-high-accuracy-1`
      the training budget must be between 1,000 and 100,000 milli node hours,
      inclusive. The default value is 24,000 which represents one day in wall
      time on a single node that is used.
    disableEarlyStopping: Use the entire training budget. This disables the
      early stopping feature. When false the early stopping feature is
      enabled, which means that AutoML Image Object Detection might stop
      training before the entire training budget has been used.
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD_HIGH_ACCURACY_1: A model best tailored to be used within Google
        Cloud, and which cannot be exported. Expected to have a higher
        latency, but should also have a higher prediction quality than other
        cloud models.
      CLOUD_LOW_LATENCY_1: A model best tailored to be used within Google
        Cloud, and which cannot be exported. Expected to have a low latency,
        but may have lower prediction quality than other cloud models.
      MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available
        within Google Cloud can also be exported (see
        ModelService.ExportModel) and used on a mobile or edge device with
        TensorFlow afterwards. Expected to have low latency, but may have
        lower prediction quality than other mobile models.
      MOBILE_TF_VERSATILE_1: A model that, in addition to being available
        within Google Cloud can also be exported (see
        ModelService.ExportModel) and used on a mobile or edge device with
        TensorFlow afterwards.
      MOBILE_TF_HIGH_ACCURACY_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) and used on a mobile or edge device with
        TensorFlow afterwards. Expected to have a higher latency, but should
        also have a higher prediction quality than other mobile models.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD_HIGH_ACCURACY_1 = 1
    CLOUD_LOW_LATENCY_1 = 2
    MOBILE_TF_LOW_LATENCY_1 = 3
    MOBILE_TF_VERSATILE_1 = 4
    MOBILE_TF_HIGH_ACCURACY_1 = 5

  budgetMilliNodeHours = _messages.IntegerField(1)
  disableEarlyStopping = _messages.BooleanField(2)
  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 3)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageObjectDet
  ectionMetadata object.

  Enums:
    SuccessfulStopReasonValueValuesEnum: For successful job completions, this
      is the reason why the job has finished.

  Fields:
    costMilliNodeHours: The actual training cost of creating this model,
      expressed in milli node hours, i.e. 1,000 value in this field means 1
      node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
    successfulStopReason: For successful job completions, this is the reason
      why the job has finished.
  """

  class SuccessfulStopReasonValueValuesEnum(_messages.Enum):
    r"""For successful job completions, this is the reason why the job has
    finished.

    Values:
      SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set.
      BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached.
      MODEL_CONVERGED: Further training of the Model ceased to increase its
        quality, since it already has converged.
    """
    SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
    BUDGET_REACHED = 1
    MODEL_CONVERGED = 2

  costMilliNodeHours = _messages.IntegerField(1)
  successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentation(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Image Segmentation
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentationInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentationMetadata', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentationInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentat
  ionInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    baseModelId: The ID of the `base` model. If it is specified, the new model
      will be trained based on the `base` model. Otherwise, the new model will
      be trained from scratch. The `base` model must be in the same Project
      and Location as the new Model to train, and have the same modelType.
    budgetMilliNodeHours: The training budget of creating this model,
      expressed in milli node hours i.e. 1,000 value in this field means 1
      node hour. The actual metadata.costMilliNodeHours will be equal or less
      than this value. If further model training ceases to provide any
      improvements, it will stop without using the full budget and the
      metadata.successfulStopReason will be `model-converged`. Note, node_hour
      = actual_hour * number_of_nodes_involved. Or actaul_wall_clock_hours =
      train_budget_milli_node_hours / (number_of_nodes_involved * 1000) For
      modelType `cloud-high-accuracy-1`(default), the budget must be between
      20,000 and 2,000,000 milli node hours, inclusive. The default value is
      192,000 which represents one day in wall time (1000 milli * 24 hours * 8
      nodes).
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD_HIGH_ACCURACY_1: A model to be used via prediction calls to uCAIP
        API. Expected to have a higher latency, but should also have a higher
        prediction quality than other models.
      CLOUD_LOW_ACCURACY_1: A model to be used via prediction calls to uCAIP
        API. Expected to have a lower latency but relatively lower prediction
        quality.
      MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) as TensorFlow model and used on a mobile or
        edge device afterwards. Expected to have low latency, but may have
        lower prediction quality than other mobile models.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD_HIGH_ACCURACY_1 = 1
    CLOUD_LOW_ACCURACY_1 = 2
    MOBILE_TF_LOW_LATENCY_1 = 3

  baseModelId = _messages.StringField(1)
  budgetMilliNodeHours = _messages.IntegerField(2)
  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 3)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentationMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlImageSegmentat
  ionMetadata object.

  Enums:
    SuccessfulStopReasonValueValuesEnum: For successful job completions, this
      is the reason why the job has finished.

  Fields:
    costMilliNodeHours: The actual training cost of creating this model,
      expressed in milli node hours, i.e. 1,000 value in this field means 1
      node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
    successfulStopReason: For successful job completions, this is the reason
      why the job has finished.
  """

  class SuccessfulStopReasonValueValuesEnum(_messages.Enum):
    r"""For successful job completions, this is the reason why the job has
    finished.

    Values:
      SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set.
      BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached.
      MODEL_CONVERGED: Further training of the Model ceased to increase its
        quality, since it already has converged.
    """
    SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
    BUDGET_REACHED = 1
    MODEL_CONVERGED = 2

  costMilliNodeHours = _messages.IntegerField(1)
  successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTables(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Tables Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesMetadata', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputs
  object.

  Fields:
    disableEarlyStopping: Use the entire training budget. This disables the
      early stopping feature. By default, the early stopping feature is
      enabled, which means that AutoML Tables might stop training before the
      entire training budget has been used.
    exportEvaluatedDataItemsConfig: Configuration for exporting test set
      predictions to a BigQuery table. If this configuration is absent, then
      the export is not performed.
    optimizationObjective: Objective function the model is optimizing towards.
      The training process creates a model that maximizes/minimizes the value
      of the objective function over the validation set. The supported
      optimization objectives depend on the prediction type. If the field is
      not set, a default objective function is used. classification (binary):
      "maximize-au-roc" (default) - Maximize the area under the receiver
      operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log
      loss. "maximize-au-prc" - Maximize the area under the precision-recall
      curve. "maximize-precision-at-recall" - Maximize precision for a
      specified recall value. "maximize-recall-at-precision" - Maximize recall
      for a specified precision value. classification (multi-class):
      "minimize-log-loss" (default) - Minimize log loss. regression:
      "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
      "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" -
      Minimize root-mean-squared log error (RMSLE).
    optimizationObjectivePrecisionValue: Required when optimization_objective
      is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
    optimizationObjectiveRecallValue: Required when optimization_objective is
      "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
    predictionType: The type of prediction the Model is to produce.
      "classification" - Predict one out of multiple target values is picked
      for each row. "regression" - Predict a value based on its relation to
      other values. This type is available only to columns that contain
      semantically numeric values, i.e. integers or floating point number,
      even if stored as e.g. strings.
    targetColumn: The column name of the target column that the model is to
      predict.
    trainBudgetMilliNodeHours: Required. The train budget of creating this
      model, expressed in milli node hours i.e. 1,000 value in this field
      means 1 node hour. The training cost of the model will not exceed this
      budget. The final cost will be attempted to be close to the budget,
      though may end up being (even) noticeably smaller - at the backend's
      discretion. This especially may happen when further model training
      ceases to provide any improvements. If the budget is set to a value
      known to be insufficient to train a model for the given dataset, the
      training won't be attempted and will error. The train budget must be
      between 1,000 and 72,000 milli node hours, inclusive.
    transformations: Each transformation will apply transform function to
      given input column. And the result will be used for training. When
      creating transformation for BigQuery Struct column, the column should be
      flattened using "." as the delimiter.
    weightColumnName: Column name that should be used as the weight column.
      Higher values in this column give more importance to the row during
      model training. The column must have numeric values between 0 and 10000
      inclusively; 0 means the row is ignored for training. If weight column
      field is not set, then all rows are assumed to have equal weight of 1.
  """

  disableEarlyStopping = _messages.BooleanField(1)
  exportEvaluatedDataItemsConfig = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig', 2)
  optimizationObjective = _messages.StringField(3)
  optimizationObjectivePrecisionValue = _messages.FloatField(4, variant=_messages.Variant.FLOAT)
  optimizationObjectiveRecallValue = _messages.FloatField(5, variant=_messages.Variant.FLOAT)
  predictionType = _messages.StringField(6)
  targetColumn = _messages.StringField(7)
  trainBudgetMilliNodeHours = _messages.IntegerField(8)
  transformations = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation', 9, repeated=True)
  weightColumnName = _messages.StringField(10)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTr
  ansformation object.

  Fields:
    auto: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInpu
      tsTransformationAutoTransformation attribute.
    categorical: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTab
      lesInputsTransformationCategoricalTransformation attribute.
    numeric: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesI
      nputsTransformationNumericTransformation attribute.
    repeatedCategorical: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionA
      utoMlTablesInputsTransformationCategoricalArrayTransformation attribute.
    repeatedNumeric: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoM
      lTablesInputsTransformationNumericArrayTransformation attribute.
    repeatedText: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTa
      blesInputsTransformationTextArrayTransformation attribute.
    text: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInpu
      tsTransformationTextTransformation attribute.
    timestamp: A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTable
      sInputsTransformationTimestampTransformation attribute.
  """

  auto = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationAutoTransformation', 1)
  categorical = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalTransformation', 2)
  numeric = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericTransformation', 3)
  repeatedCategorical = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalArrayTransformation', 4)
  repeatedNumeric = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericArrayTransformation', 5)
  repeatedText = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextArrayTransformation', 6)
  text = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextTransformation', 7)
  timestamp = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTimestampTransformation', 8)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationAutoTransformation(_messages.Message):
  r"""Training pipeline will infer the proper transformation based on the
  statistic of dataset.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalArrayTransformation(_messages.Message):
  r"""Treats the column as categorical array and performs following
  transformation functions. * For each element in the array, convert the
  category name to a dictionary lookup index and generate an embedding for
  each index. Combine the embedding of all elements into a single embedding
  using the mean. * Empty arrays treated as an embedding of zeroes.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  categorical string as is--no change to case, punctuation, spelling, tense,
  and so on. * Convert the category name to a dictionary lookup index and
  generate an embedding for each index. * Categories that appear less than 5
  times in the training dataset are treated as the "unknown" category. The
  "unknown" category gets its own special lookup index and resulting
  embedding.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericArrayTransformation(_messages.Message):
  r"""Treats the column as numerical array and performs following
  transformation functions. * All transformations for Numerical types applied
  to the average of the all elements. * The average of empty arrays is treated
  as zero.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  value converted to float32. * The z_score of the value. * log(value+1) when
  the value is greater than or equal to 0. Otherwise, this transformation is
  not applied and the value is considered a missing value. * z_score of
  log(value+1) when the value is greater than or equal to 0. Otherwise, this
  transformation is not applied and the value is considered a missing value. *
  A boolean value that indicates whether the value is valid.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextArrayTransformation(_messages.Message):
  r"""Treats the column as text array and performs following transformation
  functions. * Concatenate all text values in the array into a single text
  value using a space (" ") as a delimiter, and then treat the result as a
  single text value. Apply the transformations for Text columns. * Empty
  arrays treated as an empty text.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  text as is--no change to case, punctuation, spelling, tense, and so on. *
  Tokenize text to words. Convert each words to a dictionary lookup index and
  generate an embedding for each index. Combine the embedding of all elements
  into a single embedding using the mean. * Tokenization is based on unicode
  script boundaries. * Missing values get their own lookup index and resulting
  embedding. * Stop-words receive no special treatment and are not removed.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTimestampTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. *
  Apply the transformation functions for Numerical columns. * Determine the
  year, month, day,and weekday. Treat each value from the * timestamp as a
  Categorical column. * Invalid numerical values (for example, values that
  fall outside of a typical timestamp range, or are extreme values) receive no
  special treatment and are not removed.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
    timeFormat: The format in which that time field is expressed. The
      time_format must either be one of: * `unix-seconds` * `unix-
      milliseconds` * `unix-microseconds` * `unix-nanoseconds` (for
      respectively number of seconds, milliseconds, microseconds and
      nanoseconds since start of the Unix epoch); or be written in `strftime`
      syntax. If time_format is not set, then the default format is RFC 3339
      `date-time` format, where `time-offset` = `"Z"` (e.g.
      1985-04-12T23:20:50.52Z)
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)
  timeFormat = _messages.StringField(3)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTablesMetadata(_messages.Message):
  r"""Model metadata specific to AutoML Tables.

  Fields:
    trainCostMilliNodeHours: Output only. The actual training cost of the
      model, expressed in milli node hours, i.e. 1,000 value in this field
      means 1 node hour. Guaranteed to not exceed the train budget.
  """

  trainCostMilliNodeHours = _messages.IntegerField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextClassification(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Text Classification
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextClassificationInputs', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextClassificationInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextClassifica
  tionInputs object.

  Fields:
    multiLabel: A boolean attribute.
  """

  multiLabel = _messages.BooleanField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextExtraction(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Text Extraction Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs
  object.
  """



class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextSentiment(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Text Sentiment Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs
  object.

  Fields:
    sentimentMax: A sentiment is expressed as an integer ordinal, where higher
      value means a more positive sentiment. The range of sentiments that will
      be used is between 0 and sentimentMax (inclusive on both ends), and all
      the values in the range must be represented in the dataset before a
      model can be created. Only the Annotations with this sentimentMax will
      be used for training. sentimentMax value must be between 1 and 10
      (inclusive).
  """

  sentimentMax = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoActionRecognition(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Video Action Recognition
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoActionRecognitionInputs', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoActionRecognitionInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoActionRec
  ognitionInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD: A model best tailored to be used within Google Cloud, and which c
        annot be exported. Default.
      MOBILE_VERSATILE_1: A model that, in addition to being available within
        Google Cloud, can also be exported (see ModelService.ExportModel) as a
        TensorFlow or TensorFlow Lite model and used on a mobile or edge
        device afterwards.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD = 1
    MOBILE_VERSATILE_1 = 2

  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoClassification(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Video Classification
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoClassificationInputs', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoClassificationInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoClassific
  ationInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD: A model best tailored to be used within Google Cloud, and which
        cannot be exported. Default.
      MOBILE_VERSATILE_1: A model that, in addition to being available within
        Google Cloud, can also be exported (see ModelService.ExportModel) as a
        TensorFlow or TensorFlow Lite model and used on a mobile or edge
        device afterwards.
      MOBILE_JETSON_VERSATILE_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) to a Jetson device afterwards.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD = 1
    MOBILE_VERSATILE_1 = 2
    MOBILE_JETSON_VERSATILE_1 = 3

  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoObjectTracking(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Video ObjectTracking
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoObjectTrackingInputs', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoObjectTrackingInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionAutoMlVideoObjectTra
  ckingInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD: A model best tailored to be used within Google Cloud, and which c
        annot be exported. Default.
      MOBILE_VERSATILE_1: A model that, in addition to being available within
        Google Cloud, can also be exported (see ModelService.ExportModel) as a
        TensorFlow or TensorFlow Lite model and used on a mobile or edge
        device afterwards.
      MOBILE_CORAL_VERSATILE_1: A versatile model that is meant to be exported
        (see ModelService.ExportModel) and used on a Google Coral device.
      MOBILE_CORAL_LOW_LATENCY_1: A model that trades off quality for low
        latency, to be exported (see ModelService.ExportModel) and used on a
        Google Coral device.
      MOBILE_JETSON_VERSATILE_1: A versatile model that is meant to be
        exported (see ModelService.ExportModel) and used on an NVIDIA Jetson
        device.
      MOBILE_JETSON_LOW_LATENCY_1: A model that trades off quality for low
        latency, to be exported (see ModelService.ExportModel) and used on an
        NVIDIA Jetson device.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD = 1
    MOBILE_VERSATILE_1 = 2
    MOBILE_CORAL_VERSATILE_1 = 3
    MOBILE_CORAL_LOW_LATENCY_1 = 4
    MOBILE_JETSON_VERSATILE_1 = 5
    MOBILE_JETSON_LOW_LATENCY_1 = 6

  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionCustomJobMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionCustomJobMetadata
  object.

  Fields:
    backingCustomJob: The resource name of the CustomJob that has been created
      to carry out this custom task.
  """

  backingCustomJob = _messages.StringField(1)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionCustomTask(_messages.Message):
  r"""A TrainingJob that trains a custom code Model.

  Fields:
    inputs: The input parameters of this CustomTask.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionCustomJobMetadata', 2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig(_messages.Message):
  r"""Configuration for exporting test set predictions to a BigQuery table.

  Fields:
    destinationBigqueryUri: URI of desired destination BigQuery table.
      Expected format: bq://:: If not specified, then results are exported to
      the following auto-created BigQuery table:
      :export_evaluated_examples__.evaluated_examples
    overrideExistingTable: If true and an export destination is specified,
      then the contents of the destination are overwritten. Otherwise, if the
      export destination already exists, then the export operation fails.
  """

  destinationBigqueryUri = _messages.StringField(1)
  overrideExistingTable = _messages.BooleanField(2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningJobMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuning
  JobMetadata object.

  Fields:
    backingHyperparameterTuningJob: The resource name of the
      HyperparameterTuningJob that has been created to carry out this
      HyperparameterTuning task.
    bestTrialBackingCustomJob: The resource name of the CustomJob that has
      been created to run the best Trial of this HyperparameterTuning task.
  """

  backingHyperparameterTuningJob = _messages.StringField(1)
  bestTrialBackingCustomJob = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningJobSpec(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuning
  JobSpec object.

  Fields:
    maxFailedTrialCount: The number of failed Trials that need to be seen
      before failing the HyperparameterTuningJob. If set to 0, AI Platform
      decides how many Trials must fail before the whole job fails.
    maxTrialCount: The desired total number of Trials.
    parallelTrialCount: The desired number of Trials to run in parallel.
    studySpec: Study configuration of the HyperparameterTuningJob.
    trialJobSpec: The spec of a trial job. The same spec applies to the
      CustomJobs created in all the trials.
  """

  maxFailedTrialCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  maxTrialCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  parallelTrialCount = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  studySpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpec', 4)
  trialJobSpec = _messages.MessageField('GoogleCloudAiplatformV1CustomJobSpec', 5)


class GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningTask(_messages.Message):
  r"""A TrainingJob that tunes Hypererparameters of a custom code Model.

  Fields:
    inputs: The input parameters of this HyperparameterTuningTask.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningJobSpec', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1SchemaTrainingjobDefinitionHyperparameterTuningJobMetadata', 2)


class GoogleCloudAiplatformV1SchemaVertex(_messages.Message):
  r"""A vertex represents a 2D point in the image. NOTE: the normalized vertex
  coordinates are relative to the original image and range from 0 to 1.

  Fields:
    x: X coordinate.
    y: Y coordinate.
  """

  x = _messages.FloatField(1)
  y = _messages.FloatField(2)


class GoogleCloudAiplatformV1SchemaVideoActionRecognitionAnnotation(_messages.Message):
  r"""Annotation details specific to video action recognition.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    timeSegment: This Annotation applies to the time period represented by the
      TimeSegment. If it's not set, the Annotation applies to the whole video.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  timeSegment = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSegment', 3)


class GoogleCloudAiplatformV1SchemaVideoClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to video classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    timeSegment: This Annotation applies to the time period represented by the
      TimeSegment. If it's not set, the Annotation applies to the whole video.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  timeSegment = _messages.MessageField('GoogleCloudAiplatformV1SchemaTimeSegment', 3)


class GoogleCloudAiplatformV1SchemaVideoDataItem(_messages.Message):
  r"""Payload of Video DataItem.

  Fields:
    gcsUri: Required. Google Cloud Storage URI points to the original video in
      user's bucket. The video is up to 50 GB in size and up to 3 hour in
      duration.
    mimeType: Output only. The mime type of the content of the video. Only the
      videos in below listed mime types are supported. Supported mime_type: -
      video/mp4 - video/avi - video/quicktime
  """

  gcsUri = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaVideoDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Video DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Video DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1SchemaVideoObjectTrackingAnnotation(_messages.Message):
  r"""Annotation details specific to video object tracking.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    instanceId: The instance of the object, expressed as a positive integer.
      Used to track the same object across different frames.
    timeOffset: A time (frame) of a video to which this annotation pertains.
      Represented as the duration since the video's start.
    xMax: The rightmost coordinate of the bounding box.
    xMin: The leftmost coordinate of the bounding box.
    yMax: The bottommost coordinate of the bounding box.
    yMin: The topmost coordinate of the bounding box.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  instanceId = _messages.IntegerField(3)
  timeOffset = _messages.StringField(4)
  xMax = _messages.FloatField(5)
  xMin = _messages.FloatField(6)
  yMax = _messages.FloatField(7)
  yMin = _messages.FloatField(8)


class GoogleCloudAiplatformV1SchemaVisualInspectionClassificationLabelSavedQueryMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaVisualInspectionClassificationLabelSavedQ
  ueryMetadata object.

  Fields:
    multiLabel: Whether or not the classification label is multi_label.
  """

  multiLabel = _messages.BooleanField(1)


class GoogleCloudAiplatformV1SchemaVisualInspectionMaskSavedQueryMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1SchemaVisualInspectionMaskSavedQueryMetadata
  object.
  """



class GoogleCloudAiplatformV1SearchMigratableResourcesRequest(_messages.Message):
  r"""Request message for MigrationService.SearchMigratableResources.

  Fields:
    filter: Supported filters are: * Resource type: For a specific type of
      MigratableResource. * `ml_engine_model_version:*` * `automl_model:*`, *
      `automl_dataset:*` * `data_labeling_dataset:*`. * Migrated or not:
      Filter migrated resource or not by last_migrate_time. *
      `last_migrate_time:*` will filter migrated resources. * `NOT
      last_migrate_time:*` will filter not yet migrated resources.
    pageSize: The standard page size. The default and maximum value is 100.
    pageToken: The standard page token.
  """

  filter = _messages.StringField(1)
  pageSize = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  pageToken = _messages.StringField(3)


class GoogleCloudAiplatformV1SearchMigratableResourcesResponse(_messages.Message):
  r"""Response message for MigrationService.SearchMigratableResources.

  Fields:
    migratableResources: All migratable resources that can be migrated to the
      location specified in the request.
    nextPageToken: The standard next-page token. The migratable_resources may
      not fill page_size in SearchMigratableResourcesRequest even when there
      are subsequent pages.
  """

  migratableResources = _messages.MessageField('GoogleCloudAiplatformV1MigratableResource', 1, repeated=True)
  nextPageToken = _messages.StringField(2)


class GoogleCloudAiplatformV1SpecialistPool(_messages.Message):
  r"""SpecialistPool represents customers' own workforce to work on their data
  labeling jobs. It includes a group of specialist managers who are
  responsible for managing the labelers in this pool as well as customers'
  data labeling jobs associated with this pool. Customers create specialist
  pool as well as start data labeling jobs on Cloud, managers and labelers
  work with the jobs using CrowdCompute console.

  Fields:
    displayName: Required. The user-defined name of the SpecialistPool. The
      name can be up to 128 characters long and can be consist of any UTF-8
      characters. This field should be unique on project-level.
    name: Required. The resource name of the SpecialistPool.
    pendingDataLabelingJobs: Output only. The resource name of the pending
      data labeling jobs.
    specialistManagerEmails: The email addresses of the specialists in the
      SpecialistPool.
    specialistManagersCount: Output only. The number of Specialists in this
      SpecialistPool.
  """

  displayName = _messages.StringField(1)
  name = _messages.StringField(2)
  pendingDataLabelingJobs = _messages.StringField(3, repeated=True)
  specialistManagerEmails = _messages.StringField(4, repeated=True)
  specialistManagersCount = _messages.IntegerField(5, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1StudySpec(_messages.Message):
  r"""Represents specification of a Study.

  Enums:
    AlgorithmValueValuesEnum: The search algorithm specified for the Study.
    MeasurementSelectionTypeValueValuesEnum: Describe which measurement
      selection type will be used
    ObservationNoiseValueValuesEnum: The observation noise level of the study.
      Currently only supported by the Vizier service. Not supported by
      HyperparamterTuningJob or TrainingPipeline.

  Fields:
    algorithm: The search algorithm specified for the Study.
    measurementSelectionType: Describe which measurement selection type will
      be used
    metrics: Required. Metric specs for the Study.
    observationNoise: The observation noise level of the study. Currently only
      supported by the Vizier service. Not supported by HyperparamterTuningJob
      or TrainingPipeline.
    parameters: Required. The set of parameters to tune.
  """

  class AlgorithmValueValuesEnum(_messages.Enum):
    r"""The search algorithm specified for the Study.

    Values:
      ALGORITHM_UNSPECIFIED: The default algorithm used by AI Platform
        Optimization service.
      GRID_SEARCH: Simple grid search within the feasible space. To use grid
        search, all parameters must be `INTEGER`, `CATEGORICAL`, or
        `DISCRETE`.
      RANDOM_SEARCH: Simple random search within the feasible space.
    """
    ALGORITHM_UNSPECIFIED = 0
    GRID_SEARCH = 1
    RANDOM_SEARCH = 2

  class MeasurementSelectionTypeValueValuesEnum(_messages.Enum):
    r"""Describe which measurement selection type will be used

    Values:
      MEASUREMENT_SELECTION_TYPE_UNSPECIFIED: Will be treated as
        LAST_MEASUREMENT.
      LAST_MEASUREMENT: Use the last measurement reported.
      BEST_MEASUREMENT: Use the best measurement reported.
    """
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIED = 0
    LAST_MEASUREMENT = 1
    BEST_MEASUREMENT = 2

  class ObservationNoiseValueValuesEnum(_messages.Enum):
    r"""The observation noise level of the study. Currently only supported by
    the Vizier service. Not supported by HyperparamterTuningJob or
    TrainingPipeline.

    Values:
      OBSERVATION_NOISE_UNSPECIFIED: The default noise level chosen by the AI
        Platform service.
      LOW: AI Platform Vizier assumes that the objective function is (nearly)
        perfectly reproducible, and will never repeat the same Trial
        parameters.
      HIGH: AI Platform Vizier will estimate the amount of noise in metric
        evaluations, it may repeat the same Trial parameters more than once.
    """
    OBSERVATION_NOISE_UNSPECIFIED = 0
    LOW = 1
    HIGH = 2

  algorithm = _messages.EnumField('AlgorithmValueValuesEnum', 1)
  measurementSelectionType = _messages.EnumField('MeasurementSelectionTypeValueValuesEnum', 2)
  metrics = _messages.MessageField('GoogleCloudAiplatformV1StudySpecMetricSpec', 3, repeated=True)
  observationNoise = _messages.EnumField('ObservationNoiseValueValuesEnum', 4)
  parameters = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpec', 5, repeated=True)


class GoogleCloudAiplatformV1StudySpecMetricSpec(_messages.Message):
  r"""Represents a metric to optimize.

  Enums:
    GoalValueValuesEnum: Required. The optimization goal of the metric.

  Fields:
    goal: Required. The optimization goal of the metric.
    metricId: Required. The ID of the metric. Must not contain whitespaces and
      must be unique amongst all MetricSpecs.
  """

  class GoalValueValuesEnum(_messages.Enum):
    r"""Required. The optimization goal of the metric.

    Values:
      GOAL_TYPE_UNSPECIFIED: Goal Type will default to maximize.
      MAXIMIZE: Maximize the goal metric.
      MINIMIZE: Minimize the goal metric.
    """
    GOAL_TYPE_UNSPECIFIED = 0
    MAXIMIZE = 1
    MINIMIZE = 2

  goal = _messages.EnumField('GoalValueValuesEnum', 1)
  metricId = _messages.StringField(2)


class GoogleCloudAiplatformV1StudySpecParameterSpec(_messages.Message):
  r"""Represents a single parameter to optimize.

  Enums:
    ScaleTypeValueValuesEnum: How the parameter should be scaled. Leave unset
      for `CATEGORICAL` parameters.

  Fields:
    categoricalValueSpec: The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs: A conditional parameter node is active if the
      parameter's value matches the conditional node's parent_value_condition.
      If two items in conditional_parameter_specs have the same name, they
      must have disjoint parent_value_condition.
    discreteValueSpec: The value spec for a 'DISCRETE' parameter.
    doubleValueSpec: The value spec for a 'DOUBLE' parameter.
    integerValueSpec: The value spec for an 'INTEGER' parameter.
    parameterId: Required. The ID of the parameter. Must not contain
      whitespaces and must be unique amongst all ParameterSpecs.
    scaleType: How the parameter should be scaled. Leave unset for
      `CATEGORICAL` parameters.
  """

  class ScaleTypeValueValuesEnum(_messages.Enum):
    r"""How the parameter should be scaled. Leave unset for `CATEGORICAL`
    parameters.

    Values:
      SCALE_TYPE_UNSPECIFIED: By default, no scaling is applied.
      UNIT_LINEAR_SCALE: Scales the feasible space to (0, 1) linearly.
      UNIT_LOG_SCALE: Scales the feasible space logarithmically to (0, 1). The
        entire feasible space must be strictly positive.
      UNIT_REVERSE_LOG_SCALE: Scales the feasible space "reverse"
        logarithmically to (0, 1). The result is that values close to the top
        of the feasible space are spread out more than points near the bottom.
        The entire feasible space must be strictly positive.
    """
    SCALE_TYPE_UNSPECIFIED = 0
    UNIT_LINEAR_SCALE = 1
    UNIT_LOG_SCALE = 2
    UNIT_REVERSE_LOG_SCALE = 3

  categoricalValueSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpec', 1)
  conditionalParameterSpecs = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpec', 2, repeated=True)
  discreteValueSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpec', 3)
  doubleValueSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpec', 4)
  integerValueSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpec', 5)
  parameterId = _messages.StringField(6)
  scaleType = _messages.EnumField('ScaleTypeValueValuesEnum', 7)


class GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpec(_messages.Message):
  r"""Value specification for a parameter in `CATEGORICAL` type.

  Fields:
    values: Required. The list of possible categories.
  """

  values = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpec(_messages.Message):
  r"""Represents a parameter spec with condition from its parent parameter.

  Fields:
    parameterSpec: Required. The spec for a conditional parameter.
    parentCategoricalValues: The spec for matching values from a parent
      parameter of `CATEGORICAL` type.
    parentDiscreteValues: The spec for matching values from a parent parameter
      of `DISCRETE` type.
    parentIntValues: The spec for matching values from a parent parameter of
      `INTEGER` type.
  """

  parameterSpec = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpec', 1)
  parentCategoricalValues = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition', 2)
  parentDiscreteValues = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition', 3)
  parentIntValues = _messages.MessageField('GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueCondition', 4)


class GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition(_messages.Message):
  r"""Represents the spec to match categorical values from parent parameter.

  Fields:
    values: Required. Matches values of the parent parameter of 'CATEGORICAL'
      type. All values must exist in `categorical_value_spec` of parent
      parameter.
  """

  values = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition(_messages.Message):
  r"""Represents the spec to match discrete values from parent parameter.

  Fields:
    values: Required. Matches values of the parent parameter of 'DISCRETE'
      type. All values must exist in `discrete_value_spec` of parent
      parameter. The Epsilon of the value matching is 1e-10.
  """

  values = _messages.FloatField(1, repeated=True)


class GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueCondition(_messages.Message):
  r"""Represents the spec to match integer values from parent parameter.

  Fields:
    values: Required. Matches values of the parent parameter of 'INTEGER'
      type. All values must lie in `integer_value_spec` of parent parameter.
  """

  values = _messages.IntegerField(1, repeated=True)


class GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpec(_messages.Message):
  r"""Value specification for a parameter in `DISCRETE` type.

  Fields:
    values: Required. A list of possible values. The list should be in
      increasing order and at least 1e-10 apart. For instance, this parameter
      might have possible settings of 1.5, 2.5, and 4.0. This list should not
      contain more than 1,000 values.
  """

  values = _messages.FloatField(1, repeated=True)


class GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpec(_messages.Message):
  r"""Value specification for a parameter in `DOUBLE` type.

  Fields:
    maxValue: Required. Inclusive maximum value of the parameter.
    minValue: Required. Inclusive minimum value of the parameter.
  """

  maxValue = _messages.FloatField(1)
  minValue = _messages.FloatField(2)


class GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpec(_messages.Message):
  r"""Value specification for a parameter in `INTEGER` type.

  Fields:
    maxValue: Required. Inclusive maximum value of the parameter.
    minValue: Required. Inclusive minimum value of the parameter.
  """

  maxValue = _messages.IntegerField(1)
  minValue = _messages.IntegerField(2)


class GoogleCloudAiplatformV1TimestampSplit(_messages.Message):
  r"""Assigns input data to training, validation, and test sets based on a
  provided timestamps. The youngest data pieces are assigned to training set,
  next to validation set, and the oldest to the test set. Supported only for
  tabular Datasets.

  Fields:
    key: Required. The key is a name of one of the Dataset's data columns. The
      values of the key (the values in the column) must be in RFC 3339 `date-
      time` format, where `time-offset` = `"Z"` (e.g.
      1985-04-12T23:20:50.52Z). If for a piece of data the key is not present
      or has an invalid value, that piece is ignored by the pipeline.
    testFraction: The fraction of the input data that is to be used to
      evaluate the Model.
    trainingFraction: The fraction of the input data that is to be used to
      train the Model.
    validationFraction: The fraction of the input data that is to be used to
      validate the Model.
  """

  key = _messages.StringField(1)
  testFraction = _messages.FloatField(2)
  trainingFraction = _messages.FloatField(3)
  validationFraction = _messages.FloatField(4)


class GoogleCloudAiplatformV1TrainingConfig(_messages.Message):
  r"""CMLE training config. For every active learning labeling iteration,
  system will train a machine learning model on CMLE. The trained model will
  be used by data sampling algorithm to select DataItems.

  Fields:
    timeoutTrainingMilliHours: The timeout hours for the CMLE training job,
      expressed in milli hours i.e. 1,000 value in this field means 1 hour.
  """

  timeoutTrainingMilliHours = _messages.IntegerField(1)


class GoogleCloudAiplatformV1TrainingPipeline(_messages.Message):
  r"""The TrainingPipeline orchestrates tasks associated with training a
  Model. It always executes the training task, and optionally may also export
  data from AI Platform's Dataset which becomes the training input, upload the
  Model to AI Platform, and evaluate the Model.

  Enums:
    StateValueValuesEnum: Output only. The detailed state of the pipeline.

  Messages:
    LabelsValue: The labels with user-defined metadata to organize
      TrainingPipelines. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels.

  Fields:
    createTime: Output only. Time when the TrainingPipeline was created.
    displayName: Required. The user-defined name of this TrainingPipeline.
    encryptionSpec: Customer-managed encryption key spec for a
      TrainingPipeline. If set, this TrainingPipeline will be secured by this
      key. Note: Model trained by this TrainingPipeline is also secured by
      this key if model_to_upload is not set separately.
    endTime: Output only. Time when the TrainingPipeline entered any of the
      following states: `PIPELINE_STATE_SUCCEEDED`, `PIPELINE_STATE_FAILED`,
      `PIPELINE_STATE_CANCELLED`.
    error: Output only. Only populated when the pipeline's state is
      `PIPELINE_STATE_FAILED` or `PIPELINE_STATE_CANCELLED`.
    inputDataConfig: Specifies AI Platform owned input data that may be used
      for training the Model. The TrainingPipeline's training_task_definition
      should make clear whether this config is used and if there are any
      special requirements on how it should be filled. If nothing about this
      config is mentioned in the training_task_definition, then it should be
      assumed that the TrainingPipeline does not depend on this configuration.
    labels: The labels with user-defined metadata to organize
      TrainingPipelines. Label keys and values can be no longer than 64
      characters (Unicode codepoints), can only contain lowercase letters,
      numeric characters, underscores and dashes. International characters are
      allowed. See https://goo.gl/xmQnxf for more information and examples of
      labels.
    modelToUpload: Describes the Model that may be uploaded (via
      ModelService.UploadModel) by this TrainingPipeline. The
      TrainingPipeline's training_task_definition should make clear whether
      this Model description should be populated, and if there are any special
      requirements regarding how it should be filled. If nothing is mentioned
      in the training_task_definition, then it should be assumed that this
      field should not be filled and the training task either uploads the
      Model without a need of this information, or that training task does not
      support uploading a Model as part of the pipeline. When the Pipeline's
      state becomes `PIPELINE_STATE_SUCCEEDED` and the trained Model had been
      uploaded into AI Platform, then the model_to_upload's resource name is
      populated. The Model is always uploaded into the Project and Location in
      which this pipeline is.
    name: Output only. Resource name of the TrainingPipeline.
    startTime: Output only. Time when the TrainingPipeline for the first time
      entered the `PIPELINE_STATE_RUNNING` state.
    state: Output only. The detailed state of the pipeline.
    trainingTaskDefinition: Required. A Google Cloud Storage path to the YAML
      file that defines the training task which is responsible for producing
      the model artifact, and may also include additional auxiliary work. The
      definition files that can be used here are found in gs://google-cloud-
      aiplatform/schema/trainingjob/definition/. Note: The URI given on output
      will be immutable and probably different, including the URI scheme, than
      the one given on input. The output URI will point to a location where
      the user only has a read access.
    trainingTaskInputs: Required. The training task's parameter(s), as
      specified in the training_task_definition's `inputs`.
    trainingTaskMetadata: Output only. The metadata information as specified
      in the training_task_definition's `metadata`. This metadata is an
      auxiliary runtime and final information about the training task. While
      the pipeline is running this information is populated only at a best
      effort basis. Only present if the pipeline's training_task_definition
      contains `metadata` object.
    updateTime: Output only. Time when the TrainingPipeline was most recently
      updated.
  """

  class StateValueValuesEnum(_messages.Enum):
    r"""Output only. The detailed state of the pipeline.

    Values:
      PIPELINE_STATE_UNSPECIFIED: The pipeline state is unspecified.
      PIPELINE_STATE_QUEUED: The pipeline has been just created or resumed and
        processing has not yet begun.
      PIPELINE_STATE_PENDING: The service is preparing to run the pipeline.
      PIPELINE_STATE_RUNNING: The pipeline is in progress.
      PIPELINE_STATE_SUCCEEDED: The pipeline completed successfully.
      PIPELINE_STATE_FAILED: The pipeline failed.
      PIPELINE_STATE_CANCELLING: The pipeline is being cancelled. From this
        state the pipeline may only go to either PIPELINE_STATE_SUCCEEDED,
        PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
      PIPELINE_STATE_CANCELLED: The pipeline has been cancelled.
      PIPELINE_STATE_PAUSED: The pipeline has been stopped, and can be
        resumed.
    """
    PIPELINE_STATE_UNSPECIFIED = 0
    PIPELINE_STATE_QUEUED = 1
    PIPELINE_STATE_PENDING = 2
    PIPELINE_STATE_RUNNING = 3
    PIPELINE_STATE_SUCCEEDED = 4
    PIPELINE_STATE_FAILED = 5
    PIPELINE_STATE_CANCELLING = 6
    PIPELINE_STATE_CANCELLED = 7
    PIPELINE_STATE_PAUSED = 8

  @encoding.MapUnrecognizedFields('additionalProperties')
  class LabelsValue(_messages.Message):
    r"""The labels with user-defined metadata to organize TrainingPipelines.
    Label keys and values can be no longer than 64 characters (Unicode
    codepoints), can only contain lowercase letters, numeric characters,
    underscores and dashes. International characters are allowed. See
    https://goo.gl/xmQnxf for more information and examples of labels.

    Messages:
      AdditionalProperty: An additional property for a LabelsValue object.

    Fields:
      additionalProperties: Additional properties of type LabelsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a LabelsValue object.

      Fields:
        key: Name of the additional property.
        value: A string attribute.
      """

      key = _messages.StringField(1)
      value = _messages.StringField(2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  createTime = _messages.StringField(1)
  displayName = _messages.StringField(2)
  encryptionSpec = _messages.MessageField('GoogleCloudAiplatformV1EncryptionSpec', 3)
  endTime = _messages.StringField(4)
  error = _messages.MessageField('GoogleRpcStatus', 5)
  inputDataConfig = _messages.MessageField('GoogleCloudAiplatformV1InputDataConfig', 6)
  labels = _messages.MessageField('LabelsValue', 7)
  modelToUpload = _messages.MessageField('GoogleCloudAiplatformV1Model', 8)
  name = _messages.StringField(9)
  startTime = _messages.StringField(10)
  state = _messages.EnumField('StateValueValuesEnum', 11)
  trainingTaskDefinition = _messages.StringField(12)
  trainingTaskInputs = _messages.MessageField('extra_types.JsonValue', 13)
  trainingTaskMetadata = _messages.MessageField('extra_types.JsonValue', 14)
  updateTime = _messages.StringField(15)


class GoogleCloudAiplatformV1Trial(_messages.Message):
  r"""A message representing a Trial. A Trial contains a unique set of
  Parameters that has been or will be evaluated, along with the objective
  metrics got by running the Trial.

  Enums:
    StateValueValuesEnum: Output only. The detailed state of the Trial.

  Fields:
    customJob: Output only. The CustomJob name linked to the Trial. It's set
      for a HyperparameterTuningJob's Trial.
    endTime: Output only. Time when the Trial's status changed to `SUCCEEDED`
      or `INFEASIBLE`.
    finalMeasurement: Output only. The final measurement containing the
      objective value.
    id: Output only. The identifier of the Trial assigned by the service.
    name: Output only. Resource name of the Trial assigned by the service.
    parameters: Output only. The parameters of the Trial.
    startTime: Output only. Time when the Trial was started.
    state: Output only. The detailed state of the Trial.
  """

  class StateValueValuesEnum(_messages.Enum):
    r"""Output only. The detailed state of the Trial.

    Values:
      STATE_UNSPECIFIED: The Trial state is unspecified.
      REQUESTED: Indicates that a specific Trial has been requested, but it
        has not yet been suggested by the service.
      ACTIVE: Indicates that the Trial has been suggested.
      STOPPING: Indicates that the Trial should stop according to the service.
      SUCCEEDED: Indicates that the Trial is completed successfully.
      INFEASIBLE: Indicates that the Trial should not be attempted again. The
        service will set a Trial to INFEASIBLE when it's done but missing the
        final_measurement.
    """
    STATE_UNSPECIFIED = 0
    REQUESTED = 1
    ACTIVE = 2
    STOPPING = 3
    SUCCEEDED = 4
    INFEASIBLE = 5

  customJob = _messages.StringField(1)
  endTime = _messages.StringField(2)
  finalMeasurement = _messages.MessageField('GoogleCloudAiplatformV1Measurement', 3)
  id = _messages.StringField(4)
  name = _messages.StringField(5)
  parameters = _messages.MessageField('GoogleCloudAiplatformV1TrialParameter', 6, repeated=True)
  startTime = _messages.StringField(7)
  state = _messages.EnumField('StateValueValuesEnum', 8)


class GoogleCloudAiplatformV1TrialParameter(_messages.Message):
  r"""A message representing a parameter to be tuned.

  Fields:
    parameterId: Output only. The ID of the parameter. The parameter should be
      defined in StudySpec's Parameters.
    value: Output only. The value of the parameter. `number_value` will be set
      if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or
      'DISCRETE'. `string_value` will be set if a parameter defined in
      StudySpec is in type 'CATEGORICAL'.
  """

  parameterId = _messages.StringField(1)
  value = _messages.MessageField('extra_types.JsonValue', 2)


class GoogleCloudAiplatformV1UndeployModelOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.UndeployModel.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1UndeployModelRequest(_messages.Message):
  r"""Request message for EndpointService.UndeployModel.

  Messages:
    TrafficSplitValue: If this field is provided, then the Endpoint's
      traffic_split will be overwritten with it. If last DeployedModel is
      being undeployed from the Endpoint, the [Endpoint.traffic_split] will
      always end up empty when this call returns. A DeployedModel will be
      successfully undeployed only if it doesn't have any traffic assigned to
      it when this method executes, or if this field unassigns any traffic to
      it.

  Fields:
    deployedModelId: Required. The ID of the DeployedModel to be undeployed
      from the Endpoint.
    trafficSplit: If this field is provided, then the Endpoint's traffic_split
      will be overwritten with it. If last DeployedModel is being undeployed
      from the Endpoint, the [Endpoint.traffic_split] will always end up empty
      when this call returns. A DeployedModel will be successfully undeployed
      only if it doesn't have any traffic assigned to it when this method
      executes, or if this field unassigns any traffic to it.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class TrafficSplitValue(_messages.Message):
    r"""If this field is provided, then the Endpoint's traffic_split will be
    overwritten with it. If last DeployedModel is being undeployed from the
    Endpoint, the [Endpoint.traffic_split] will always end up empty when this
    call returns. A DeployedModel will be successfully undeployed only if it
    doesn't have any traffic assigned to it when this method executes, or if
    this field unassigns any traffic to it.

    Messages:
      AdditionalProperty: An additional property for a TrafficSplitValue
        object.

    Fields:
      additionalProperties: Additional properties of type TrafficSplitValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a TrafficSplitValue object.

      Fields:
        key: Name of the additional property.
        value: A integer attribute.
      """

      key = _messages.StringField(1)
      value = _messages.IntegerField(2, variant=_messages.Variant.INT32)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  deployedModelId = _messages.StringField(1)
  trafficSplit = _messages.MessageField('TrafficSplitValue', 2)


class GoogleCloudAiplatformV1UndeployModelResponse(_messages.Message):
  r"""Response message for EndpointService.UndeployModel."""


class GoogleCloudAiplatformV1UpdateSpecialistPoolOperationMetadata(_messages.Message):
  r"""Runtime operation metadata for
  SpecialistPoolService.UpdateSpecialistPool.

  Fields:
    genericMetadata: The operation generic information.
    specialistPool: Output only. The name of the SpecialistPool to which the
      specialists are being added. Format: `projects/{project_id}/locations/{l
      ocation_id}/specialistPools/{specialist_pool}`
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)
  specialistPool = _messages.StringField(2)


class GoogleCloudAiplatformV1UploadModelOperationMetadata(_messages.Message):
  r"""Details of ModelService.UploadModel operation.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1UploadModelRequest(_messages.Message):
  r"""Request message for ModelService.UploadModel.

  Fields:
    model: Required. The Model to create.
  """

  model = _messages.MessageField('GoogleCloudAiplatformV1Model', 1)


class GoogleCloudAiplatformV1UploadModelResponse(_messages.Message):
  r"""Response message of ModelService.UploadModel operation.

  Fields:
    model: The name of the uploaded Model resource. Format:
      `projects/{project}/locations/{location}/models/{model}`
  """

  model = _messages.StringField(1)


class GoogleCloudAiplatformV1UserActionReference(_messages.Message):
  r"""References an API call. It contains more information about long running
  operation and Jobs that are triggered by the API call.

  Fields:
    dataLabelingJob: For API calls that start a LabelingJob. Resource name of
      the LabelingJob. Format: 'projects/{project}/locations/{location}/dataLa
      belingJobs/{data_labeling_job}'
    method: The method name of the API call. For example,
      "/google.cloud.aiplatform.v1alpha1.DatasetService.CreateDataset"
    operation: For API calls that return a long running operation. Resource
      name of the long running operation. Format:
      'projects/{project}/locations/{location}/operations/{operation}'
  """

  dataLabelingJob = _messages.StringField(1)
  method = _messages.StringField(2)
  operation = _messages.StringField(3)


class GoogleCloudAiplatformV1WorkerPoolSpec(_messages.Message):
  r"""Represents the spec of a worker pool in a job.

  Fields:
    containerSpec: The custom container task.
    diskSpec: Disk spec.
    machineSpec: Optional. Immutable. The specification of a single machine.
    pythonPackageSpec: The Python packaged task.
    replicaCount: Optional. The number of worker replicas to use for this
      worker pool.
  """

  containerSpec = _messages.MessageField('GoogleCloudAiplatformV1ContainerSpec', 1)
  diskSpec = _messages.MessageField('GoogleCloudAiplatformV1DiskSpec', 2)
  machineSpec = _messages.MessageField('GoogleCloudAiplatformV1MachineSpec', 3)
  pythonPackageSpec = _messages.MessageField('GoogleCloudAiplatformV1PythonPackageSpec', 4)
  replicaCount = _messages.IntegerField(5)


class GoogleCloudAiplatformV1alpha1AutomaticResources(_messages.Message):
  r"""A description of resources that to large degree are decided by AI
  Platform, and require only a modest additional configuration. Each Model
  supporting these resources documents its specific guidelines.

  Fields:
    maxReplicaCount: Immutable. The maximum number of replicas this
      DeployedModel may be deployed on when the traffic against it increases.
      If the requested value is too large, the deployment will error, but if
      deployment succeeds then the ability to scale the model to that many
      replicas is guaranteed (barring service outages). If traffic against the
      DeployedModel increases beyond what its replicas at maximum may handle,
      a portion of the traffic will be dropped. If this value is not provided,
      a no upper bound for scaling under heavy traffic will be assume, though
      AI Platform may be unable to scale beyond certain replica number.
    minReplicaCount: Immutable. The minimum number of replicas this
      DeployedModel will be always deployed on. If traffic against it
      increases, it may dynamically be deployed onto more replicas up to
      max_replica_count, and as traffic decreases, some of these extra
      replicas may be freed. If the requested value is too large, the
      deployment will error.
  """

  maxReplicaCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  minReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1alpha1BatchMigrateResourcesOperationMetadata(_messages.Message):
  r"""Runtime operation information for
  MigrationService.BatchMigrateResources.

  Fields:
    genericMetadata: The common part of the operation metadata.
    partialResults: Partial results that reflect the latest migration
      operation progress.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)
  partialResults = _messages.MessageField('GoogleCloudAiplatformV1alpha1BatchMigrateResourcesOperationMetadataPartialResult', 2, repeated=True)


class GoogleCloudAiplatformV1alpha1BatchMigrateResourcesOperationMetadataPartialResult(_messages.Message):
  r"""Represents a partial result in batch migration operation for one
  MigrateResourceRequest.

  Fields:
    dataset: Migrated dataset resource name.
    error: The error result of the migration request in case of failure.
    model: Migrated model resource name.
    request: It's the same as the value in
      MigrateResourceRequest.migrate_resource_requests.
  """

  dataset = _messages.StringField(1)
  error = _messages.MessageField('GoogleRpcStatus', 2)
  model = _messages.StringField(3)
  request = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigrateResourceRequest', 4)


class GoogleCloudAiplatformV1alpha1BatchMigrateResourcesResponse(_messages.Message):
  r"""Response message for MigrationService.BatchMigrateResources.

  Fields:
    migrateResourceResponses: Successfully migrated resources.
  """

  migrateResourceResponses = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigrateResourceResponse', 1, repeated=True)


class GoogleCloudAiplatformV1alpha1CreateDatasetOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.CreateDataset.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1CreateEndpointOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.CreateEndpoint.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1CreateIndexEndpointOperationMetadata(_messages.Message):
  r"""Runtime operation information for
  IndexEndpointService.CreateIndexEndpoint.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1CreateIndexOperationMetadata(_messages.Message):
  r"""Runtime operation information for IndexService.CreateIndex.

  Fields:
    genericMetadata: The operation generic information.
    nearestNeighborSearchOperationMetadata: The operation metadata with regard
      to ScaNN Index operation.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)
  nearestNeighborSearchOperationMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadata', 2)


class GoogleCloudAiplatformV1alpha1CreateSpecialistPoolOperationMetadata(_messages.Message):
  r"""Runtime operation information for
  SpecialistPoolService.CreateSpecialistPool.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1DedicatedResources(_messages.Message):
  r"""A description of resources that are dedicated to a DeployedModel, and
  that need a higher degree of manual configuration.

  Fields:
    machineSpec: Required. Immutable. The specification of a single machine
      used by the prediction.
    minReplicaCount: Required. Immutable. The minimum number of machine
      replicas this DeployedModel will be always deployed on. If traffic
      against it increases, it may dynamically be deployed onto more replicas,
      and as traffic decreases, some of these extra replicas may be freed.
      Note: if machine_spec.accelerator_count is above 0, currently the model
      will be always deployed precisely on min_replica_count.
  """

  machineSpec = _messages.MessageField('GoogleCloudAiplatformV1alpha1MachineSpec', 1)
  minReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1alpha1DeleteOperationMetadata(_messages.Message):
  r"""Details of operations that perform deletes of any entities.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1DeployIndexOperationMetadata(_messages.Message):
  r"""Runtime operation information for IndexEndpointService.DeployIndex.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1DeployIndexResponse(_messages.Message):
  r"""Response message for IndexEndpointService.DeployIndex.

  Fields:
    deployedIndex: The DeployedIndex that had been deployed in the
      IndexEndpoint.
  """

  deployedIndex = _messages.MessageField('GoogleCloudAiplatformV1alpha1DeployedIndex', 1)


class GoogleCloudAiplatformV1alpha1DeployModelOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.DeployModel.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1DeployModelResponse(_messages.Message):
  r"""Response message for EndpointService.DeployModel.

  Fields:
    deployedModel: The DeployedModel that had been deployed in the Endpoint.
  """

  deployedModel = _messages.MessageField('GoogleCloudAiplatformV1alpha1DeployedModel', 1)


class GoogleCloudAiplatformV1alpha1DeployedIndex(_messages.Message):
  r"""A deployment of an Index. IndexEndpoints contain one or more
  DeployedIndexes.

  Fields:
    automaticResources: Optional. A description of resources that the
      DeployedIndex uses, which to large degree are decided by AI Platform,
      and optionally allows only a modest additional configuration. If
      min_replica_count is not set, the default value is 1. If
      max_replica_count is not set, the default value is min_replica_count.
      The max allowed replica count is 1000. The user is billed for the
      resources (at least their minimal amount) even if the DeployedIndex
      receives no traffic.
    createTime: Output only. Timestamp when the DeployedIndex was created.
    displayName: The display name of the DeployedIndex. If not provided upon
      creation, the Index's display_name is used.
    id: Required. The user specified ID of the DeployedIndex. The ID can be up
      to 128 characters long and must start with a letter and only contain
      letters, numbers, and underscores. The ID must be unique within the
      project it is created in.
    index: Required. The name of the Index this is the deployment of. We may
      refer to this Index as the DeployedIndex's "original" Index.
    indexSyncTime: Output only. The DeployedIndex may depend on various data
      on its original Index. Additionally when certain changes to the original
      Index are being done (e.g. when what the Index contains is being
      changed) the DeployedIndex may be asynchronously updated in the
      background to reflect this changes. If this timestamp's value is at
      least the Index.update_time of the original Index, it means that this
      DeployedIndex and the original Index are in sync. If this timestamp is
      older, then to see which updates this DeployedIndex already contains
      (and which not), one must list Operations working on the original Index.
      Only the successfully completed Operations with
      Operations.metadata.generic_metadata.update_time equal or before this
      sync time are contained in this DeployedIndex.
    privateEndpoints: Output only. Provides paths for users to send requests
      directly to the deployed index services running on Cloud via private
      services access. This field is populated if network is configured.
  """

  automaticResources = _messages.MessageField('GoogleCloudAiplatformV1alpha1AutomaticResources', 1)
  createTime = _messages.StringField(2)
  displayName = _messages.StringField(3)
  id = _messages.StringField(4)
  index = _messages.StringField(5)
  indexSyncTime = _messages.StringField(6)
  privateEndpoints = _messages.MessageField('GoogleCloudAiplatformV1alpha1IndexPrivateEndpoints', 7)


class GoogleCloudAiplatformV1alpha1DeployedModel(_messages.Message):
  r"""A deployment of a Model. Endpoints contain one or more DeployedModels.

  Fields:
    automaticResources: A description of resources that to large degree are
      decided by AI Platform, and require only a modest additional
      configuration.
    createTime: Output only. Timestamp when the DeployedModel was created.
    dedicatedResources: A description of resources that are dedicated to the
      DeployedModel, and that need a higher degree of manual configuration.
    displayName: The display name of the DeployedModel. If not provided upon
      creation, the Model's display_name is used.
    explanationSpec: Explanation configuration for this DeployedModel. When
      deploying a Model using EndpointService.DeployModel, this value
      overrides the value of Model.explanation_spec. All fields of
      explanation_spec are optional in the request. If a field of
      explanation_spec is not populated, the value of the same field of
      Model.explanation_spec is inherited. If the corresponding
      Model.explanation_spec is not populated, all fields of the
      explanation_spec will be used for the explanation configuration.
    id: Output only. The ID of the DeployedModel.
    model: Required. The name of the Model that this is the deployment of.
      Note that the Model may be in a different location than the
      DeployedModel's Endpoint.
  """

  automaticResources = _messages.MessageField('GoogleCloudAiplatformV1alpha1AutomaticResources', 1)
  createTime = _messages.StringField(2)
  dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1alpha1DedicatedResources', 3)
  displayName = _messages.StringField(4)
  explanationSpec = _messages.MessageField('GoogleCloudAiplatformV1alpha1ExplanationSpec', 5)
  id = _messages.StringField(6)
  model = _messages.StringField(7)


class GoogleCloudAiplatformV1alpha1ExplanationMetadata(_messages.Message):
  r"""Metadata describing the Model's input and output for explanation.

  Messages:
    InputsValue: Required. Map from feature names to feature input metadata.
      Keys are the name of the features. Values are the specification of the
      feature. An empty InputMetadata is valid. It describes a text feature
      which has the name specified as the key in ExplanationMetadata.inputs.
      The baseline of the empty feature is chosen by AI Platform. For AI
      Platform provided Tensorflow images, the key can be any friendly name of
      the feature. Once specified, featureAttributions are keyed by this key
      (if not grouped with another feature). For custom images, the key must
      match with the key in instance.
    OutputsValue: Required. Map from output names to output metadata. For AI
      Platform provided Tensorflow images, keys can be any string user
      defines. For custom images, keys are the name of the output field in the
      prediction to be explained. Currently only one key is allowed.

  Fields:
    featureAttributionsSchemaUri: Points to a YAML file stored on Google Cloud
      Storage describing the format of the feature attributions. The schema is
      defined as an OpenAPI 3.0.2 [Schema
      Object](https://tinyurl.com/y538mdwt#schema-object). AutoML tabular
      Models always have this field populated by AI Platform. Note: The URI
      given on output may be different, including the URI scheme, than the one
      given on input. The output URI will point to a location where the user
      only has a read access.
    inputs: Required. Map from feature names to feature input metadata. Keys
      are the name of the features. Values are the specification of the
      feature. An empty InputMetadata is valid. It describes a text feature
      which has the name specified as the key in ExplanationMetadata.inputs.
      The baseline of the empty feature is chosen by AI Platform. For AI
      Platform provided Tensorflow images, the key can be any friendly name of
      the feature. Once specified, featureAttributions are keyed by this key
      (if not grouped with another feature). For custom images, the key must
      match with the key in instance.
    outputs: Required. Map from output names to output metadata. For AI
      Platform provided Tensorflow images, keys can be any string user
      defines. For custom images, keys are the name of the output field in the
      prediction to be explained. Currently only one key is allowed.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class InputsValue(_messages.Message):
    r"""Required. Map from feature names to feature input metadata. Keys are
    the name of the features. Values are the specification of the feature. An
    empty InputMetadata is valid. It describes a text feature which has the
    name specified as the key in ExplanationMetadata.inputs. The baseline of
    the empty feature is chosen by AI Platform. For AI Platform provided
    Tensorflow images, the key can be any friendly name of the feature. Once
    specified, featureAttributions are keyed by this key (if not grouped with
    another feature). For custom images, the key must match with the key in
    instance.

    Messages:
      AdditionalProperty: An additional property for a InputsValue object.

    Fields:
      additionalProperties: Additional properties of type InputsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a InputsValue object.

      Fields:
        key: Name of the additional property.
        value: A GoogleCloudAiplatformV1alpha1ExplanationMetadataInputMetadata
          attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('GoogleCloudAiplatformV1alpha1ExplanationMetadataInputMetadata', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  @encoding.MapUnrecognizedFields('additionalProperties')
  class OutputsValue(_messages.Message):
    r"""Required. Map from output names to output metadata. For AI Platform
    provided Tensorflow images, keys can be any string user defines. For
    custom images, keys are the name of the output field in the prediction to
    be explained. Currently only one key is allowed.

    Messages:
      AdditionalProperty: An additional property for a OutputsValue object.

    Fields:
      additionalProperties: Additional properties of type OutputsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a OutputsValue object.

      Fields:
        key: Name of the additional property.
        value: A
          GoogleCloudAiplatformV1alpha1ExplanationMetadataOutputMetadata
          attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('GoogleCloudAiplatformV1alpha1ExplanationMetadataOutputMetadata', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  featureAttributionsSchemaUri = _messages.StringField(1)
  inputs = _messages.MessageField('InputsValue', 2)
  outputs = _messages.MessageField('OutputsValue', 3)


class GoogleCloudAiplatformV1alpha1ExplanationMetadataInputMetadata(_messages.Message):
  r"""Metadata of the input of a feature. Fields other than
  InputMetadata.input_baselines are applicable only for Models that are using
  AI Platform-provided images for Tensorflow.

  Fields:
    inputBaselines: Baseline inputs for this feature. If no baseline is
      specified, AI Platform chooses the baseline for this feature. If
      multiple baselines are specified, AI Platform returns the average
      attributions across them in Attributions.baseline_attribution. For AI
      Platform provided Tensorflow images (both 1.x and 2.x), the shape of
      each baseline must match the shape of the input tensor. If a scalar is
      provided, we broadcast to the same shape as the input tensor. For custom
      images, the element of the baselines must be in the same format as the
      feature's input in the instance[]. The schema of any single instance may
      be specified via Endpoint's DeployedModels' Model's PredictSchemata's
      instance_schema_uri.
  """

  inputBaselines = _messages.MessageField('extra_types.JsonValue', 1, repeated=True)


class GoogleCloudAiplatformV1alpha1ExplanationMetadataOutputMetadata(_messages.Message):
  r"""Metadata of the prediction output to be explained.

  Fields:
    displayNameMappingKey: Specify a field name in the prediction to look for
      the display name. Use this if the prediction contains the display names
      for the outputs. The display names in the prediction must have the same
      shape of the outputs, so that it can be located by
      Attribution.output_index for a specific output.
    indexDisplayNameMapping: Static mapping between the index and display
      name. Use this if the outputs are a deterministic n-dimensional array,
      e.g. a list of scores of all the classes in a pre-defined order for a
      multi-classification Model. It's not feasible if the outputs are non-
      deterministic, e.g. the Model produces top-k classes or sort the outputs
      by their values. The shape of the value must be an n-dimensional array
      of strings. The number of dimensions must match that of the outputs to
      be explained. The Attribution.output_display_name is populated by
      locating in the mapping with Attribution.output_index.
  """

  displayNameMappingKey = _messages.StringField(1)
  indexDisplayNameMapping = _messages.MessageField('extra_types.JsonValue', 2)


class GoogleCloudAiplatformV1alpha1ExplanationParameters(_messages.Message):
  r"""Parameters to configure explaining for Model's predictions.

  Fields:
    sampledShapleyAttribution: An attribution method that approximates Shapley
      values for features that contribute to the label being predicted. A
      sampling strategy is used to approximate the value rather than
      considering all subsets of features. Refer to this paper for model
      details: https://arxiv.org/abs/1306.4265.
  """

  sampledShapleyAttribution = _messages.MessageField('GoogleCloudAiplatformV1alpha1SampledShapleyAttribution', 1)


class GoogleCloudAiplatformV1alpha1ExplanationSpec(_messages.Message):
  r"""Specification of Model explanation.

  Fields:
    metadata: Required. Metadata describing the Model's input and output for
      explanation.
    parameters: Required. Parameters that configure explaining of the Model's
      predictions.
  """

  metadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1ExplanationMetadata', 1)
  parameters = _messages.MessageField('GoogleCloudAiplatformV1alpha1ExplanationParameters', 2)


class GoogleCloudAiplatformV1alpha1ExportDataOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.ExportData.

  Fields:
    gcsOutputDirectory: A Google Cloud Storage directory which path ends with
      '/'. The exported data is stored in the directory.
    genericMetadata: The common part of the operation metadata.
  """

  gcsOutputDirectory = _messages.StringField(1)
  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 2)


class GoogleCloudAiplatformV1alpha1ExportDataResponse(_messages.Message):
  r"""Response message for DatasetService.ExportData.

  Fields:
    exportedFiles: All of the files that are exported in this export
      operation.
  """

  exportedFiles = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1alpha1ExportEvaluatedDataItemsOperationMetadata(_messages.Message):
  r"""Details of ModelService.ExportEvaluatedDataItems operation.

  Fields:
    genericMetadata: The common part of the operation metadata.
    outputInfo: Output only. Information further describing the output of this
      operation.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)
  outputInfo = _messages.MessageField('GoogleCloudAiplatformV1alpha1ExportEvaluatedDataItemsOperationMetadataOutputInfo', 2)


class GoogleCloudAiplatformV1alpha1ExportEvaluatedDataItemsOperationMetadataOutputInfo(_messages.Message):
  r"""Further describes this operation's output. Supplements
  ExportEvaluatedDataItemsRequest.output_config.

  Fields:
    bigqueryOutputDataset: Output only. The path of the BigQuery dataset
      created, in bq://projectId.bqDatasetId.tableId format, into which the
      evaluated DataItems are exported.
  """

  bigqueryOutputDataset = _messages.StringField(1)


class GoogleCloudAiplatformV1alpha1ExportEvaluatedDataItemsOperationResponse(_messages.Message):
  r"""Response of ModelService.ExportEvaluatedDataItems operation."""


class GoogleCloudAiplatformV1alpha1ExportModelOperationMetadata(_messages.Message):
  r"""Details of ModelService.ExportModel operation.

  Fields:
    genericMetadata: The common part of the operation metadata.
    outputInfo: Output only. Information further describing the output of this
      Model export.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)
  outputInfo = _messages.MessageField('GoogleCloudAiplatformV1alpha1ExportModelOperationMetadataOutputInfo', 2)


class GoogleCloudAiplatformV1alpha1ExportModelOperationMetadataOutputInfo(_messages.Message):
  r"""Further describes the output of the ExportModel. Supplements
  ExportModelRequest.OutputConfig.

  Fields:
    gcsOutputDirectory: Output only. If the Model is being exported to Google
      Cloud Storage this is the full path of the directory created, into which
      the Model files are being written to.
  """

  gcsOutputDirectory = _messages.StringField(1)


class GoogleCloudAiplatformV1alpha1ExportModelResponse(_messages.Message):
  r"""Response message of ModelService.ExportModel operation."""


class GoogleCloudAiplatformV1alpha1GenericOperationMetadata(_messages.Message):
  r"""Generic Metadata shared by all operations.

  Fields:
    createTime: Output only. Time when the operation was created.
    partialFailures: Output only. Partial failures encountered. E.g. single
      files that couldn't be read. This field should never exceed 20 entries.
      Status details field will contain standard GCP error details.
    updateTime: Output only. Time when the operation was updated for the last
      time. If the operation has finished (successfully or not), this is the
      finish time.
  """

  createTime = _messages.StringField(1)
  partialFailures = _messages.MessageField('GoogleRpcStatus', 2, repeated=True)
  updateTime = _messages.StringField(3)


class GoogleCloudAiplatformV1alpha1ImportDataOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.ImportData.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1ImportDataResponse(_messages.Message):
  r"""Response message for DatasetService.ImportData."""


class GoogleCloudAiplatformV1alpha1IndexPrivateEndpoints(_messages.Message):
  r"""IndexPrivateEndpoints proto is used to provide paths for users to send
  requests via private services access.

  Fields:
    matchGrpcAddress: Output only. The ip address used to send match gRPC
      requests.
  """

  matchGrpcAddress = _messages.StringField(1)


class GoogleCloudAiplatformV1alpha1MachineSpec(_messages.Message):
  r"""Specification of a single machine.

  Enums:
    AcceleratorTypeValueValuesEnum: Immutable. The type of accelerator(s) that
      may be attached to the machine as per accelerator_count.

  Fields:
    acceleratorCount: The number of accelerators to attach to the machine.
    acceleratorType: Immutable. The type of accelerator(s) that may be
      attached to the machine as per accelerator_count.
    machineType: Immutable. The type of the machine. For the machine types
      supported for prediction, see https://tinyurl.com/aip-
      docs/predictions/machine-types. For machine types supported for creating
      a custom training job, see https://tinyurl.com/aip-
      docs/training/configure-compute. For DeployedModel this field is
      optional, and the default value is `n1-standard-2`. For
      BatchPredictionJob or as part of WorkerPoolSpec this field is required.
  """

  class AcceleratorTypeValueValuesEnum(_messages.Enum):
    r"""Immutable. The type of accelerator(s) that may be attached to the
    machine as per accelerator_count.

    Values:
      ACCELERATOR_TYPE_UNSPECIFIED: Unspecified accelerator type, which means
        no accelerator.
      NVIDIA_TESLA_K80: Nvidia Tesla K80 GPU.
      NVIDIA_TESLA_P100: Nvidia Tesla P100 GPU.
      NVIDIA_TESLA_V100: Nvidia Tesla V100 GPU.
      NVIDIA_TESLA_P4: Nvidia Tesla P4 GPU.
      NVIDIA_TESLA_T4: Nvidia Tesla T4 GPU.
      TPU_V2: TPU v2.
      TPU_V3: TPU v3.
    """
    ACCELERATOR_TYPE_UNSPECIFIED = 0
    NVIDIA_TESLA_K80 = 1
    NVIDIA_TESLA_P100 = 2
    NVIDIA_TESLA_V100 = 3
    NVIDIA_TESLA_P4 = 4
    NVIDIA_TESLA_T4 = 5
    TPU_V2 = 6
    TPU_V3 = 7

  acceleratorCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  acceleratorType = _messages.EnumField('AcceleratorTypeValueValuesEnum', 2)
  machineType = _messages.StringField(3)


class GoogleCloudAiplatformV1alpha1MigratableResource(_messages.Message):
  r"""Represents one resource that exists in automl.googleapis.com,
  datalabeling.googleapis.com or ml.googleapis.com.

  Fields:
    automlDataset: Output only. Represents one Dataset in
      automl.googleapis.com.
    automlModel: Output only. Represents one Model in automl.googleapis.com.
    dataLabelingDataset: Output only. Represents one Dataset in
      datalabeling.googleapis.com.
    lastMigrateTime: Output only. Timestamp when the last migration attempt on
      this MigratableResource started. Will not be set if there's no migration
      attempt on this MigratableResource.
    lastUpdateTime: Output only. Timestamp when this MigratableResource was
      last updated.
    mlEngineModelVersion: Output only. Represents one Version in
      ml.googleapis.com.
  """

  automlDataset = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigratableResourceAutomlDataset', 1)
  automlModel = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigratableResourceAutomlModel', 2)
  dataLabelingDataset = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigratableResourceDataLabelingDataset', 3)
  lastMigrateTime = _messages.StringField(4)
  lastUpdateTime = _messages.StringField(5)
  mlEngineModelVersion = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigratableResourceMlEngineModelVersion', 6)


class GoogleCloudAiplatformV1alpha1MigratableResourceAutomlDataset(_messages.Message):
  r"""Represents one Dataset in automl.googleapis.com.

  Fields:
    dataset: Full resource name of automl Dataset. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`.
    datasetDisplayName: The Dataset's display name in automl.googleapis.com.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1MigratableResourceAutomlModel(_messages.Message):
  r"""Represents one Model in automl.googleapis.com.

  Fields:
    model: Full resource name of automl Model. Format:
      `projects/{project}/locations/{location}/models/{model}`.
    modelDisplayName: The Model's display name in automl.googleapis.com.
  """

  model = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1MigratableResourceDataLabelingDataset(_messages.Message):
  r"""Represents one Dataset in datalabeling.googleapis.com.

  Fields:
    dataLabelingAnnotatedDatasets: The migratable AnnotatedDataset in
      datalabeling.googleapis.com belongs to the data labeling Dataset.
    dataset: Full resource name of data labeling Dataset. Format:
      `projects/{project}/datasets/{dataset}`.
    datasetDisplayName: The Dataset's display name in
      datalabeling.googleapis.com.
  """

  dataLabelingAnnotatedDatasets = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset', 1, repeated=True)
  dataset = _messages.StringField(2)
  datasetDisplayName = _messages.StringField(3)


class GoogleCloudAiplatformV1alpha1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset(_messages.Message):
  r"""Represents one AnnotatedDataset in datalabeling.googleapis.com.

  Fields:
    annotatedDataset: Full resource name of data labeling AnnotatedDataset.
      Format: `projects/{project}/datasets/{dataset}/annotatedDatasets/{annota
      ted_dataset}`.
    annotatedDatasetDisplayName: The AnnotatedDataset's display name in
      datalabeling.googleapis.com.
  """

  annotatedDataset = _messages.StringField(1)
  annotatedDatasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1MigratableResourceMlEngineModelVersion(_messages.Message):
  r"""Represents one model Version in ml.googleapis.com.

  Fields:
    endpoint: The ml.googleapis.com endpoint that this model Version currently
      lives in. Example values: * ml.googleapis.com * us-centrall-
      ml.googleapis.com * europe-west4-ml.googleapis.com * asia-
      east1-ml.googleapis.com
    version: Full resource name of ml engine model Version. Format:
      `projects/{project}/models/{model}/versions/{version}`.
  """

  endpoint = _messages.StringField(1)
  version = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1MigrateResourceRequest(_messages.Message):
  r"""Config of migrating one resource from automl.googleapis.com,
  datalabeling.googleapis.com and ml.googleapis.com to AI Platform.

  Fields:
    migrateAutomlDatasetConfig: Config for migrating Dataset in
      automl.googleapis.com to AI Platform's Dataset.
    migrateAutomlModelConfig: Config for migrating Model in
      automl.googleapis.com to AI Platform's Model.
    migrateDataLabelingDatasetConfig: Config for migrating Dataset in
      datalabeling.googleapis.com to AI Platform's Dataset.
    migrateMlEngineModelVersionConfig: Config for migrating Version in
      ml.googleapis.com to AI Platform's Model.
  """

  migrateAutomlDatasetConfig = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateAutomlDatasetConfig', 1)
  migrateAutomlModelConfig = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateAutomlModelConfig', 2)
  migrateDataLabelingDatasetConfig = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateDataLabelingDatasetConfig', 3)
  migrateMlEngineModelVersionConfig = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateMlEngineModelVersionConfig', 4)


class GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateAutomlDatasetConfig(_messages.Message):
  r"""Config for migrating Dataset in automl.googleapis.com to AI Platform's
  Dataset.

  Fields:
    dataset: Required. Full resource name of automl Dataset. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`.
    datasetDisplayName: Required. Display name of the Dataset in AI Platform.
      System will pick a display name if unspecified.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateAutomlModelConfig(_messages.Message):
  r"""Config for migrating Model in automl.googleapis.com to AI Platform's
  Model.

  Fields:
    model: Required. Full resource name of automl Model. Format:
      `projects/{project}/locations/{location}/models/{model}`.
    modelDisplayName: Optional. Display name of the model in AI Platform.
      System will pick a display name if unspecified.
  """

  model = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateDataLabelingDatasetConfig(_messages.Message):
  r"""Config for migrating Dataset in datalabeling.googleapis.com to AI
  Platform's Dataset.

  Fields:
    dataset: Required. Full resource name of data labeling Dataset. Format:
      `projects/{project}/datasets/{dataset}`.
    datasetDisplayName: Optional. Display name of the Dataset in AI Platform.
      System will pick a display name if unspecified.
    migrateDataLabelingAnnotatedDatasetConfigs: Optional. Configs for
      migrating AnnotatedDataset in datalabeling.googleapis.com to AI
      Platform's SavedQuery. The specified AnnotatedDatasets have to belong to
      the datalabeling Dataset.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)
  migrateDataLabelingAnnotatedDatasetConfigs = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig', 3, repeated=True)


class GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig(_messages.Message):
  r"""Config for migrating AnnotatedDataset in datalabeling.googleapis.com to
  AI Platform's SavedQuery.

  Fields:
    annotatedDataset: Required. Full resource name of data labeling
      AnnotatedDataset. Format: `projects/{project}/datasets/{dataset}/annotat
      edDatasets/{annotated_dataset}`.
  """

  annotatedDataset = _messages.StringField(1)


class GoogleCloudAiplatformV1alpha1MigrateResourceRequestMigrateMlEngineModelVersionConfig(_messages.Message):
  r"""Config for migrating version in ml.googleapis.com to AI Platform's
  Model.

  Fields:
    endpoint: Required. The ml.googleapis.com endpoint that this model version
      should be migrated from. Example values: * ml.googleapis.com * us-
      centrall-ml.googleapis.com * europe-west4-ml.googleapis.com * asia-
      east1-ml.googleapis.com
    modelDisplayName: Required. Display name of the model in AI Platform.
      System will pick a display name if unspecified.
    modelVersion: Required. Full resource name of ml engine model version.
      Format: `projects/{project}/models/{model}/versions/{version}`.
  """

  endpoint = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)
  modelVersion = _messages.StringField(3)


class GoogleCloudAiplatformV1alpha1MigrateResourceResponse(_messages.Message):
  r"""Describes a successfully migrated resource.

  Fields:
    dataset: Migrated Dataset's resource name.
    migratableResource: Before migration, the identifier in ml.googleapis.com,
      automl.googleapis.com or datalabeling.googleapis.com.
    model: Migrated Model's resource name.
  """

  dataset = _messages.StringField(1)
  migratableResource = _messages.MessageField('GoogleCloudAiplatformV1alpha1MigratableResource', 2)
  model = _messages.StringField(3)


class GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadata(_messages.Message):
  r"""Runtime operation metadata with regard to ScaNN Index.

  Fields:
    contentValidationStats: The validation stats of the content (per file) to
      be inserted or updated on the ScaNN Index resource. Populated if
      contentsDeltaUri is provided as part of Index.metadata. Please note
      that, currently for those files that are broken or has unsupported file
      format, we will not have the stats for those files.
  """

  contentValidationStats = _messages.MessageField('GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadataContentValidationStats', 1, repeated=True)


class GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadataContentValidationStats(_messages.Message):
  r"""A GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadataCon
  tentValidationStats object.

  Fields:
    invalidRecordCount: Number of records in this file we skipped due to
      validate errors.
    partialErrors: The detail information of the partial failures encountered
      for those invalid records that couldn't be parsed. Up to 50 partial
      errors will be reported.
    sourceGcsUri: GCS uri pointing to the original file in user's bucket.
    validRecordCount: Number of records in this file that were successfully
      processed.
  """

  invalidRecordCount = _messages.IntegerField(1)
  partialErrors = _messages.MessageField('GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadataRecordError', 2, repeated=True)
  sourceGcsUri = _messages.StringField(3)
  validRecordCount = _messages.IntegerField(4)


class GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadataRecordError(_messages.Message):
  r"""A GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadataRec
  ordError object.

  Enums:
    ErrorTypeValueValuesEnum: The error type of this record.

  Fields:
    embeddingId: Empty if the embedding id is failed to parse.
    errorMessage: A human-readable message that is shown to the user to help
      them fix the error. Note that this message may change from time to time,
      your code should check against error_type as the source of truth.
    errorType: The error type of this record.
    rawRecord: The original content of this record.
    sourceGcsUri: GCS uri pointing to the original file in user's bucket.
  """

  class ErrorTypeValueValuesEnum(_messages.Enum):
    r"""The error type of this record.

    Values:
      ERROR_TYPE_UNSPECIFIED: Default, shall not be used.
      EMPTY_LINE: The record is empty.
      INVALID_JSON_SYNTAX: Invalid json format.
      INVALID_CSV_SYNTAX: Invalid csv format.
      INVALID_AVRO_SYNTAX: Invalid avro format.
      INVALID_EMBEDDING_ID: The embedding id is not valid.
      EMBEDDING_SIZE_MISMATCH: The size of the embedding vectors does not
        match with the specified dimension.
      NAMESPACE_MISSING: The `namespace` field is missing.
    """
    ERROR_TYPE_UNSPECIFIED = 0
    EMPTY_LINE = 1
    INVALID_JSON_SYNTAX = 2
    INVALID_CSV_SYNTAX = 3
    INVALID_AVRO_SYNTAX = 4
    INVALID_EMBEDDING_ID = 5
    EMBEDDING_SIZE_MISMATCH = 6
    NAMESPACE_MISSING = 7

  embeddingId = _messages.StringField(1)
  errorMessage = _messages.StringField(2)
  errorType = _messages.EnumField('ErrorTypeValueValuesEnum', 3)
  rawRecord = _messages.StringField(4)
  sourceGcsUri = _messages.StringField(5)


class GoogleCloudAiplatformV1alpha1SampledShapleyAttribution(_messages.Message):
  r"""An attribution method that approximates Shapley values for features that
  contribute to the label being predicted. A sampling strategy is used to
  approximate the value rather than considering all subsets of features.

  Fields:
    pathCount: Required. The number of feature permutations to consider when
      approximating the Shapley values. Valid range of its value is [1, 50],
      inclusively.
  """

  pathCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1alpha1SchemaAnnotationSpecColor(_messages.Message):
  r"""An entry of mapping between color and AnnotationSpec. The mapping is
  used in segmentation mask.

  Fields:
    color: The color of the AnnotationSpec in a segmentation mask.
    displayName: The display name of the AnnotationSpec represented by the
      color in the segmentation mask.
    id: The ID of the AnnotationSpec represented by the color in the
      segmentation mask.
  """

  color = _messages.MessageField('GoogleTypeColor', 1)
  displayName = _messages.StringField(2)
  id = _messages.StringField(3)


class GoogleCloudAiplatformV1alpha1SchemaImageBoundingBoxAnnotation(_messages.Message):
  r"""Annotation details specific to image object detection.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    xMax: The rightmost coordinate of the bounding box.
    xMin: The leftmost coordinate of the bounding box.
    yMax: The bottommost coordinate of the bounding box.
    yMin: The topmost coordinate of the bounding box.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  xMax = _messages.FloatField(3)
  xMin = _messages.FloatField(4)
  yMax = _messages.FloatField(5)
  yMin = _messages.FloatField(6)


class GoogleCloudAiplatformV1alpha1SchemaImageClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to image classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaImageDataItem(_messages.Message):
  r"""Payload of Image DataItem.

  Fields:
    gcsUri: Required. Google Cloud Storage URI points to the original image in
      user's bucket. The image is up to 30MB in size.
    mimeType: Output only. The mime type of the content of the image. Only the
      images in below listed mime types are supported. - image/jpeg -
      image/gif - image/png - image/webp - image/bmp - image/tiff -
      image/vnd.microsoft.icon
  """

  gcsUri = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaImageDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Image DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Image DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaImageSegmentationAnnotation(_messages.Message):
  r"""Annotation details specific to image segmentation.

  Fields:
    maskAnnotation: Mask based segmentation annotation. Only one mask
      annotation can exist for one image.
    polygonAnnotation: Polygon annotation.
    polylineAnnotation: Polyline annotation.
  """

  maskAnnotation = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaImageSegmentationAnnotationMaskAnnotation', 1)
  polygonAnnotation = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaImageSegmentationAnnotationPolygonAnnotation', 2)
  polylineAnnotation = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaImageSegmentationAnnotationPolylineAnnotation', 3)


class GoogleCloudAiplatformV1alpha1SchemaImageSegmentationAnnotationMaskAnnotation(_messages.Message):
  r"""The mask based segmentation annotation.

  Fields:
    annotationSpecColors: The mapping between color and AnnotationSpec for
      this Annotation.
    maskGcsUri: Google Cloud Storage URI that points to the mask image. The
      image must be in PNG format. It must have the same size as the
      DataItem's image. Each pixel in the image mask represents the
      AnnotationSpec which the pixel in the image DataItem belong to. Each
      color is mapped to one AnnotationSpec based on annotation_spec_colors.
  """

  annotationSpecColors = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaAnnotationSpecColor', 1, repeated=True)
  maskGcsUri = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaImageSegmentationAnnotationPolygonAnnotation(_messages.Message):
  r"""Represents a polygon in image.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    vertexes: The vertexes are connected one by one and the last vertex is
      connected to the first one to represent a polygon.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  vertexes = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaVertex', 3, repeated=True)


class GoogleCloudAiplatformV1alpha1SchemaImageSegmentationAnnotationPolylineAnnotation(_messages.Message):
  r"""Represents a polyline in image.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    vertexes: The vertexes are connected one by one and the last vertex in not
      connected to the first one.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  vertexes = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaVertex', 3, repeated=True)


class GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain tables data.

  Fields:
    inputConfig: A
      GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataInputConfig
      attribute.
  """

  inputConfig = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataInputConfig', 1)


class GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataBigQuerySource(_messages.Message):
  r"""A GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataBigQuerySource
  object.

  Fields:
    uri: The URI of a BigQuery table. e.g.
      bq://projectId.bqDatasetId.bqTableId
  """

  uri = _messages.StringField(1)


class GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataGcsSource(_messages.Message):
  r"""A GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataGcsSource
  object.

  Fields:
    uri: Cloud Storage URI of one or more files. Only CSV files are supported.
      The first line of the CSV file is used as the header. If there are
      multiple files, the header is the first line of the lexicographically
      first file, the other files must either contain the exact same header or
      omit the header.
  """

  uri = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataInputConfig(_messages.Message):
  r"""The tables Dataset's data source. The Dataset doesn't store the data
  directly, but only pointer(s) to its data.

  Fields:
    bigquerySource: A
      GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataBigQuerySource
      attribute.
    gcsSource: A
      GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataGcsSource
      attribute.
  """

  bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataBigQuerySource', 1)
  gcsSource = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTablesDatasetMetadataGcsSource', 2)


class GoogleCloudAiplatformV1alpha1SchemaTextClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to text classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaTextDataItem(_messages.Message):
  r"""Payload of Text DataItem.

  Fields:
    gcsUri: Output only. Google Cloud Storage URI points to the original text
      in user's bucket. The text file is up to 10MB in size.
  """

  gcsUri = _messages.StringField(1)


class GoogleCloudAiplatformV1alpha1SchemaTextDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Text DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Text DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaTextExtractionAnnotation(_messages.Message):
  r"""Annotation details specific to text extraction.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    textSegment: The segment of the text content.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  textSegment = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTextSegment', 3)


class GoogleCloudAiplatformV1alpha1SchemaTextSegment(_messages.Message):
  r"""The text segment inside of DataItem.

  Fields:
    content: The text content in the segment for output only.
    endOffset: Zero-based character index of the first character past the end
      of the text segment (counting character from the beginning of the text).
      The character at the end_offset is NOT included in the text segment.
    startOffset: Zero-based character index of the first character of the text
      segment (counting characters from the beginning of the text).
  """

  content = _messages.StringField(1)
  endOffset = _messages.IntegerField(2, variant=_messages.Variant.UINT64)
  startOffset = _messages.IntegerField(3, variant=_messages.Variant.UINT64)


class GoogleCloudAiplatformV1alpha1SchemaTextSentimentAnnotation(_messages.Message):
  r"""Annotation details specific to text sentiment.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    sentiment: The sentiment score for text.
    sentimentMax: The sentiment max score for text.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  sentiment = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  sentimentMax = _messages.IntegerField(4, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1alpha1SchemaTextSentimentSavedQueryMetadata(_messages.Message):
  r"""The metadata of SavedQuery contains TextSentiment Annotations.

  Fields:
    sentimentMax: The maximum sentiment of sentiment Anntoation in this
      SavedQuery.
  """

  sentimentMax = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1alpha1SchemaTimeSegment(_messages.Message):
  r"""A time period inside of a DataItem that has a time dimension (e.g.
  video).

  Fields:
    endTimeOffset: End of the time segment (exclusive), represented as the
      duration since the start of the DataItem.
    startTimeOffset: Start of the time segment (inclusive), represented as the
      duration since the start of the DataItem.
  """

  endTimeOffset = _messages.StringField(1)
  startTimeOffset = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain time series data.

  Fields:
    inputConfig: A
      GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataInputConfig
      attribute.
    timeColumn: The column name of the time column that identifies time order
      in the time series.
    timeSeriesIdentifierColumn: The column name of the time series identifier
      column that identifies the time series.
  """

  inputConfig = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataInputConfig', 1)
  timeColumn = _messages.StringField(2)
  timeSeriesIdentifierColumn = _messages.StringField(3)


class GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataBigQuerySource(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataBigQuerySource
  object.

  Fields:
    uri: The URI of a BigQuery table.
  """

  uri = _messages.StringField(1)


class GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataGcsSource(_messages.Message):
  r"""A GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataGcsSource
  object.

  Fields:
    uri: Cloud Storage URI of one or more files. Only CSV files are supported.
      The first line of the CSV file is used as the header. If there are
      multiple files, the header is the first line of the lexicographically
      first file, the other files must either contain the exact same header or
      omit the header.
  """

  uri = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataInputConfig(_messages.Message):
  r"""The time series Dataset's data source. The Dataset doesn't store the
  data directly, but only pointer(s) to its data.

  Fields:
    bigquerySource: A
      GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataBigQuerySour
      ce attribute.
    gcsSource: A
      GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataGcsSource
      attribute.
  """

  bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataBigQuerySource', 1)
  gcsSource = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTimeSeriesDatasetMetadataGcsSource', 2)


class GoogleCloudAiplatformV1alpha1SchemaVertex(_messages.Message):
  r"""A vertex represents a 2D point in the image. NOTE: the normalized vertex
  coordinates are relative to the original image and range from 0 to 1.

  Fields:
    x: X coordinate.
    y: Y coordinate.
  """

  x = _messages.FloatField(1)
  y = _messages.FloatField(2)


class GoogleCloudAiplatformV1alpha1SchemaVideoActionRecognitionAnnotation(_messages.Message):
  r"""Annotation details specific to video action recognition.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    timeSegment: This Annotation applies to the time period represented by the
      TimeSegment. If it's not set, the Annotation applies to the whole video.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  timeSegment = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTimeSegment', 3)


class GoogleCloudAiplatformV1alpha1SchemaVideoClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to video classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    timeSegment: This Annotation applies to the time period represented by the
      TimeSegment. If it's not set, the Annotation applies to the whole video.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  timeSegment = _messages.MessageField('GoogleCloudAiplatformV1alpha1SchemaTimeSegment', 3)


class GoogleCloudAiplatformV1alpha1SchemaVideoDataItem(_messages.Message):
  r"""Payload of Video DataItem.

  Fields:
    gcsUri: Required. Google Cloud Storage URI points to the original video in
      user's bucket. The video is up to 50 GB in size and up to 3 hour in
      duration.
    mimeType: Output only. The mime type of the content of the video. Only the
      videos in below listed mime types are supported. Supported mime_type: -
      video/mp4 - video/avi - video/quicktime
  """

  gcsUri = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaVideoDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Video DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Video DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1SchemaVideoObjectTrackingAnnotation(_messages.Message):
  r"""Annotation details specific to video object tracking.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    instanceId: The instance of the object, expressed as a positive integer.
      Used to track the same object across different frames.
    timeOffset: A time (frame) of a video to which this annotation pertains.
      Represented as the duration since the video's start.
    xMax: The rightmost coordinate of the bounding box.
    xMin: The leftmost coordinate of the bounding box.
    yMax: The bottommost coordinate of the bounding box.
    yMin: The topmost coordinate of the bounding box.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  instanceId = _messages.IntegerField(3)
  timeOffset = _messages.StringField(4)
  xMax = _messages.FloatField(5)
  xMin = _messages.FloatField(6)
  yMax = _messages.FloatField(7)
  yMin = _messages.FloatField(8)


class GoogleCloudAiplatformV1alpha1SchemaVisualInspectionClassificationLabelSavedQueryMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1alpha1SchemaVisualInspectionClassificationLabel
  SavedQueryMetadata object.

  Fields:
    multiLabel: Whether or not the classification label is multi_label.
  """

  multiLabel = _messages.BooleanField(1)


class GoogleCloudAiplatformV1alpha1SchemaVisualInspectionMaskSavedQueryMetadata(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1alpha1SchemaVisualInspectionMaskSavedQueryMetadata
  object.
  """



class GoogleCloudAiplatformV1alpha1SpecialistPool(_messages.Message):
  r"""SpecialistPool represents customers' own workforce to work on their data
  labeling jobs. It includes a group of specialist managers who are
  responsible for managing the labelers in this pool as well as customers'
  data labeling jobs associated with this pool. Customers create specialist
  pool as well as start data labeling jobs on Cloud, managers and labelers
  work with the jobs using CrowdCompute console.

  Fields:
    displayName: Required. The user-defined name of the SpecialistPool. The
      name can be up to 128 characters long and can be consist of any UTF-8
      characters. This field should be unique on project-level.
    name: Required. The resource name of the SpecialistPool.
    pendingDataLabelingJobs: Output only. The resource name of the pending
      data labeling jobs.
    specialistManagerEmails: The email addresses of the specialists in the
      SpecialistPool.
    specialistManagersCount: Output only. The number of Specialists in this
      SpecialistPool.
  """

  displayName = _messages.StringField(1)
  name = _messages.StringField(2)
  pendingDataLabelingJobs = _messages.StringField(3, repeated=True)
  specialistManagerEmails = _messages.StringField(4, repeated=True)
  specialistManagersCount = _messages.IntegerField(5, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1alpha1UndeployIndexOperationMetadata(_messages.Message):
  r"""Runtime operation information for IndexEndpointService.UndeployIndex.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1UndeployIndexResponse(_messages.Message):
  r"""Response message for IndexEndpointService.UndeployIndex."""


class GoogleCloudAiplatformV1alpha1UndeployModelOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.UndeployModel.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1UndeployModelResponse(_messages.Message):
  r"""Response message for EndpointService.UndeployModel."""


class GoogleCloudAiplatformV1alpha1UpdateIndexOperationMetadata(_messages.Message):
  r"""Runtime operation information for IndexService.UpdateIndex.

  Fields:
    genericMetadata: The operation generic information.
    nearestNeighborSearchOperationMetadata: The operation metadata with regard
      to ScaNN Index operation.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)
  nearestNeighborSearchOperationMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1NearestNeighborSearchOperationMetadata', 2)


class GoogleCloudAiplatformV1alpha1UpdateSpecialistPoolOperationMetadata(_messages.Message):
  r"""Runtime operation metadata for
  SpecialistPoolService.UpdateSpecialistPool.

  Fields:
    genericMetadata: The operation generic information.
    specialistPool: Output only. The name of the SpecialistPool to which the
      specialists are being added. Format: `projects/{project_id}/locations/{l
      ocation_id}/specialistPools/{specialist_pool}`
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)
  specialistPool = _messages.StringField(2)


class GoogleCloudAiplatformV1alpha1UploadModelOperationMetadata(_messages.Message):
  r"""Details of ModelService.UploadModel operation.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1alpha1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1alpha1UploadModelResponse(_messages.Message):
  r"""Response message of ModelService.UploadModel operation.

  Fields:
    model: The name of the uploaded Model resource. Format:
      `projects/{project}/locations/{location}/models/{model}`
  """

  model = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1AutomaticResources(_messages.Message):
  r"""A description of resources that to large degree are decided by AI
  Platform, and require only a modest additional configuration. Each Model
  supporting these resources documents its specific guidelines.

  Fields:
    maxReplicaCount: Immutable. The maximum number of replicas this
      DeployedModel may be deployed on when the traffic against it increases.
      If the requested value is too large, the deployment will error, but if
      deployment succeeds then the ability to scale the model to that many
      replicas is guaranteed (barring service outages). If traffic against the
      DeployedModel increases beyond what its replicas at maximum may handle,
      a portion of the traffic will be dropped. If this value is not provided,
      a no upper bound for scaling under heavy traffic will be assume, though
      AI Platform may be unable to scale beyond certain replica number.
    minReplicaCount: Immutable. The minimum number of replicas this
      DeployedModel will be always deployed on. If traffic against it
      increases, it may dynamically be deployed onto more replicas up to
      max_replica_count, and as traffic decreases, some of these extra
      replicas may be freed. If the requested value is too large, the
      deployment will error.
  """

  maxReplicaCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  minReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadata(_messages.Message):
  r"""Runtime operation information for
  MigrationService.BatchMigrateResources.

  Fields:
    genericMetadata: The common part of the operation metadata.
    partialResults: Partial results that reflect the latest migration
      operation progress.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)
  partialResults = _messages.MessageField('GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult', 2, repeated=True)


class GoogleCloudAiplatformV1beta1BatchMigrateResourcesOperationMetadataPartialResult(_messages.Message):
  r"""Represents a partial result in batch migration operation for one
  MigrateResourceRequest.

  Fields:
    dataset: Migrated dataset resource name.
    error: The error result of the migration request in case of failure.
    model: Migrated model resource name.
    request: It's the same as the value in
      MigrateResourceRequest.migrate_resource_requests.
  """

  dataset = _messages.StringField(1)
  error = _messages.MessageField('GoogleRpcStatus', 2)
  model = _messages.StringField(3)
  request = _messages.MessageField('GoogleCloudAiplatformV1beta1MigrateResourceRequest', 4)


class GoogleCloudAiplatformV1beta1BatchMigrateResourcesResponse(_messages.Message):
  r"""Response message for MigrationService.BatchMigrateResources.

  Fields:
    migrateResourceResponses: Successfully migrated resources.
  """

  migrateResourceResponses = _messages.MessageField('GoogleCloudAiplatformV1beta1MigrateResourceResponse', 1, repeated=True)


class GoogleCloudAiplatformV1beta1ContainerSpec(_messages.Message):
  r"""The spec of a Container.

  Fields:
    args: The arguments to be passed when starting the container.
    command: The command to be invoked when the container is started. It
      overrides the entrypoint instruction in Dockerfile when provided.
    imageUri: Required. The URI of a container image in the Container Registry
      that is to be run on each worker replica.
  """

  args = _messages.StringField(1, repeated=True)
  command = _messages.StringField(2, repeated=True)
  imageUri = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1CreateDatasetOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.CreateDataset.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1CreateEndpointOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.CreateEndpoint.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1CreateIndexEndpointOperationMetadata(_messages.Message):
  r"""Runtime operation information for
  IndexEndpointService.CreateIndexEndpoint.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1CreateIndexOperationMetadata(_messages.Message):
  r"""Runtime operation information for IndexService.CreateIndex.

  Fields:
    genericMetadata: The operation generic information.
    nearestNeighborSearchOperationMetadata: The operation metadata with regard
      to ScaNN Index operation.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)
  nearestNeighborSearchOperationMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata', 2)


class GoogleCloudAiplatformV1beta1CreateSpecialistPoolOperationMetadata(_messages.Message):
  r"""Runtime operation information for
  SpecialistPoolService.CreateSpecialistPool.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1CustomJobSpec(_messages.Message):
  r"""Represents the spec of a CustomJob.

  Fields:
    baseOutputDirectory: The Cloud Storage location to store the output of
      this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob,
      the baseOutputDirectory of each child CustomJob backing a Trial is set
      to a subdirectory of name id under its parent HyperparameterTuningJob's
      baseOutputDirectory. The following AI Platform environment variables
      will be passed to containers or python modules when this field is set:
      For CustomJob: * AIP_MODEL_DIR = `/model/` * AIP_CHECKPOINT_DIR =
      `/checkpoints/` * AIP_TENSORBOARD_LOG_DIR = `/logs/` For CustomJob
      backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = `//model/`
      * AIP_CHECKPOINT_DIR = `//checkpoints/` * AIP_TENSORBOARD_LOG_DIR =
      `//logs/`
    network: The full name of the Compute Engine
      [network](/compute/docs/networks-and-firewalls#networks) to which the
      Job should be peered. For example,
      `projects/12345/global/networks/myVPC`.
      [Format](/compute/docs/reference/rest/v1/networks/insert) is of the form
      `projects/{project}/global/networks/{network}`. Where {project} is a
      project number, as in `12345`, and {network} is a network name. Private
      services access must already be configured for the network. If left
      unspecified, the job is not peered with any network.
    scheduling: Scheduling options for a CustomJob.
    serviceAccount: Specifies the service account for workload run-as account.
      Users submitting jobs must have act-as permission on this run-as
      account. If unspecified, the AI Platform Custom Code Service Agent for
      the CustomJob's project is used.
    workerPoolSpecs: Required. The spec of the worker pools including machine
      type and Docker image. All worker pools except the first one are
      optional and can be skipped by providing an empty value.
  """

  baseOutputDirectory = _messages.MessageField('GoogleCloudAiplatformV1beta1GcsDestination', 1)
  network = _messages.StringField(2)
  scheduling = _messages.MessageField('GoogleCloudAiplatformV1beta1Scheduling', 3)
  serviceAccount = _messages.StringField(4)
  workerPoolSpecs = _messages.MessageField('GoogleCloudAiplatformV1beta1WorkerPoolSpec', 5, repeated=True)


class GoogleCloudAiplatformV1beta1DedicatedResources(_messages.Message):
  r"""A description of resources that are dedicated to a DeployedModel, and
  that need a higher degree of manual configuration.

  Fields:
    machineSpec: Required. Immutable. The specification of a single machine
      used by the prediction.
    maxReplicaCount: Immutable. The maximum number of replicas this
      DeployedModel may be deployed on when the traffic against it increases.
      If the requested value is too large, the deployment will error, but if
      deployment succeeds then the ability to scale the model to that many
      replicas is guaranteed (barring service outages). If traffic against the
      DeployedModel increases beyond what its replicas at maximum may handle,
      a portion of the traffic will be dropped. If this value is not provided,
      will use min_replica_count as the default value.
    minReplicaCount: Required. Immutable. The minimum number of machine
      replicas this DeployedModel will be always deployed on. If traffic
      against it increases, it may dynamically be deployed onto more replicas,
      and as traffic decreases, some of these extra replicas may be freed.
      Note: if machine_spec.accelerator_count is above 0, currently the model
      will be always deployed precisely on min_replica_count.
  """

  machineSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1MachineSpec', 1)
  maxReplicaCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  minReplicaCount = _messages.IntegerField(3, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1DeleteOperationMetadata(_messages.Message):
  r"""Details of operations that perform deletes of any entities.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1DeployIndexOperationMetadata(_messages.Message):
  r"""Runtime operation information for IndexEndpointService.DeployIndex.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1DeployIndexResponse(_messages.Message):
  r"""Response message for IndexEndpointService.DeployIndex.

  Fields:
    deployedIndex: The DeployedIndex that had been deployed in the
      IndexEndpoint.
  """

  deployedIndex = _messages.MessageField('GoogleCloudAiplatformV1beta1DeployedIndex', 1)


class GoogleCloudAiplatformV1beta1DeployModelOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.DeployModel.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1DeployModelResponse(_messages.Message):
  r"""Response message for EndpointService.DeployModel.

  Fields:
    deployedModel: The DeployedModel that had been deployed in the Endpoint.
  """

  deployedModel = _messages.MessageField('GoogleCloudAiplatformV1beta1DeployedModel', 1)


class GoogleCloudAiplatformV1beta1DeployedIndex(_messages.Message):
  r"""A deployment of an Index. IndexEndpoints contain one or more
  DeployedIndexes.

  Fields:
    automaticResources: Optional. A description of resources that the
      DeployedIndex uses, which to large degree are decided by AI Platform,
      and optionally allows only a modest additional configuration. If
      min_replica_count is not set, the default value is 1. If
      max_replica_count is not set, the default value is min_replica_count.
      The max allowed replica count is 1000. The user is billed for the
      resources (at least their minimal amount) even if the DeployedIndex
      receives no traffic.
    createTime: Output only. Timestamp when the DeployedIndex was created.
    displayName: The display name of the DeployedIndex. If not provided upon
      creation, the Index's display_name is used.
    id: Required. The user specified ID of the DeployedIndex. The ID can be up
      to 128 characters long and must start with a letter and only contain
      letters, numbers, and underscores. The ID must be unique within the
      project it is created in.
    index: Required. The name of the Index this is the deployment of. We may
      refer to this Index as the DeployedIndex's "original" Index.
    indexSyncTime: Output only. The DeployedIndex may depend on various data
      on its original Index. Additionally when certain changes to the original
      Index are being done (e.g. when what the Index contains is being
      changed) the DeployedIndex may be asynchronously updated in the
      background to reflect this changes. If this timestamp's value is at
      least the Index.update_time of the original Index, it means that this
      DeployedIndex and the original Index are in sync. If this timestamp is
      older, then to see which updates this DeployedIndex already contains
      (and which not), one must list Operations working on the original Index.
      Only the successfully completed Operations with
      Operations.metadata.generic_metadata.update_time equal or before this
      sync time are contained in this DeployedIndex.
    privateEndpoints: Output only. Provides paths for users to send requests
      directly to the deployed index services running on Cloud via private
      services access. This field is populated if network is configured.
  """

  automaticResources = _messages.MessageField('GoogleCloudAiplatformV1beta1AutomaticResources', 1)
  createTime = _messages.StringField(2)
  displayName = _messages.StringField(3)
  id = _messages.StringField(4)
  index = _messages.StringField(5)
  indexSyncTime = _messages.StringField(6)
  privateEndpoints = _messages.MessageField('GoogleCloudAiplatformV1beta1IndexPrivateEndpoints', 7)


class GoogleCloudAiplatformV1beta1DeployedModel(_messages.Message):
  r"""A deployment of a Model. Endpoints contain one or more DeployedModels.

  Fields:
    automaticResources: A description of resources that to large degree are
      decided by AI Platform, and require only a modest additional
      configuration.
    createTime: Output only. Timestamp when the DeployedModel was created.
    dedicatedResources: A description of resources that are dedicated to the
      DeployedModel, and that need a higher degree of manual configuration.
    displayName: The display name of the DeployedModel. If not provided upon
      creation, the Model's display_name is used.
    enableAccessLogging: These logs are like standard server access logs,
      containing information like timestamp and latency for each prediction
      request. Note that Stackdriver logs may incur a cost, especially if your
      project receives prediction requests at a high queries per second rate
      (QPS). Estimate your costs before enabling this option.
    enableContainerLogging: If true, the container of the DeployedModel
      instances will send `stderr` and `stdout` streams to Stackdriver
      Logging. Only supported for custom-trained Models and AutoML Tabular
      Models.
    explanationSpec: Explanation configuration for this DeployedModel. When
      deploying a Model using EndpointService.DeployModel, this value
      overrides the value of Model.explanation_spec. All fields of
      explanation_spec are optional in the request. If a field of
      explanation_spec is not populated, the value of the same field of
      Model.explanation_spec is inherited. If the corresponding
      Model.explanation_spec is not populated, all fields of the
      explanation_spec will be used for the explanation configuration.
    id: Output only. The ID of the DeployedModel.
    model: Required. The name of the Model that this is the deployment of.
      Note that the Model may be in a different location than the
      DeployedModel's Endpoint.
    serviceAccount: The service account that the DeployedModel's container
      runs as. Specify the email address of the service account. If this
      service account is not specified, the container runs as a service
      account that doesn't have access to the resource project. Users
      deploying the Model must have the `iam.serviceAccounts.actAs` permission
      on this service account.
  """

  automaticResources = _messages.MessageField('GoogleCloudAiplatformV1beta1AutomaticResources', 1)
  createTime = _messages.StringField(2)
  dedicatedResources = _messages.MessageField('GoogleCloudAiplatformV1beta1DedicatedResources', 3)
  displayName = _messages.StringField(4)
  enableAccessLogging = _messages.BooleanField(5)
  enableContainerLogging = _messages.BooleanField(6)
  explanationSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1ExplanationSpec', 7)
  id = _messages.StringField(8)
  model = _messages.StringField(9)
  serviceAccount = _messages.StringField(10)


class GoogleCloudAiplatformV1beta1DiskSpec(_messages.Message):
  r"""Represents the spec of disk options.

  Fields:
    bootDiskSizeGb: Size in GB of the boot disk (default is 100GB).
    bootDiskType: Type of the boot disk (default is "pd-ssd"). Valid values:
      "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard"
      (Persistent Disk Hard Disk Drive).
  """

  bootDiskSizeGb = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  bootDiskType = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1ExplanationMetadata(_messages.Message):
  r"""Metadata describing the Model's input and output for explanation.

  Messages:
    InputsValue: Required. Map from feature names to feature input metadata.
      Keys are the name of the features. Values are the specification of the
      feature. An empty InputMetadata is valid. It describes a text feature
      which has the name specified as the key in ExplanationMetadata.inputs.
      The baseline of the empty feature is chosen by AI Platform. For AI
      Platform provided Tensorflow images, the key can be any friendly name of
      the feature. Once specified, featureAttributions are keyed by this key
      (if not grouped with another feature). For custom images, the key must
      match with the key in instance.
    OutputsValue: Required. Map from output names to output metadata. For AI
      Platform provided Tensorflow images, keys can be any string user
      defines. For custom images, keys are the name of the output field in the
      prediction to be explained. Currently only one key is allowed.

  Fields:
    featureAttributionsSchemaUri: Points to a YAML file stored on Google Cloud
      Storage describing the format of the feature attributions. The schema is
      defined as an OpenAPI 3.0.2 [Schema
      Object](https://tinyurl.com/y538mdwt#schema-object). AutoML tabular
      Models always have this field populated by AI Platform. Note: The URI
      given on output may be different, including the URI scheme, than the one
      given on input. The output URI will point to a location where the user
      only has a read access.
    inputs: Required. Map from feature names to feature input metadata. Keys
      are the name of the features. Values are the specification of the
      feature. An empty InputMetadata is valid. It describes a text feature
      which has the name specified as the key in ExplanationMetadata.inputs.
      The baseline of the empty feature is chosen by AI Platform. For AI
      Platform provided Tensorflow images, the key can be any friendly name of
      the feature. Once specified, featureAttributions are keyed by this key
      (if not grouped with another feature). For custom images, the key must
      match with the key in instance.
    outputs: Required. Map from output names to output metadata. For AI
      Platform provided Tensorflow images, keys can be any string user
      defines. For custom images, keys are the name of the output field in the
      prediction to be explained. Currently only one key is allowed.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class InputsValue(_messages.Message):
    r"""Required. Map from feature names to feature input metadata. Keys are
    the name of the features. Values are the specification of the feature. An
    empty InputMetadata is valid. It describes a text feature which has the
    name specified as the key in ExplanationMetadata.inputs. The baseline of
    the empty feature is chosen by AI Platform. For AI Platform provided
    Tensorflow images, the key can be any friendly name of the feature. Once
    specified, featureAttributions are keyed by this key (if not grouped with
    another feature). For custom images, the key must match with the key in
    instance.

    Messages:
      AdditionalProperty: An additional property for a InputsValue object.

    Fields:
      additionalProperties: Additional properties of type InputsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a InputsValue object.

      Fields:
        key: Name of the additional property.
        value: A GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadata
          attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadata', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  @encoding.MapUnrecognizedFields('additionalProperties')
  class OutputsValue(_messages.Message):
    r"""Required. Map from output names to output metadata. For AI Platform
    provided Tensorflow images, keys can be any string user defines. For
    custom images, keys are the name of the output field in the prediction to
    be explained. Currently only one key is allowed.

    Messages:
      AdditionalProperty: An additional property for a OutputsValue object.

    Fields:
      additionalProperties: Additional properties of type OutputsValue
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a OutputsValue object.

      Fields:
        key: Name of the additional property.
        value: A GoogleCloudAiplatformV1beta1ExplanationMetadataOutputMetadata
          attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('GoogleCloudAiplatformV1beta1ExplanationMetadataOutputMetadata', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  featureAttributionsSchemaUri = _messages.StringField(1)
  inputs = _messages.MessageField('InputsValue', 2)
  outputs = _messages.MessageField('OutputsValue', 3)


class GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadata(_messages.Message):
  r"""Metadata of the input of a feature. Fields other than
  InputMetadata.input_baselines are applicable only for Models that are using
  AI Platform-provided images for Tensorflow.

  Enums:
    EncodingValueValuesEnum: Defines how the feature is encoded into the input
      tensor. Defaults to IDENTITY.

  Fields:
    denseShapeTensorName: Specifies the shape of the values of the input if
      the input is a sparse representation. Refer to Tensorflow documentation
      for more details:
      https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
    encodedBaselines: A list of baselines for the encoded tensor. The shape of
      each baseline should match the shape of the encoded tensor. If a scalar
      is provided, AI Platform broadcast to the same shape as the encoded
      tensor.
    encodedTensorName: Encoded tensor is a transformation of the input tensor.
      Must be provided if choosing Integrated Gradients attribution or XRAI
      attribution and the input tensor is not differentiable. An encoded
      tensor is generated if the input tensor is encoded by a lookup table.
    encoding: Defines how the feature is encoded into the input tensor.
      Defaults to IDENTITY.
    featureValueDomain: The domain details of the input feature value. Like
      min/max, original mean or standard deviation if normalized.
    groupName: Name of the group that the input belongs to. Features with the
      same group name will be treated as one feature when computing
      attributions. Features grouped together can have different shapes in
      value. If provided, there will be one single attribution generated in
      featureAttributions, keyed by the group name.
    indexFeatureMapping: A list of feature names for each index in the input
      tensor. Required when the input InputMetadata.encoding is
      BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
    indicesTensorName: Specifies the index of the values of the input tensor.
      Required when the input tensor is a sparse representation. Refer to
      Tensorflow documentation for more details:
      https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
    inputBaselines: Baseline inputs for this feature. If no baseline is
      specified, AI Platform chooses the baseline for this feature. If
      multiple baselines are specified, AI Platform returns the average
      attributions across them in Attributions.baseline_attribution. For AI
      Platform provided Tensorflow images (both 1.x and 2.x), the shape of
      each baseline must match the shape of the input tensor. If a scalar is
      provided, we broadcast to the same shape as the input tensor. For custom
      images, the element of the baselines must be in the same format as the
      feature's input in the instance[]. The schema of any single instance may
      be specified via Endpoint's DeployedModels' Model's PredictSchemata's
      instance_schema_uri.
    inputTensorName: Name of the input tensor for this feature. Required and
      is only applicable to AI Platform provided images for Tensorflow.
    modality: Modality of the feature. Valid values are: numeric, image.
      Defaults to numeric.
    visualization: Visualization configurations for image explanation.
  """

  class EncodingValueValuesEnum(_messages.Enum):
    r"""Defines how the feature is encoded into the input tensor. Defaults to
    IDENTITY.

    Values:
      ENCODING_UNSPECIFIED: Default value. This is the same as IDENTITY.
      IDENTITY: The tensor represents one feature.
      BAG_OF_FEATURES: The tensor represents a bag of features where each
        index maps to a feature. InputMetadata.index_feature_mapping must be
        provided for this encoding. For example: ``` input = [27, 6.0, 150]
        index_feature_mapping = ["age", "height", "weight"] ```
      BAG_OF_FEATURES_SPARSE: The tensor represents a bag of features where
        each index maps to a feature. Zero values in the tensor indicates
        feature being non-existent. InputMetadata.index_feature_mapping must
        be provided for this encoding. For example: ``` input = [2, 0, 5, 0,
        1] index_feature_mapping = ["a", "b", "c", "d", "e"] ```
      INDICATOR: The tensor is a list of binaries representing whether a
        feature exists or not (1 indicates existence).
        InputMetadata.index_feature_mapping must be provided for this
        encoding. For example: ``` input = [1, 0, 1, 0, 1]
        index_feature_mapping = ["a", "b", "c", "d", "e"] ```
      COMBINED_EMBEDDING: The tensor is encoded into a 1-dimensional array
        represented by an encoded tensor. InputMetadata.encoded_tensor_name
        must be provided for this encoding. For example: ``` input = ["This",
        "is", "a", "test", "."] encoded = [0.1, 0.2, 0.3, 0.4, 0.5] ```
      CONCAT_EMBEDDING: Select this encoding when the input tensor is encoded
        into a 2-dimensional array represented by an encoded tensor.
        InputMetadata.encoded_tensor_name must be provided for this encoding.
        The first dimension of the encoded tensor's shape is the same as the
        input tensor's shape. For example: ``` input = ["This", "is", "a",
        "test", "."] encoded = [[0.1, 0.2, 0.3, 0.4, 0.5], [0.2, 0.1, 0.4,
        0.3, 0.5], [0.5, 0.1, 0.3, 0.5, 0.4], [0.5, 0.3, 0.1, 0.2, 0.4], [0.4,
        0.3, 0.2, 0.5, 0.1]] ```
    """
    ENCODING_UNSPECIFIED = 0
    IDENTITY = 1
    BAG_OF_FEATURES = 2
    BAG_OF_FEATURES_SPARSE = 3
    INDICATOR = 4
    COMBINED_EMBEDDING = 5
    CONCAT_EMBEDDING = 6

  denseShapeTensorName = _messages.StringField(1)
  encodedBaselines = _messages.MessageField('extra_types.JsonValue', 2, repeated=True)
  encodedTensorName = _messages.StringField(3)
  encoding = _messages.EnumField('EncodingValueValuesEnum', 4)
  featureValueDomain = _messages.MessageField('GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataFeatureValueDomain', 5)
  groupName = _messages.StringField(6)
  indexFeatureMapping = _messages.StringField(7, repeated=True)
  indicesTensorName = _messages.StringField(8)
  inputBaselines = _messages.MessageField('extra_types.JsonValue', 9, repeated=True)
  inputTensorName = _messages.StringField(10)
  modality = _messages.StringField(11)
  visualization = _messages.MessageField('GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataVisualization', 12)


class GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataFeatureValueDomain(_messages.Message):
  r"""Domain details of the input feature value. Provides numeric information
  about the feature, such as its range (min, max). If the feature has been
  pre-processed, for example with z-scoring, then it provides information
  about how to recover the original feature. For example, if the input feature
  is an image and it has been pre-processed to obtain 0-mean and stddev = 1
  values, then original_mean, and original_stddev refer to the mean and stddev
  of the original feature (e.g. image tensor) from which input feature (with
  mean = 0 and stddev = 1) was obtained.

  Fields:
    maxValue: The maximum permissible value for this feature.
    minValue: The minimum permissible value for this feature.
    originalMean: If this input feature has been normalized to a mean value of
      0, the original_mean specifies the mean value of the domain prior to
      normalization.
    originalStddev: If this input feature has been normalized to a standard
      deviation of 1.0, the original_stddev specifies the standard deviation
      of the domain prior to normalization.
  """

  maxValue = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  minValue = _messages.FloatField(2, variant=_messages.Variant.FLOAT)
  originalMean = _messages.FloatField(3, variant=_messages.Variant.FLOAT)
  originalStddev = _messages.FloatField(4, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataVisualization(_messages.Message):
  r"""Visualization configurations for image explanation.

  Enums:
    ColorMapValueValuesEnum: The color scheme used for the highlighted areas.
      Defaults to PINK_GREEN for Integrated Gradients attribution, which shows
      positive attributions in green and negative in pink. Defaults to VIRIDIS
      for XRAI attribution, which highlights the most influential regions in
      yellow and the least influential in blue.
    OverlayTypeValueValuesEnum: How the original image is displayed in the
      visualization. Adjusting the overlay can help increase visual clarity if
      the original image makes it difficult to view the visualization.
      Defaults to NONE.
    PolarityValueValuesEnum: Whether to only highlight pixels with positive
      contributions, negative or both. Defaults to POSITIVE.
    TypeValueValuesEnum: Type of the image visualization. Only applicable to
      Integrated Gradients attribution. OUTLINES shows regions of attribution,
      while PIXELS shows per-pixel attribution. Defaults to OUTLINES.

  Fields:
    clipPercentLowerbound: Excludes attributions below the specified
      percentile, from the highlighted areas. Defaults to 35.
    clipPercentUpperbound: Excludes attributions above the specified
      percentile from the highlighted areas. Using the clip_percent_upperbound
      and clip_percent_lowerbound together can be useful for filtering out
      noise and making it easier to see areas of strong attribution. Defaults
      to 99.9.
    colorMap: The color scheme used for the highlighted areas. Defaults to
      PINK_GREEN for Integrated Gradients attribution, which shows positive
      attributions in green and negative in pink. Defaults to VIRIDIS for XRAI
      attribution, which highlights the most influential regions in yellow and
      the least influential in blue.
    overlayType: How the original image is displayed in the visualization.
      Adjusting the overlay can help increase visual clarity if the original
      image makes it difficult to view the visualization. Defaults to NONE.
    polarity: Whether to only highlight pixels with positive contributions,
      negative or both. Defaults to POSITIVE.
    type: Type of the image visualization. Only applicable to Integrated
      Gradients attribution. OUTLINES shows regions of attribution, while
      PIXELS shows per-pixel attribution. Defaults to OUTLINES.
  """

  class ColorMapValueValuesEnum(_messages.Enum):
    r"""The color scheme used for the highlighted areas. Defaults to
    PINK_GREEN for Integrated Gradients attribution, which shows positive
    attributions in green and negative in pink. Defaults to VIRIDIS for XRAI
    attribution, which highlights the most influential regions in yellow and
    the least influential in blue.

    Values:
      COLOR_MAP_UNSPECIFIED: Should not be used.
      PINK_GREEN: Positive: green. Negative: pink.
      VIRIDIS: Viridis color map: A perceptually uniform color mapping which
        is easier to see by those with colorblindness and progresses from
        yellow to green to blue. Positive: yellow. Negative: blue.
      RED: Positive: red. Negative: red.
      GREEN: Positive: green. Negative: green.
      RED_GREEN: Positive: green. Negative: red.
      PINK_WHITE_GREEN: PiYG palette.
    """
    COLOR_MAP_UNSPECIFIED = 0
    PINK_GREEN = 1
    VIRIDIS = 2
    RED = 3
    GREEN = 4
    RED_GREEN = 5
    PINK_WHITE_GREEN = 6

  class OverlayTypeValueValuesEnum(_messages.Enum):
    r"""How the original image is displayed in the visualization. Adjusting
    the overlay can help increase visual clarity if the original image makes
    it difficult to view the visualization. Defaults to NONE.

    Values:
      OVERLAY_TYPE_UNSPECIFIED: Default value. This is the same as NONE.
      NONE: No overlay.
      ORIGINAL: The attributions are shown on top of the original image.
      GRAYSCALE: The attributions are shown on top of grayscaled version of
        the original image.
      MASK_BLACK: The attributions are used as a mask to reveal predictive
        parts of the image and hide the un-predictive parts.
    """
    OVERLAY_TYPE_UNSPECIFIED = 0
    NONE = 1
    ORIGINAL = 2
    GRAYSCALE = 3
    MASK_BLACK = 4

  class PolarityValueValuesEnum(_messages.Enum):
    r"""Whether to only highlight pixels with positive contributions, negative
    or both. Defaults to POSITIVE.

    Values:
      POLARITY_UNSPECIFIED: Default value. This is the same as POSITIVE.
      POSITIVE: Highlights the pixels/outlines that were most influential to
        the model's prediction.
      NEGATIVE: Setting polarity to negative highlights areas that does not
        lead to the models's current prediction.
      BOTH: Shows both positive and negative attributions.
    """
    POLARITY_UNSPECIFIED = 0
    POSITIVE = 1
    NEGATIVE = 2
    BOTH = 3

  class TypeValueValuesEnum(_messages.Enum):
    r"""Type of the image visualization. Only applicable to Integrated
    Gradients attribution. OUTLINES shows regions of attribution, while PIXELS
    shows per-pixel attribution. Defaults to OUTLINES.

    Values:
      TYPE_UNSPECIFIED: Should not be used.
      PIXELS: Shows which pixel contributed to the image prediction.
      OUTLINES: Shows which region contributed to the image prediction by
        outlining the region.
    """
    TYPE_UNSPECIFIED = 0
    PIXELS = 1
    OUTLINES = 2

  clipPercentLowerbound = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  clipPercentUpperbound = _messages.FloatField(2, variant=_messages.Variant.FLOAT)
  colorMap = _messages.EnumField('ColorMapValueValuesEnum', 3)
  overlayType = _messages.EnumField('OverlayTypeValueValuesEnum', 4)
  polarity = _messages.EnumField('PolarityValueValuesEnum', 5)
  type = _messages.EnumField('TypeValueValuesEnum', 6)


class GoogleCloudAiplatformV1beta1ExplanationMetadataOutputMetadata(_messages.Message):
  r"""Metadata of the prediction output to be explained.

  Fields:
    displayNameMappingKey: Specify a field name in the prediction to look for
      the display name. Use this if the prediction contains the display names
      for the outputs. The display names in the prediction must have the same
      shape of the outputs, so that it can be located by
      Attribution.output_index for a specific output.
    indexDisplayNameMapping: Static mapping between the index and display
      name. Use this if the outputs are a deterministic n-dimensional array,
      e.g. a list of scores of all the classes in a pre-defined order for a
      multi-classification Model. It's not feasible if the outputs are non-
      deterministic, e.g. the Model produces top-k classes or sort the outputs
      by their values. The shape of the value must be an n-dimensional array
      of strings. The number of dimensions must match that of the outputs to
      be explained. The Attribution.output_display_name is populated by
      locating in the mapping with Attribution.output_index.
    outputTensorName: Name of the output tensor. Required and is only
      applicable to AI Platform provided images for Tensorflow.
  """

  displayNameMappingKey = _messages.StringField(1)
  indexDisplayNameMapping = _messages.MessageField('extra_types.JsonValue', 2)
  outputTensorName = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1ExplanationParameters(_messages.Message):
  r"""Parameters to configure explaining for Model's predictions.

  Fields:
    integratedGradientsAttribution: An attribution method that computes
      Aumann-Shapley values taking advantage of the model's fully
      differentiable structure. Refer to this paper for more details:
      https://arxiv.org/abs/1703.01365
    outputIndices: If populated, only returns attributions that have
      output_index contained in output_indices. It must be an ndarray of
      integers, with the same shape of the output it's explaining. If not
      populated, returns attributions for top_k indices of outputs. If neither
      top_k nor output_indeices is populated, returns the argmax index of the
      outputs. Only applicable to Models that predict multiple outputs (e,g,
      multi-class Models that predict multiple classes).
    sampledShapleyAttribution: An attribution method that approximates Shapley
      values for features that contribute to the label being predicted. A
      sampling strategy is used to approximate the value rather than
      considering all subsets of features. Refer to this paper for model
      details: https://arxiv.org/abs/1306.4265.
    topK: If populated, returns attributions for top K indices of outputs
      (defaults to 1). Only applies to Models that predicts more than one
      outputs (e,g, multi-class Models). When set to -1, returns explanations
      for all outputs.
    xraiAttribution: An attribution method that redistributes Integrated
      Gradients attribution to segmented regions, taking advantage of the
      model's fully differentiable structure. Refer to this paper for more
      details: https://arxiv.org/abs/1906.02825 XRAI currently performs better
      on natural images, like a picture of a house or an animal. If the images
      are taken in artificial environments, like a lab or manufacturing line,
      or from diagnostic equipment, like x-rays or quality-control cameras,
      use Integrated Gradients instead.
  """

  integratedGradientsAttribution = _messages.MessageField('GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution', 1)
  outputIndices = _messages.MessageField('extra_types.JsonValue', 2, repeated=True)
  sampledShapleyAttribution = _messages.MessageField('GoogleCloudAiplatformV1beta1SampledShapleyAttribution', 3)
  topK = _messages.IntegerField(4, variant=_messages.Variant.INT32)
  xraiAttribution = _messages.MessageField('GoogleCloudAiplatformV1beta1XraiAttribution', 5)


class GoogleCloudAiplatformV1beta1ExplanationSpec(_messages.Message):
  r"""Specification of Model explanation.

  Fields:
    metadata: Required. Metadata describing the Model's input and output for
      explanation.
    parameters: Required. Parameters that configure explaining of the Model's
      predictions.
  """

  metadata = _messages.MessageField('GoogleCloudAiplatformV1beta1ExplanationMetadata', 1)
  parameters = _messages.MessageField('GoogleCloudAiplatformV1beta1ExplanationParameters', 2)


class GoogleCloudAiplatformV1beta1ExportDataOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.ExportData.

  Fields:
    gcsOutputDirectory: A Google Cloud Storage directory which path ends with
      '/'. The exported data is stored in the directory.
    genericMetadata: The common part of the operation metadata.
  """

  gcsOutputDirectory = _messages.StringField(1)
  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 2)


class GoogleCloudAiplatformV1beta1ExportDataResponse(_messages.Message):
  r"""Response message for DatasetService.ExportData.

  Fields:
    exportedFiles: All of the files that are exported in this export
      operation.
  """

  exportedFiles = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1beta1ExportModelOperationMetadata(_messages.Message):
  r"""Details of ModelService.ExportModel operation.

  Fields:
    genericMetadata: The common part of the operation metadata.
    outputInfo: Output only. Information further describing the output of this
      Model export.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)
  outputInfo = _messages.MessageField('GoogleCloudAiplatformV1beta1ExportModelOperationMetadataOutputInfo', 2)


class GoogleCloudAiplatformV1beta1ExportModelOperationMetadataOutputInfo(_messages.Message):
  r"""Further describes the output of the ExportModel. Supplements
  ExportModelRequest.OutputConfig.

  Fields:
    artifactOutputUri: Output only. If the Model artifact is being exported to
      Google Cloud Storage this is the full path of the directory created,
      into which the Model files are being written to.
    imageOutputUri: Output only. If the Model image is being exported to
      Google Container Registry or Artifact Registry this is the full path of
      the image created.
  """

  artifactOutputUri = _messages.StringField(1)
  imageOutputUri = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1ExportModelResponse(_messages.Message):
  r"""Response message of ModelService.ExportModel operation."""


class GoogleCloudAiplatformV1beta1FeatureNoiseSigma(_messages.Message):
  r"""Noise sigma by features. Noise sigma represents the standard deviation
  of the gaussian kernel that will be used to add noise to interpolated inputs
  prior to computing gradients.

  Fields:
    noiseSigma: Noise sigma per feature. No noise is added to features that
      are not set.
  """

  noiseSigma = _messages.MessageField('GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature', 1, repeated=True)


class GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature(_messages.Message):
  r"""Noise sigma for a single feature.

  Fields:
    name: The name of the input feature for which noise sigma is provided. The
      features are defined in explanation metadata inputs.
    sigma: This represents the standard deviation of the Gaussian kernel that
      will be used to add noise to the feature prior to computing gradients.
      Similar to noise_sigma but represents the noise added to the current
      feature. Defaults to 0.1.
  """

  name = _messages.StringField(1)
  sigma = _messages.FloatField(2, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1beta1GcsDestination(_messages.Message):
  r"""The Google Cloud Storage location where the output is to be written to.

  Fields:
    outputUriPrefix: Required. Google Cloud Storage URI to output directory.
      If the uri doesn't end with '/', a '/' will be automatically appended.
      The directory is created if it doesn't exist.
  """

  outputUriPrefix = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1GenericOperationMetadata(_messages.Message):
  r"""Generic Metadata shared by all operations.

  Fields:
    createTime: Output only. Time when the operation was created.
    partialFailures: Output only. Partial failures encountered. E.g. single
      files that couldn't be read. This field should never exceed 20 entries.
      Status details field will contain standard GCP error details.
    updateTime: Output only. Time when the operation was updated for the last
      time. If the operation has finished (successfully or not), this is the
      finish time.
  """

  createTime = _messages.StringField(1)
  partialFailures = _messages.MessageField('GoogleRpcStatus', 2, repeated=True)
  updateTime = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1ImportDataOperationMetadata(_messages.Message):
  r"""Runtime operation information for DatasetService.ImportData.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1ImportDataResponse(_messages.Message):
  r"""Response message for DatasetService.ImportData."""


class GoogleCloudAiplatformV1beta1IndexPrivateEndpoints(_messages.Message):
  r"""IndexPrivateEndpoints proto is used to provide paths for users to send
  requests via private services access.

  Fields:
    matchGrpcAddress: Output only. The ip address used to send match gRPC
      requests.
  """

  matchGrpcAddress = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution(_messages.Message):
  r"""An attribution method that computes the Aumann-Shapley value taking
  advantage of the model's fully differentiable structure. Refer to this paper
  for more details: https://arxiv.org/abs/1703.01365

  Fields:
    smoothGradConfig: Config for SmoothGrad approximation of gradients. When
      enabled, the gradients are approximated by averaging the gradients from
      noisy samples in the vicinity of the inputs. Adding noise can help
      improve the computed gradients. Refer to this paper for more details:
      https://arxiv.org/pdf/1706.03825.pdf
    stepCount: Required. The number of steps for approximating the path
      integral. A good value to start is 50 and gradually increase until the
      sum to diff property is within the desired error range. Valid range of
      its value is [1, 100], inclusively.
  """

  smoothGradConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1SmoothGradConfig', 1)
  stepCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1MachineSpec(_messages.Message):
  r"""Specification of a single machine.

  Enums:
    AcceleratorTypeValueValuesEnum: Immutable. The type of accelerator(s) that
      may be attached to the machine as per accelerator_count.

  Fields:
    acceleratorCount: The number of accelerators to attach to the machine.
    acceleratorType: Immutable. The type of accelerator(s) that may be
      attached to the machine as per accelerator_count.
    machineType: Immutable. The type of the machine. For the machine types
      supported for prediction, see https://tinyurl.com/aip-
      docs/predictions/machine-types. For machine types supported for creating
      a custom training job, see https://tinyurl.com/aip-
      docs/training/configure-compute. For DeployedModel this field is
      optional, and the default value is `n1-standard-2`. For
      BatchPredictionJob or as part of WorkerPoolSpec this field is required.
  """

  class AcceleratorTypeValueValuesEnum(_messages.Enum):
    r"""Immutable. The type of accelerator(s) that may be attached to the
    machine as per accelerator_count.

    Values:
      ACCELERATOR_TYPE_UNSPECIFIED: Unspecified accelerator type, which means
        no accelerator.
      NVIDIA_TESLA_K80: Nvidia Tesla K80 GPU.
      NVIDIA_TESLA_P100: Nvidia Tesla P100 GPU.
      NVIDIA_TESLA_V100: Nvidia Tesla V100 GPU.
      NVIDIA_TESLA_P4: Nvidia Tesla P4 GPU.
      NVIDIA_TESLA_T4: Nvidia Tesla T4 GPU.
      TPU_V2: TPU v2.
      TPU_V3: TPU v3.
    """
    ACCELERATOR_TYPE_UNSPECIFIED = 0
    NVIDIA_TESLA_K80 = 1
    NVIDIA_TESLA_P100 = 2
    NVIDIA_TESLA_V100 = 3
    NVIDIA_TESLA_P4 = 4
    NVIDIA_TESLA_T4 = 5
    TPU_V2 = 6
    TPU_V3 = 7

  acceleratorCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  acceleratorType = _messages.EnumField('AcceleratorTypeValueValuesEnum', 2)
  machineType = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1MigratableResource(_messages.Message):
  r"""Represents one resource that exists in automl.googleapis.com,
  datalabeling.googleapis.com or ml.googleapis.com.

  Fields:
    automlDataset: Output only. Represents one Dataset in
      automl.googleapis.com.
    automlModel: Output only. Represents one Model in automl.googleapis.com.
    dataLabelingDataset: Output only. Represents one Dataset in
      datalabeling.googleapis.com.
    lastMigrateTime: Output only. Timestamp when the last migration attempt on
      this MigratableResource started. Will not be set if there's no migration
      attempt on this MigratableResource.
    lastUpdateTime: Output only. Timestamp when this MigratableResource was
      last updated.
    mlEngineModelVersion: Output only. Represents one Version in
      ml.googleapis.com.
  """

  automlDataset = _messages.MessageField('GoogleCloudAiplatformV1beta1MigratableResourceAutomlDataset', 1)
  automlModel = _messages.MessageField('GoogleCloudAiplatformV1beta1MigratableResourceAutomlModel', 2)
  dataLabelingDataset = _messages.MessageField('GoogleCloudAiplatformV1beta1MigratableResourceDataLabelingDataset', 3)
  lastMigrateTime = _messages.StringField(4)
  lastUpdateTime = _messages.StringField(5)
  mlEngineModelVersion = _messages.MessageField('GoogleCloudAiplatformV1beta1MigratableResourceMlEngineModelVersion', 6)


class GoogleCloudAiplatformV1beta1MigratableResourceAutomlDataset(_messages.Message):
  r"""Represents one Dataset in automl.googleapis.com.

  Fields:
    dataset: Full resource name of automl Dataset. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`.
    datasetDisplayName: The Dataset's display name in automl.googleapis.com.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1MigratableResourceAutomlModel(_messages.Message):
  r"""Represents one Model in automl.googleapis.com.

  Fields:
    model: Full resource name of automl Model. Format:
      `projects/{project}/locations/{location}/models/{model}`.
    modelDisplayName: The Model's display name in automl.googleapis.com.
  """

  model = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1MigratableResourceDataLabelingDataset(_messages.Message):
  r"""Represents one Dataset in datalabeling.googleapis.com.

  Fields:
    dataLabelingAnnotatedDatasets: The migratable AnnotatedDataset in
      datalabeling.googleapis.com belongs to the data labeling Dataset.
    dataset: Full resource name of data labeling Dataset. Format:
      `projects/{project}/datasets/{dataset}`.
    datasetDisplayName: The Dataset's display name in
      datalabeling.googleapis.com.
  """

  dataLabelingAnnotatedDatasets = _messages.MessageField('GoogleCloudAiplatformV1beta1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset', 1, repeated=True)
  dataset = _messages.StringField(2)
  datasetDisplayName = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1MigratableResourceDataLabelingDatasetDataLabelingAnnotatedDataset(_messages.Message):
  r"""Represents one AnnotatedDataset in datalabeling.googleapis.com.

  Fields:
    annotatedDataset: Full resource name of data labeling AnnotatedDataset.
      Format: `projects/{project}/datasets/{dataset}/annotatedDatasets/{annota
      ted_dataset}`.
    annotatedDatasetDisplayName: The AnnotatedDataset's display name in
      datalabeling.googleapis.com.
  """

  annotatedDataset = _messages.StringField(1)
  annotatedDatasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1MigratableResourceMlEngineModelVersion(_messages.Message):
  r"""Represents one model Version in ml.googleapis.com.

  Fields:
    endpoint: The ml.googleapis.com endpoint that this model Version currently
      lives in. Example values: * ml.googleapis.com * us-centrall-
      ml.googleapis.com * europe-west4-ml.googleapis.com * asia-
      east1-ml.googleapis.com
    version: Full resource name of ml engine model Version. Format:
      `projects/{project}/models/{model}/versions/{version}`.
  """

  endpoint = _messages.StringField(1)
  version = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1MigrateResourceRequest(_messages.Message):
  r"""Config of migrating one resource from automl.googleapis.com,
  datalabeling.googleapis.com and ml.googleapis.com to AI Platform.

  Fields:
    migrateAutomlDatasetConfig: Config for migrating Dataset in
      automl.googleapis.com to AI Platform's Dataset.
    migrateAutomlModelConfig: Config for migrating Model in
      automl.googleapis.com to AI Platform's Model.
    migrateDataLabelingDatasetConfig: Config for migrating Dataset in
      datalabeling.googleapis.com to AI Platform's Dataset.
    migrateMlEngineModelVersionConfig: Config for migrating Version in
      ml.googleapis.com to AI Platform's Model.
  """

  migrateAutomlDatasetConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateAutomlDatasetConfig', 1)
  migrateAutomlModelConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateAutomlModelConfig', 2)
  migrateDataLabelingDatasetConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateDataLabelingDatasetConfig', 3)
  migrateMlEngineModelVersionConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateMlEngineModelVersionConfig', 4)


class GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateAutomlDatasetConfig(_messages.Message):
  r"""Config for migrating Dataset in automl.googleapis.com to AI Platform's
  Dataset.

  Fields:
    dataset: Required. Full resource name of automl Dataset. Format:
      `projects/{project}/locations/{location}/datasets/{dataset}`.
    datasetDisplayName: Required. Display name of the Dataset in AI Platform.
      System will pick a display name if unspecified.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateAutomlModelConfig(_messages.Message):
  r"""Config for migrating Model in automl.googleapis.com to AI Platform's
  Model.

  Fields:
    model: Required. Full resource name of automl Model. Format:
      `projects/{project}/locations/{location}/models/{model}`.
    modelDisplayName: Optional. Display name of the model in AI Platform.
      System will pick a display name if unspecified.
  """

  model = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateDataLabelingDatasetConfig(_messages.Message):
  r"""Config for migrating Dataset in datalabeling.googleapis.com to AI
  Platform's Dataset.

  Fields:
    dataset: Required. Full resource name of data labeling Dataset. Format:
      `projects/{project}/datasets/{dataset}`.
    datasetDisplayName: Optional. Display name of the Dataset in AI Platform.
      System will pick a display name if unspecified.
    migrateDataLabelingAnnotatedDatasetConfigs: Optional. Configs for
      migrating AnnotatedDataset in datalabeling.googleapis.com to AI
      Platform's SavedQuery. The specified AnnotatedDatasets have to belong to
      the datalabeling Dataset.
  """

  dataset = _messages.StringField(1)
  datasetDisplayName = _messages.StringField(2)
  migrateDataLabelingAnnotatedDatasetConfigs = _messages.MessageField('GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig', 3, repeated=True)


class GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateDataLabelingDatasetConfigMigrateDataLabelingAnnotatedDatasetConfig(_messages.Message):
  r"""Config for migrating AnnotatedDataset in datalabeling.googleapis.com to
  AI Platform's SavedQuery.

  Fields:
    annotatedDataset: Required. Full resource name of data labeling
      AnnotatedDataset. Format: `projects/{project}/datasets/{dataset}/annotat
      edDatasets/{annotated_dataset}`.
  """

  annotatedDataset = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1MigrateResourceRequestMigrateMlEngineModelVersionConfig(_messages.Message):
  r"""Config for migrating version in ml.googleapis.com to AI Platform's
  Model.

  Fields:
    endpoint: Required. The ml.googleapis.com endpoint that this model version
      should be migrated from. Example values: * ml.googleapis.com * us-
      centrall-ml.googleapis.com * europe-west4-ml.googleapis.com * asia-
      east1-ml.googleapis.com
    modelDisplayName: Required. Display name of the model in AI Platform.
      System will pick a display name if unspecified.
    modelVersion: Required. Full resource name of ml engine model version.
      Format: `projects/{project}/models/{model}/versions/{version}`.
  """

  endpoint = _messages.StringField(1)
  modelDisplayName = _messages.StringField(2)
  modelVersion = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1MigrateResourceResponse(_messages.Message):
  r"""Describes a successfully migrated resource.

  Fields:
    dataset: Migrated Dataset's resource name.
    migratableResource: Before migration, the identifier in ml.googleapis.com,
      automl.googleapis.com or datalabeling.googleapis.com.
    model: Migrated Model's resource name.
  """

  dataset = _messages.StringField(1)
  migratableResource = _messages.MessageField('GoogleCloudAiplatformV1beta1MigratableResource', 2)
  model = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata(_messages.Message):
  r"""Runtime operation metadata with regard to ScaNN Index.

  Fields:
    contentValidationStats: The validation stats of the content (per file) to
      be inserted or updated on the ScaNN Index resource. Populated if
      contentsDeltaUri is provided as part of Index.metadata. Please note
      that, currently for those files that are broken or has unsupported file
      format, we will not have the stats for those files.
  """

  contentValidationStats = _messages.MessageField('GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataContentValidationStats', 1, repeated=True)


class GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataContentValidationStats(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataCont
  entValidationStats object.

  Fields:
    invalidRecordCount: Number of records in this file we skipped due to
      validate errors.
    partialErrors: The detail information of the partial failures encountered
      for those invalid records that couldn't be parsed. Up to 50 partial
      errors will be reported.
    sourceGcsUri: GCS uri pointing to the original file in user's bucket.
    validRecordCount: Number of records in this file that were successfully
      processed.
  """

  invalidRecordCount = _messages.IntegerField(1)
  partialErrors = _messages.MessageField('GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataRecordError', 2, repeated=True)
  sourceGcsUri = _messages.StringField(3)
  validRecordCount = _messages.IntegerField(4)


class GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataRecordError(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadataReco
  rdError object.

  Enums:
    ErrorTypeValueValuesEnum: The error type of this record.

  Fields:
    embeddingId: Empty if the embedding id is failed to parse.
    errorMessage: A human-readable message that is shown to the user to help
      them fix the error. Note that this message may change from time to time,
      your code should check against error_type as the source of truth.
    errorType: The error type of this record.
    rawRecord: The original content of this record.
    sourceGcsUri: GCS uri pointing to the original file in user's bucket.
  """

  class ErrorTypeValueValuesEnum(_messages.Enum):
    r"""The error type of this record.

    Values:
      ERROR_TYPE_UNSPECIFIED: Default, shall not be used.
      EMPTY_LINE: The record is empty.
      INVALID_JSON_SYNTAX: Invalid json format.
      INVALID_CSV_SYNTAX: Invalid csv format.
      INVALID_AVRO_SYNTAX: Invalid avro format.
      INVALID_EMBEDDING_ID: The embedding id is not valid.
      EMBEDDING_SIZE_MISMATCH: The size of the embedding vectors does not
        match with the specified dimension.
      NAMESPACE_MISSING: The `namespace` field is missing.
    """
    ERROR_TYPE_UNSPECIFIED = 0
    EMPTY_LINE = 1
    INVALID_JSON_SYNTAX = 2
    INVALID_CSV_SYNTAX = 3
    INVALID_AVRO_SYNTAX = 4
    INVALID_EMBEDDING_ID = 5
    EMBEDDING_SIZE_MISMATCH = 6
    NAMESPACE_MISSING = 7

  embeddingId = _messages.StringField(1)
  errorMessage = _messages.StringField(2)
  errorType = _messages.EnumField('ErrorTypeValueValuesEnum', 3)
  rawRecord = _messages.StringField(4)
  sourceGcsUri = _messages.StringField(5)


class GoogleCloudAiplatformV1beta1PythonPackageSpec(_messages.Message):
  r"""The spec of a Python packaged code.

  Fields:
    args: Command line arguments to be passed to the Python task.
    executorImageUri: Required. The URI of a container image in the Container
      Registry that will run the provided python package. AI Platform provides
      wide range of executor images with pre-installed packages to meet users'
      various use cases. Only one of the provided images can be set here.
    packageUris: Required. The Google Cloud Storage location of the Python
      package files which are the training program and its dependent packages.
      The maximum number of package URIs is 100.
    pythonModule: Required. The Python module name to run after installing the
      packages.
  """

  args = _messages.StringField(1, repeated=True)
  executorImageUri = _messages.StringField(2)
  packageUris = _messages.StringField(3, repeated=True)
  pythonModule = _messages.StringField(4)


class GoogleCloudAiplatformV1beta1SampledShapleyAttribution(_messages.Message):
  r"""An attribution method that approximates Shapley values for features that
  contribute to the label being predicted. A sampling strategy is used to
  approximate the value rather than considering all subsets of features.

  Fields:
    pathCount: Required. The number of feature permutations to consider when
      approximating the Shapley values. Valid range of its value is [1, 50],
      inclusively.
  """

  pathCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1Scheduling(_messages.Message):
  r"""All parameters related to queuing and scheduling of custom jobs.

  Fields:
    restartJobOnWorkerRestart: Restarts the entire CustomJob if a worker gets
      restarted. This feature can be used by distributed training jobs that
      are not resilient to workers leaving and joining a job.
    timeout: The maximum job running time. The default is 7 days.
  """

  restartJobOnWorkerRestart = _messages.BooleanField(1)
  timeout = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaAnnotationSpecColor(_messages.Message):
  r"""An entry of mapping between color and AnnotationSpec. The mapping is
  used in segmentation mask.

  Fields:
    color: The color of the AnnotationSpec in a segmentation mask.
    displayName: The display name of the AnnotationSpec represented by the
      color in the segmentation mask.
    id: The ID of the AnnotationSpec represented by the color in the
      segmentation mask.
  """

  color = _messages.MessageField('GoogleTypeColor', 1)
  displayName = _messages.StringField(2)
  id = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1SchemaImageBoundingBoxAnnotation(_messages.Message):
  r"""Annotation details specific to image object detection.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    xMax: The rightmost coordinate of the bounding box.
    xMin: The leftmost coordinate of the bounding box.
    yMax: The bottommost coordinate of the bounding box.
    yMin: The topmost coordinate of the bounding box.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  xMax = _messages.FloatField(3)
  xMin = _messages.FloatField(4)
  yMax = _messages.FloatField(5)
  yMin = _messages.FloatField(6)


class GoogleCloudAiplatformV1beta1SchemaImageClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to image classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaImageDataItem(_messages.Message):
  r"""Payload of Image DataItem.

  Fields:
    gcsUri: Required. Google Cloud Storage URI points to the original image in
      user's bucket. The image is up to 30MB in size.
    mimeType: Output only. The mime type of the content of the image. Only the
      images in below listed mime types are supported. - image/jpeg -
      image/gif - image/png - image/webp - image/bmp - image/tiff -
      image/vnd.microsoft.icon
  """

  gcsUri = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaImageDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Image DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Image DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotation(_messages.Message):
  r"""Annotation details specific to image segmentation.

  Fields:
    maskAnnotation: Mask based segmentation annotation. Only one mask
      annotation can exist for one image.
    polygonAnnotation: Polygon annotation.
    polylineAnnotation: Polyline annotation.
  """

  maskAnnotation = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationMaskAnnotation', 1)
  polygonAnnotation = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationPolygonAnnotation', 2)
  polylineAnnotation = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationPolylineAnnotation', 3)


class GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationMaskAnnotation(_messages.Message):
  r"""The mask based segmentation annotation.

  Fields:
    annotationSpecColors: The mapping between color and AnnotationSpec for
      this Annotation.
    maskGcsUri: Google Cloud Storage URI that points to the mask image. The
      image must be in PNG format. It must have the same size as the
      DataItem's image. Each pixel in the image mask represents the
      AnnotationSpec which the pixel in the image DataItem belong to. Each
      color is mapped to one AnnotationSpec based on annotation_spec_colors.
  """

  annotationSpecColors = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaAnnotationSpecColor', 1, repeated=True)
  maskGcsUri = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationPolygonAnnotation(_messages.Message):
  r"""Represents a polygon in image.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    vertexes: The vertexes are connected one by one and the last vertex is
      connected to the first one to represent a polygon.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  vertexes = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaVertex', 3, repeated=True)


class GoogleCloudAiplatformV1beta1SchemaImageSegmentationAnnotationPolylineAnnotation(_messages.Message):
  r"""Represents a polyline in image.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    vertexes: The vertexes are connected one by one and the last vertex in not
      connected to the first one.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  vertexes = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaVertex', 3, repeated=True)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceImageClassificationPredictionInstance(_messages.Message):
  r"""Prediction input format for Image Classification.

  Fields:
    content: The image bytes or GCS URI to make the prediction on.
    mimeType: The MIME type of the content of the image. Only the images in
      below listed MIME types are supported. - image/jpeg - image/gif -
      image/png - image/webp - image/bmp - image/tiff -
      image/vnd.microsoft.icon
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceImageObjectDetectionPredictionInstance(_messages.Message):
  r"""Prediction input format for Image Object Detection.

  Fields:
    content: The image bytes or GCS URI to make the prediction on.
    mimeType: The MIME type of the content of the image. Only the images in
      below listed MIME types are supported. - image/jpeg - image/gif -
      image/png - image/webp - image/bmp - image/tiff -
      image/vnd.microsoft.icon
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceImageSegmentationPredictionInstance(_messages.Message):
  r"""Prediction input format for Image Segmentation.

  Fields:
    content: The image bytes to make the predictions on.
    mimeType: The MIME type of the content of the image. Only the images in
      below listed MIME types are supported. - image/jpeg - image/png
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceTextClassificationPredictionInstance(_messages.Message):
  r"""Prediction input format for Text Classification.

  Fields:
    content: The text snippet to make the predictions on.
    mimeType: The MIME type of the text snippet. The supported MIME types are
      listed below. - text/plain
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceTextExtractionPredictionInstance(_messages.Message):
  r"""Prediction input format for Text Extraction.

  Fields:
    content: The text snippet to make the predictions on.
    key: This field is only used for batch prediction. If a key is provided,
      the batch prediction result will by mapped to this key. If omitted, then
      the batch prediction result will contain the entire input instance. AI
      Platform will not check if keys in the request are duplicates, so it is
      up to the caller to ensure the keys are unique.
    mimeType: The MIME type of the text snippet. The supported MIME types are
      listed below. - text/plain
  """

  content = _messages.StringField(1)
  key = _messages.StringField(2)
  mimeType = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceTextSentimentPredictionInstance(_messages.Message):
  r"""Prediction input format for Text Sentiment.

  Fields:
    content: The text snippet to make the predictions on.
    mimeType: The MIME type of the text snippet. The supported MIME types are
      listed below. - text/plain
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceVideoActionRecognitionPredictionInstance(_messages.Message):
  r"""Prediction input format for Video Action Recognition.

  Fields:
    content: The Google Cloud Storage location of the video on which to
      perform the prediction.
    mimeType: The MIME type of the content of the video. Only the following
      are supported: video/mp4 video/avi video/quicktime
    timeSegmentEnd: The end, exclusive, of the video's time segment on which
      to perform the prediction. Expressed as a number of seconds as measured
      from the start of the video, with "s" appended at the end. Fractions are
      allowed, up to a microsecond precision, and "inf" or "Infinity" is
      allowed, which means the end of the video.
    timeSegmentStart: The beginning, inclusive, of the video's time segment on
      which to perform the prediction. Expressed as a number of seconds as
      measured from the start of the video, with "s" appended at the end.
      Fractions are allowed, up to a microsecond precision.
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)
  timeSegmentEnd = _messages.StringField(3)
  timeSegmentStart = _messages.StringField(4)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceVideoClassificationPredictionInstance(_messages.Message):
  r"""Prediction input format for Video Classification.

  Fields:
    content: The Google Cloud Storage location of the video on which to
      perform the prediction.
    mimeType: The MIME type of the content of the video. Only the following
      are supported: video/mp4 video/avi video/quicktime
    timeSegmentEnd: The end, exclusive, of the video's time segment on which
      to perform the prediction. Expressed as a number of seconds as measured
      from the start of the video, with "s" appended at the end. Fractions are
      allowed, up to a microsecond precision, and "inf" or "Infinity" is
      allowed, which means the end of the video.
    timeSegmentStart: The beginning, inclusive, of the video's time segment on
      which to perform the prediction. Expressed as a number of seconds as
      measured from the start of the video, with "s" appended at the end.
      Fractions are allowed, up to a microsecond precision.
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)
  timeSegmentEnd = _messages.StringField(3)
  timeSegmentStart = _messages.StringField(4)


class GoogleCloudAiplatformV1beta1SchemaPredictInstanceVideoObjectTrackingPredictionInstance(_messages.Message):
  r"""Prediction input format for Video Object Tracking.

  Fields:
    content: The Google Cloud Storage location of the video on which to
      perform the prediction.
    mimeType: The MIME type of the content of the video. Only the following
      are supported: video/mp4 video/avi video/quicktime
    timeSegmentEnd: The end, exclusive, of the video's time segment on which
      to perform the prediction. Expressed as a number of seconds as measured
      from the start of the video, with "s" appended at the end. Fractions are
      allowed, up to a microsecond precision, and "inf" or "Infinity" is
      allowed, which means the end of the video.
    timeSegmentStart: The beginning, inclusive, of the video's time segment on
      which to perform the prediction. Expressed as a number of seconds as
      measured from the start of the video, with "s" appended at the end.
      Fractions are allowed, up to a microsecond precision.
  """

  content = _messages.StringField(1)
  mimeType = _messages.StringField(2)
  timeSegmentEnd = _messages.StringField(3)
  timeSegmentStart = _messages.StringField(4)


class GoogleCloudAiplatformV1beta1SchemaPredictParamsImageClassificationPredictionParams(_messages.Message):
  r"""Prediction model parameters for Image Classification.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The Model only returns up to that many top, by confidence
      score, predictions per instance. If this number is very high, the Model
      may return fewer predictions. Default value is 10.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1SchemaPredictParamsImageObjectDetectionPredictionParams(_messages.Message):
  r"""Prediction model parameters for Image Object Detection.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The Model only returns up to that many top, by confidence
      score, predictions per instance. Note that number of returned
      predictions is also limited by metadata's predictionsLimit. Default
      value is 10.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1SchemaPredictParamsImageSegmentationPredictionParams(_messages.Message):
  r"""Prediction model parameters for Image Segmentation.

  Fields:
    confidenceThreshold: When the model predicts category of pixels of the
      image, it will only provide predictions for pixels that it is at least
      this much confident about. All other pixels will be classified as
      background. Default value is 0.5.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1beta1SchemaPredictParamsVideoActionRecognitionPredictionParams(_messages.Message):
  r"""Prediction model parameters for Video Action Recognition.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The model only returns up to that many top, by confidence
      score, predictions per frame of the video. If this number is very high,
      the Model may return fewer predictions per frame. Default value is 50.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1SchemaPredictParamsVideoClassificationPredictionParams(_messages.Message):
  r"""Prediction model parameters for Video Classification.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The Model only returns up to that many top, by confidence
      score, predictions per instance. If this number is very high, the Model
      may return fewer predictions. Default value is 10,000.
    oneSecIntervalClassification: Set to true to request classification for a
      video at one-second intervals. AI Platform returns labels and their
      confidence scores for each second of the entire time segment of the
      video that user specified in the input WARNING: Model evaluation is not
      done for this classification type, the quality of it depends on the
      training data, but there are no metrics provided to describe that
      quality. Default value is false
    segmentClassification: Set to true to request segment-level
      classification. AI Platform returns labels and their confidence scores
      for the entire time segment of the video that user specified in the
      input instance. Default value is true
    shotClassification: Set to true to request shot-level classification. AI
      Platform determines the boundaries for each camera shot in the entire
      time segment of the video that user specified in the input instance. AI
      Platform then returns labels and their confidence scores for each
      detected shot, along with the start and end time of the shot. WARNING:
      Model evaluation is not done for this classification type, the quality
      of it depends on the training data, but there are no metrics provided to
      describe that quality. Default value is false
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  oneSecIntervalClassification = _messages.BooleanField(3)
  segmentClassification = _messages.BooleanField(4)
  shotClassification = _messages.BooleanField(5)


class GoogleCloudAiplatformV1beta1SchemaPredictParamsVideoObjectTrackingPredictionParams(_messages.Message):
  r"""Prediction model parameters for Video Object Tracking.

  Fields:
    confidenceThreshold: The Model only returns predictions with at least this
      confidence score. Default value is 0.0
    maxPredictions: The model only returns up to that many top, by confidence
      score, predictions per frame of the video. If this number is very high,
      the Model may return fewer predictions per frame. Default value is 50.
    minBoundingBoxSize: Only bounding boxes with shortest edge at least that
      long as a relative value of video frame size are returned. Default value
      is 0.0.
  """

  confidenceThreshold = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  maxPredictions = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  minBoundingBoxSize = _messages.FloatField(3, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionClassificationPredictionResult(_messages.Message):
  r"""Prediction output format for Image and Text Classification.

  Fields:
    confidences: The Model's confidences in correctness of the predicted IDs,
      higher value means higher confidence. Order matches the Ids.
    displayNames: The display names of the AnnotationSpecs that had been
      identified, order matches the IDs.
    ids: The resource IDs of the AnnotationSpecs that had been identified,
      ordered by the confidence score descendingly.
  """

  confidences = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT)
  displayNames = _messages.StringField(2, repeated=True)
  ids = _messages.IntegerField(3, repeated=True)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionImageObjectDetectionPredictionResult(_messages.Message):
  r"""Prediction output format for Image Object Detection.

  Messages:
    BboxesValueListEntry: Single entry in a BboxesValue.

  Fields:
    bboxes: Bounding boxes, i.e. the rectangles over the image, that pinpoint
      the found AnnotationSpecs. Given in order that matches the IDs. Each
      bounding box is an array of 4 numbers `xMin`, `xMax`, `yMin`, and
      `yMax`, which represent the extremal coordinates of the box. They are
      relative to the image size, and the point 0,0 is in the top left of the
      image.
    confidences: The Model's confidences in correctness of the predicted IDs,
      higher value means higher confidence. Order matches the Ids.
    displayNames: The display names of the AnnotationSpecs that had been
      identified, order matches the IDs.
    ids: The resource IDs of the AnnotationSpecs that had been identified,
      ordered by the confidence score descendingly.
  """

  class BboxesValueListEntry(_messages.Message):
    r"""Single entry in a BboxesValue.

    Fields:
      entry: A extra_types.JsonValue attribute.
    """

    entry = _messages.MessageField('extra_types.JsonValue', 1, repeated=True)

  bboxes = _messages.MessageField('BboxesValueListEntry', 1, repeated=True)
  confidences = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT)
  displayNames = _messages.StringField(3, repeated=True)
  ids = _messages.IntegerField(4, repeated=True)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionImageSegmentationPredictionResult(_messages.Message):
  r"""Prediction output format for Image Segmentation.

  Fields:
    categoryMask: A PNG image where each pixel in the mask represents the
      category in which the pixel in the original image was predicted to
      belong to. The size of this image will be the same as the original
      image. The mapping between the AnntoationSpec and the color can be found
      in model's metadata. The model will choose the most likely category and
      if none of the categories reach the confidence threshold, the pixel will
      be marked as background.
    confidenceMask: A one channel image which is encoded as an 8bit lossless
      PNG. The size of the image will be the same as the original image. For a
      specific pixel, darker color means less confidence in correctness of the
      cateogry in the categoryMask for the corresponding pixel. Black means no
      confidence and white means complete confidence.
  """

  categoryMask = _messages.StringField(1)
  confidenceMask = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionTabularClassificationPredictionResult(_messages.Message):
  r"""Prediction output format for Tabular Classification.

  Fields:
    classes: The name of the classes being classified, contains all possible
      values of the target column.
    scores: The model's confidence in each class being correct, higher value
      means higher confidence. The N-th score corresponds to the N-th class in
      classes.
  """

  classes = _messages.StringField(1, repeated=True)
  scores = _messages.FloatField(2, repeated=True, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionTabularRegressionPredictionResult(_messages.Message):
  r"""Prediction output format for Tabular Regression.

  Fields:
    lowerBound: The lower bound of the prediction interval.
    upperBound: The upper bound of the prediction interval.
    value: The regression value.
  """

  lowerBound = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  upperBound = _messages.FloatField(2, variant=_messages.Variant.FLOAT)
  value = _messages.FloatField(3, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionTextExtractionPredictionResult(_messages.Message):
  r"""Prediction output format for Text Extraction.

  Fields:
    confidences: The Model's confidences in correctness of the predicted IDs,
      higher value means higher confidence. Order matches the Ids.
    displayNames: The display names of the AnnotationSpecs that had been
      identified, order matches the IDs.
    ids: The resource IDs of the AnnotationSpecs that had been identified,
      ordered by the confidence score descendingly.
    textSegmentEndOffsets: The end offsets, inclusive, of the text segment in
      which the AnnotationSpec has been identified. Expressed as a zero-based
      number of characters as measured from the start of the text snippet.
    textSegmentStartOffsets: The start offsets, inclusive, of the text segment
      in which the AnnotationSpec has been identified. Expressed as a zero-
      based number of characters as measured from the start of the text
      snippet.
  """

  confidences = _messages.FloatField(1, repeated=True, variant=_messages.Variant.FLOAT)
  displayNames = _messages.StringField(2, repeated=True)
  ids = _messages.IntegerField(3, repeated=True)
  textSegmentEndOffsets = _messages.IntegerField(4, repeated=True)
  textSegmentStartOffsets = _messages.IntegerField(5, repeated=True)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionTextSentimentPredictionResult(_messages.Message):
  r"""Prediction output format for Text Sentiment

  Fields:
    sentiment: The integer sentiment labels between 0 (inclusive) and
      sentimentMax label (inclusive), while 0 maps to the least positive
      sentiment and sentimentMax maps to the most positive one. The higher the
      score is, the more positive the sentiment in the text snippet is. Note:
      sentimentMax is an integer value between 1 (inclusive) and 10
      (inclusive).
  """

  sentiment = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionTimeSeriesForecastingPredictionResult(_messages.Message):
  r"""Prediction output format for Time Series Forecasting.

  Fields:
    value: The regression value.
  """

  value = _messages.FloatField(1, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoActionRecognitionPredictionResult(_messages.Message):
  r"""Prediction output format for Video Action Recognition.

  Fields:
    confidence: The Model's confidence in correction of this prediction,
      higher value means higher confidence.
    displayName: The display name of the AnnotationSpec that had been
      identified.
    id: The resource ID of the AnnotationSpec that had been identified.
    timeSegmentEnd: The end, exclusive, of the video's time segment in which
      the AnnotationSpec has been identified. Expressed as a number of seconds
      as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end.
    timeSegmentStart: The beginning, inclusive, of the video's time segment in
      which the AnnotationSpec has been identified. Expressed as a number of
      seconds as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end.
  """

  confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  displayName = _messages.StringField(2)
  id = _messages.StringField(3)
  timeSegmentEnd = _messages.StringField(4)
  timeSegmentStart = _messages.StringField(5)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoClassificationPredictionResult(_messages.Message):
  r"""Prediction output format for Video Classification.

  Fields:
    confidence: The Model's confidence in correction of this prediction,
      higher value means higher confidence.
    displayName: The display name of the AnnotationSpec that had been
      identified.
    id: The resource ID of the AnnotationSpec that had been identified.
    timeSegmentEnd: The end, exclusive, of the video's time segment in which
      the AnnotationSpec has been identified. Expressed as a number of seconds
      as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end. Note that for
      'segment-classification' prediction type, this equals the original
      'timeSegmentEnd' from the input instance, for other types it is the end
      of a shot or a 1 second interval respectively.
    timeSegmentStart: The beginning, inclusive, of the video's time segment in
      which the AnnotationSpec has been identified. Expressed as a number of
      seconds as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end. Note that for
      'segment-classification' prediction type, this equals the original
      'timeSegmentStart' from the input instance, for other types it is the
      start of a shot or a 1 second interval respectively.
    type: The type of the prediction. The requested types can be configured
      via parameters. This will be one of - segment-classification - shot-
      classification - one-sec-interval-classification
  """

  confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  displayName = _messages.StringField(2)
  id = _messages.StringField(3)
  timeSegmentEnd = _messages.StringField(4)
  timeSegmentStart = _messages.StringField(5)
  type = _messages.StringField(6)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoObjectTrackingPredictionResult(_messages.Message):
  r"""Prediction output format for Video Object Tracking.

  Fields:
    confidence: The Model's confidence in correction of this prediction,
      higher value means higher confidence.
    displayName: The display name of the AnnotationSpec that had been
      identified.
    frames: All of the frames of the video in which a single object instance
      has been detected. The bounding boxes in the frames identify the same
      object.
    id: The resource ID of the AnnotationSpec that had been identified.
    timeSegmentEnd: The end, inclusive, of the video's time segment in which
      the object instance has been detected. Expressed as a number of seconds
      as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end.
    timeSegmentStart: The beginning, inclusive, of the video's time segment in
      which the object instance has been detected. Expressed as a number of
      seconds as measured from the start of the video, with fractions up to a
      microsecond precision, and with "s" appended at the end.
  """

  confidence = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  displayName = _messages.StringField(2)
  frames = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoObjectTrackingPredictionResultFrame', 3, repeated=True)
  id = _messages.StringField(4)
  timeSegmentEnd = _messages.StringField(5)
  timeSegmentStart = _messages.StringField(6)


class GoogleCloudAiplatformV1beta1SchemaPredictPredictionVideoObjectTrackingPredictionResultFrame(_messages.Message):
  r"""The fields `xMin`, `xMax`, `yMin`, and `yMax` refer to a bounding box,
  i.e. the rectangle over the video frame pinpointing the found
  AnnotationSpec. The coordinates are relative to the frame size, and the
  point 0,0 is in the top left of the frame.

  Fields:
    timeOffset: A time (frame) of a video in which the object has been
      detected. Expressed as a number of seconds as measured from the start of
      the video, with fractions up to a microsecond precision, and with "s"
      appended at the end.
    xMax: The rightmost coordinate of the bounding box.
    xMin: The leftmost coordinate of the bounding box.
    yMax: The bottommost coordinate of the bounding box.
    yMin: The topmost coordinate of the bounding box.
  """

  timeOffset = _messages.StringField(1)
  xMax = _messages.FloatField(2, variant=_messages.Variant.FLOAT)
  xMin = _messages.FloatField(3, variant=_messages.Variant.FLOAT)
  yMax = _messages.FloatField(4, variant=_messages.Variant.FLOAT)
  yMin = _messages.FloatField(5, variant=_messages.Variant.FLOAT)


class GoogleCloudAiplatformV1beta1SchemaPredictionResult(_messages.Message):
  r"""Represents a line of JSONL in the batch prediction output file.

  Messages:
    InstanceValue: User's input instance. Struct is used here instead of Any
      so that JsonFormat does not append an extra "@type" field when we
      convert the proto to JSON.

  Fields:
    error: The error result. Do not set prediction if this is set.
    instance: User's input instance. Struct is used here instead of Any so
      that JsonFormat does not append an extra "@type" field when we convert
      the proto to JSON.
    key: Optional user-provided key from the input instance.
    prediction: The prediction result. Value is used here instead of Any so
      that JsonFormat does not append an extra "@type" field when we convert
      the proto to JSON and so we can represent array of objects. Do not set
      error if this is set.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class InstanceValue(_messages.Message):
    r"""User's input instance. Struct is used here instead of Any so that
    JsonFormat does not append an extra "@type" field when we convert the
    proto to JSON.

    Messages:
      AdditionalProperty: An additional property for a InstanceValue object.

    Fields:
      additionalProperties: Properties of the object.
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a InstanceValue object.

      Fields:
        key: Name of the additional property.
        value: A extra_types.JsonValue attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('extra_types.JsonValue', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  error = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaPredictionResultError', 1)
  instance = _messages.MessageField('InstanceValue', 2)
  key = _messages.StringField(3)
  prediction = _messages.MessageField('extra_types.JsonValue', 4)


class GoogleCloudAiplatformV1beta1SchemaPredictionResultError(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaPredictionResultError object.

  Enums:
    StatusValueValuesEnum: Error status. This will be serialized into the enum
      name e.g. "NOT_FOUND".

  Fields:
    message: Error message with additional details.
    status: Error status. This will be serialized into the enum name e.g.
      "NOT_FOUND".
  """

  class StatusValueValuesEnum(_messages.Enum):
    r"""Error status. This will be serialized into the enum name e.g.
    "NOT_FOUND".

    Values:
      OK: Not an error; returned on success HTTP Mapping: 200 OK
      CANCELLED: The operation was cancelled, typically by the caller. HTTP
        Mapping: 499 Client Closed Request
      UNKNOWN: Unknown error. For example, this error may be returned when a
        `Status` value received from another address space belongs to an error
        space that is not known in this address space. Also errors raised by
        APIs that do not return enough error information may be converted to
        this error. HTTP Mapping: 500 Internal Server Error
      INVALID_ARGUMENT: The client specified an invalid argument. Note that
        this differs from `FAILED_PRECONDITION`. `INVALID_ARGUMENT` indicates
        arguments that are problematic regardless of the state of the system
        (e.g., a malformed file name). HTTP Mapping: 400 Bad Request
      DEADLINE_EXCEEDED: The deadline expired before the operation could
        complete. For operations that change the state of the system, this
        error may be returned even if the operation has completed
        successfully. For example, a successful response from a server could
        have been delayed long enough for the deadline to expire. HTTP
        Mapping: 504 Gateway Timeout
      NOT_FOUND: Some requested entity (e.g., file or directory) was not
        found. Note to server developers: if a request is denied for an entire
        class of users, such as gradual feature rollout or undocumented
        allowlist, `NOT_FOUND` may be used. If a request is denied for some
        users within a class of users, such as user-based access control,
        `PERMISSION_DENIED` must be used. HTTP Mapping: 404 Not Found
      ALREADY_EXISTS: The entity that a client attempted to create (e.g., file
        or directory) already exists. HTTP Mapping: 409 Conflict
      PERMISSION_DENIED: The caller does not have permission to execute the
        specified operation. `PERMISSION_DENIED` must not be used for
        rejections caused by exhausting some resource (use
        `RESOURCE_EXHAUSTED` instead for those errors). `PERMISSION_DENIED`
        must not be used if the caller can not be identified (use
        `UNAUTHENTICATED` instead for those errors). This error code does not
        imply the request is valid or the requested entity exists or satisfies
        other pre-conditions. HTTP Mapping: 403 Forbidden
      UNAUTHENTICATED: The request does not have valid authentication
        credentials for the operation. HTTP Mapping: 401 Unauthorized
      RESOURCE_EXHAUSTED: Some resource has been exhausted, perhaps a per-user
        quota, or perhaps the entire file system is out of space. HTTP
        Mapping: 429 Too Many Requests
      FAILED_PRECONDITION: The operation was rejected because the system is
        not in a state required for the operation's execution. For example,
        the directory to be deleted is non-empty, an rmdir operation is
        applied to a non-directory, etc. Service implementors can use the
        following guidelines to decide between `FAILED_PRECONDITION`,
        `ABORTED`, and `UNAVAILABLE`: (a) Use `UNAVAILABLE` if the client can
        retry just the failing call. (b) Use `ABORTED` if the client should
        retry at a higher level (e.g., when a client-specified test-and-set
        fails, indicating the client should restart a read-modify-write
        sequence). (c) Use `FAILED_PRECONDITION` if the client should not
        retry until the system state has been explicitly fixed. E.g., if an
        "rmdir" fails because the directory is non-empty,
        `FAILED_PRECONDITION` should be returned since the client should not
        retry unless the files are deleted from the directory. HTTP Mapping:
        400 Bad Request
      ABORTED: The operation was aborted, typically due to a concurrency issue
        such as a sequencer check failure or transaction abort. See the
        guidelines above for deciding between `FAILED_PRECONDITION`,
        `ABORTED`, and `UNAVAILABLE`. HTTP Mapping: 409 Conflict
      OUT_OF_RANGE: The operation was attempted past the valid range. E.g.,
        seeking or reading past end-of-file. Unlike `INVALID_ARGUMENT`, this
        error indicates a problem that may be fixed if the system state
        changes. For example, a 32-bit file system will generate
        `INVALID_ARGUMENT` if asked to read at an offset that is not in the
        range [0,2^32-1], but it will generate `OUT_OF_RANGE` if asked to read
        from an offset past the current file size. There is a fair bit of
        overlap between `FAILED_PRECONDITION` and `OUT_OF_RANGE`. We recommend
        using `OUT_OF_RANGE` (the more specific error) when it applies so that
        callers who are iterating through a space can easily look for an
        `OUT_OF_RANGE` error to detect when they are done. HTTP Mapping: 400
        Bad Request
      UNIMPLEMENTED: The operation is not implemented or is not
        supported/enabled in this service. HTTP Mapping: 501 Not Implemented
      INTERNAL: Internal errors. This means that some invariants expected by
        the underlying system have been broken. This error code is reserved
        for serious errors. HTTP Mapping: 500 Internal Server Error
      UNAVAILABLE: The service is currently unavailable. This is most likely a
        transient condition, which can be corrected by retrying with a
        backoff. Note that it is not always safe to retry non-idempotent
        operations. See the guidelines above for deciding between
        `FAILED_PRECONDITION`, `ABORTED`, and `UNAVAILABLE`. HTTP Mapping: 503
        Service Unavailable
      DATA_LOSS: Unrecoverable data loss or corruption. HTTP Mapping: 500
        Internal Server Error
    """
    OK = 0
    CANCELLED = 1
    UNKNOWN = 2
    INVALID_ARGUMENT = 3
    DEADLINE_EXCEEDED = 4
    NOT_FOUND = 5
    ALREADY_EXISTS = 6
    PERMISSION_DENIED = 7
    UNAUTHENTICATED = 8
    RESOURCE_EXHAUSTED = 9
    FAILED_PRECONDITION = 10
    ABORTED = 11
    OUT_OF_RANGE = 12
    UNIMPLEMENTED = 13
    INTERNAL = 14
    UNAVAILABLE = 15
    DATA_LOSS = 16

  message = _messages.StringField(1)
  status = _messages.EnumField('StatusValueValuesEnum', 2)


class GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain tables data.

  Fields:
    inputConfig: A
      GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataInputConfig
      attribute.
  """

  inputConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataInputConfig', 1)


class GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataBigQuerySource(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataBigQuerySource
  object.

  Fields:
    uri: The URI of a BigQuery table. e.g.
      bq://projectId.bqDatasetId.bqTableId
  """

  uri = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataGcsSource(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataGcsSource
  object.

  Fields:
    uri: Cloud Storage URI of one or more files. Only CSV files are supported.
      The first line of the CSV file is used as the header. If there are
      multiple files, the header is the first line of the lexicographically
      first file, the other files must either contain the exact same header or
      omit the header.
  """

  uri = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataInputConfig(_messages.Message):
  r"""The tables Dataset's data source. The Dataset doesn't store the data
  directly, but only pointer(s) to its data.

  Fields:
    bigquerySource: A
      GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataBigQuerySource
      attribute.
    gcsSource: A
      GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataGcsSource
      attribute.
  """

  bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataBigQuerySource', 1)
  gcsSource = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTablesDatasetMetadataGcsSource', 2)


class GoogleCloudAiplatformV1beta1SchemaTextClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to text classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaTextDataItem(_messages.Message):
  r"""Payload of Text DataItem.

  Fields:
    gcsUri: Output only. Google Cloud Storage URI points to the original text
      in user's bucket. The text file is up to 10MB in size.
  """

  gcsUri = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTextDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Text DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Text DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaTextExtractionAnnotation(_messages.Message):
  r"""Annotation details specific to text extraction.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    textSegment: The segment of the text content.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  textSegment = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTextSegment', 3)


class GoogleCloudAiplatformV1beta1SchemaTextSegment(_messages.Message):
  r"""The text segment inside of DataItem.

  Fields:
    content: The text content in the segment for output only.
    endOffset: Zero-based character index of the first character past the end
      of the text segment (counting character from the beginning of the text).
      The character at the end_offset is NOT included in the text segment.
    startOffset: Zero-based character index of the first character of the text
      segment (counting characters from the beginning of the text).
  """

  content = _messages.StringField(1)
  endOffset = _messages.IntegerField(2, variant=_messages.Variant.UINT64)
  startOffset = _messages.IntegerField(3, variant=_messages.Variant.UINT64)


class GoogleCloudAiplatformV1beta1SchemaTextSentimentAnnotation(_messages.Message):
  r"""Annotation details specific to text sentiment.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    sentiment: The sentiment score for text.
    sentimentMax: The sentiment max score for text.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  sentiment = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  sentimentMax = _messages.IntegerField(4, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1SchemaTextSentimentSavedQueryMetadata(_messages.Message):
  r"""The metadata of SavedQuery contains TextSentiment Annotations.

  Fields:
    sentimentMax: The maximum sentiment of sentiment Anntoation in this
      SavedQuery.
  """

  sentimentMax = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1SchemaTimeSegment(_messages.Message):
  r"""A time period inside of a DataItem that has a time dimension (e.g.
  video).

  Fields:
    endTimeOffset: End of the time segment (exclusive), represented as the
      duration since the start of the DataItem.
    startTimeOffset: Start of the time segment (inclusive), represented as the
      duration since the start of the DataItem.
  """

  endTimeOffset = _messages.StringField(1)
  startTimeOffset = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain time series data.

  Fields:
    inputConfig: A
      GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataInputConfig
      attribute.
    timeColumn: The column name of the time column that identifies time order
      in the time series.
    timeSeriesIdentifierColumn: The column name of the time series identifier
      column that identifies the time series.
  """

  inputConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataInputConfig', 1)
  timeColumn = _messages.StringField(2)
  timeSeriesIdentifierColumn = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataBigQuerySource(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataBigQuerySource
  object.

  Fields:
    uri: The URI of a BigQuery table.
  """

  uri = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataGcsSource(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataGcsSource
  object.

  Fields:
    uri: Cloud Storage URI of one or more files. Only CSV files are supported.
      The first line of the CSV file is used as the header. If there are
      multiple files, the header is the first line of the lexicographically
      first file, the other files must either contain the exact same header or
      omit the header.
  """

  uri = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataInputConfig(_messages.Message):
  r"""The time series Dataset's data source. The Dataset doesn't store the
  data directly, but only pointer(s) to its data.

  Fields:
    bigquerySource: A
      GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataBigQuerySourc
      e attribute.
    gcsSource: A
      GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataGcsSource
      attribute.
  """

  bigquerySource = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataBigQuerySource', 1)
  gcsSource = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTimeSeriesDatasetMetadataGcsSource', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecasting(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Forecasting Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingMetadata', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecasti
  ngInputs object.

  Fields:
    exportEvaluatedDataItemsConfig: Configuration for exporting test set
      predictions to a BigQuery table. If this configuration is absent, then
      the export is not performed.
    forecastWindowEnd: The number of periods offset into the future as the end
      of the forecast window (the window of future values to predict, relative
      to the present.), where each period is one unit of granularity as
      defined by the `period` field above. Inclusive.
    forecastWindowStart: The number of periods offset into the future as the
      start of the forecast window (the window of future values to predict,
      relative to the present.), where each period is one unit of granularity
      as defined by the `period` field above. Default to 0. Inclusive.
    optimizationObjective: Objective function the model is optimizing towards.
      The training process creates a model that optimizes the value of the
      objective function over the validation set. The supported optimization
      objectives: "minimize-rmse" (default) - Minimize root-mean-squared error
      (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-
      rmsle" - Minimize root-mean-squared log error (RMSLE). "minimize-rmspe"
      - Minimize root-mean-squared percentage error (RMSPE). "minimize-wape-
      mae" - Minimize the combination of weighted absolute percentage error
      (WAPE) and mean-absolute-error (MAE). "minimize-quantile-loss" -
      Minimize the quantile loss at the quantiles defined in `quantiles`.
    pastHorizon: The number of periods offset into the past to restrict past
      sequence, where each period is one unit of granularity as defined by the
      `period`. Default value 0 means that it lets algorithm to define the
      value. Inclusive.
    period: Expected difference in time granularity between rows in the data.
      If it is not set, the period is inferred from data.
    quantiles: Quantiles to use for minimize-quantile-loss
      `optimization_objective`. Up to 5 quantiles are allowed of values
      between 0 and 1, exclusive. Required if the value of
      optimization_objective is minimize-quantile-loss. Represents the percent
      quantiles to use for that objective. Quantiles must be unique.
    staticColumns: Column names that should be used as static columns. The
      value of these columns are static per time series.
    targetColumn: The name of the column that the model is to predict.
    timeColumn: The name of the column that identifies time order in the time
      series.
    timeSeriesIdentifierColumn: The name of the column that identifies the
      time series.
    timeVariantPastAndFutureColumns: Column names that should be used as time
      variant past and future columns. This column contains information for
      the given entity (identified by the key column) that is known for the
      past and the future
    timeVariantPastOnlyColumns: Column names that should be used as time
      variant past only columns. This column contains information for the
      given entity (identified by the time_series_identifier_column) that is
      known for the past but not the future (e.g. population of a city in a
      given year, or weather on a given day).
    trainBudgetMilliNodeHours: Required. The train budget of creating this
      model, expressed in milli node hours i.e. 1,000 value in this field
      means 1 node hour. The training cost of the model will not exceed this
      budget. The final cost will be attempted to be close to the budget,
      though may end up being (even) noticeably smaller - at the backend's
      discretion. This especially may happen when further model training
      ceases to provide any improvements. If the budget is set to a value
      known to be insufficient to train a model for the given dataset, the
      training won't be attempted and will error. The train budget must be
      between 1,000 and 72,000 milli node hours, inclusive.
    transformations: Each transformation will apply transform function to
      given input column. And the result will be used for training. When
      creating transformation for BigQuery Struct column, the column should be
      flattened using "." as the delimiter.
    validationOptions: Validation options for the data validation component.
      The available options are: "fail-pipeline" - default, will validate
      against the validation and fail the pipeline if it fails. "ignore-
      validation" - ignore the results of the validation and continue
    weightColumn: Column name that should be used as the weight column. Higher
      values in this column give more importance to the row during model
      training. The column must have numeric values between 0 and 10000
      inclusively; 0 means the row is ignored for training. If weight column
      field is not set, then all rows are assumed to have equal weight of 1.
  """

  exportEvaluatedDataItemsConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig', 1)
  forecastWindowEnd = _messages.IntegerField(2)
  forecastWindowStart = _messages.IntegerField(3)
  optimizationObjective = _messages.StringField(4)
  pastHorizon = _messages.IntegerField(5)
  period = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsPeriod', 6)
  quantiles = _messages.FloatField(7, repeated=True)
  staticColumns = _messages.StringField(8, repeated=True)
  targetColumn = _messages.StringField(9)
  timeColumn = _messages.StringField(10)
  timeSeriesIdentifierColumn = _messages.StringField(11)
  timeVariantPastAndFutureColumns = _messages.StringField(12, repeated=True)
  timeVariantPastOnlyColumns = _messages.StringField(13, repeated=True)
  trainBudgetMilliNodeHours = _messages.IntegerField(14)
  transformations = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation', 15, repeated=True)
  validationOptions = _messages.StringField(16)
  weightColumn = _messages.StringField(17)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsPeriod(_messages.Message):
  r"""A duration of time expressed in time granularity units.

  Fields:
    quantity: The number of units per period, e.g. 3 weeks or 2 months.
    unit: The time granularity unit of this time period. The supported unit
      are: "minute" "hour" "day" "week" "month" "year"
  """

  quantity = _messages.IntegerField(1)
  unit = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformation(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecasti
  ngInputsTransformation object.

  Fields:
    auto: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForec
      astingInputsTransformationAutoTransformation attribute.
    categorical: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAuto
      MlForecastingInputsTransformationCategoricalTransformation attribute.
    numeric: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlFo
      recastingInputsTransformationNumericTransformation attribute.
    repeatedCategorical: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefini
      tionAutoMlForecastingInputsTransformationCategoricalArrayTransformation
      attribute.
    repeatedNumeric: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinition
      AutoMlForecastingInputsTransformationNumericArrayTransformation
      attribute.
    repeatedText: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAut
      oMlForecastingInputsTransformationTextArrayTransformation attribute.
    text: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForec
      astingInputsTransformationTextTransformation attribute.
    timestamp: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMl
      ForecastingInputsTransformationTimestampTransformation attribute.
  """

  auto = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation', 1)
  categorical = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation', 2)
  numeric = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation', 3)
  repeatedCategorical = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalArrayTransformation', 4)
  repeatedNumeric = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericArrayTransformation', 5)
  repeatedText = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextArrayTransformation', 6)
  text = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation', 7)
  timestamp = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation', 8)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationAutoTransformation(_messages.Message):
  r"""Training pipeline will infer the proper transformation based on the
  statistic of dataset.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalArrayTransformation(_messages.Message):
  r"""Treats the column as categorical array and performs following
  transformation functions. * For each element in the array, convert the
  category name to a dictionary lookup index and generate an embedding for
  each index. Combine the embedding of all elements into a single embedding
  using the mean. * Empty arrays treated as an embedding of zeroes.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationCategoricalTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  categorical string as is--no change to case, punctuation, spelling, tense,
  and so on. * Convert the category name to a dictionary lookup index and
  generate an embedding for each index. * Categories that appear less than 5
  times in the training dataset are treated as the "unknown" category. The
  "unknown" category gets its own special lookup index and resulting
  embedding.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericArrayTransformation(_messages.Message):
  r"""Treats the column as numerical array and performs following
  transformation functions. * All transformations for Numerical types applied
  to the average of the all elements. * The average of empty arrays is treated
  as zero.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationNumericTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  value converted to float32. * The z_score of the value. * log(value+1) when
  the value is greater than or equal to 0. Otherwise, this transformation is
  not applied and the value is considered a missing value. * z_score of
  log(value+1) when the value is greater than or equal to 0. Otherwise, this
  transformation is not applied and the value is considered a missing value. *
  A boolean value that indicates whether the value is valid.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextArrayTransformation(_messages.Message):
  r"""Treats the column as text array and performs following transformation
  functions. * Concatenate all text values in the array into a single text
  value using a space (" ") as a delimiter, and then treat the result as a
  single text value. Apply the transformations for Text columns. * Empty
  arrays treated as an empty text.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTextTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  text as is--no change to case, punctuation, spelling, tense, and so on. *
  Tokenize text to words. Convert each words to a dictionary lookup index and
  generate an embedding for each index. Combine the embedding of all elements
  into a single embedding using the mean. * Tokenization is based on unicode
  script boundaries. * Missing values get their own lookup index and resulting
  embedding. * Stop-words receive no special treatment and are not removed.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingInputsTransformationTimestampTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. *
  Apply the transformation functions for Numerical columns. * Determine the
  year, month, day,and weekday. Treat each value from the * timestamp as a
  Categorical column. * Invalid numerical values (for example, values that
  fall outside of a typical timestamp range, or are extreme values) receive no
  special treatment and are not removed.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
    timeFormat: The format in which that time field is expressed. The
      time_format must either be one of: * `unix-seconds` * `unix-
      milliseconds` * `unix-microseconds` * `unix-nanoseconds` (for
      respectively number of seconds, milliseconds, microseconds and
      nanoseconds since start of the Unix epoch); or be written in `strftime`
      syntax. If time_format is not set, then the default format is RFC 3339
      `date-time` format, where `time-offset` = `"Z"` (e.g.
      1985-04-12T23:20:50.52Z)
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)
  timeFormat = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlForecastingMetadata(_messages.Message):
  r"""Model metadata specific to AutoML Forecasting.

  Fields:
    trainCostMilliNodeHours: Output only. The actual training cost of the
      model, expressed in milli node hours, i.e. 1,000 value in this field
      means 1 node hour. Guaranteed to not exceed the train budget.
  """

  trainCostMilliNodeHours = _messages.IntegerField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassification(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Image Classification
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassificationMetadata', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassificationInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClas
  sificationInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    baseModelId: The ID of the `base` model. If it is specified, the new model
      will be trained based on the `base` model. Otherwise, the new model will
      be trained from scratch. The `base` model must be in the same Project
      and Location as the new Model to train, and have the same modelType.
    budgetMilliNodeHours: The training budget of creating this model,
      expressed in milli node hours i.e. 1,000 value in this field means 1
      node hour. The actual metadata.costMilliNodeHours will be equal or less
      than this value. If further model training ceases to provide any
      improvements, it will stop without using the full budget and the
      metadata.successfulStopReason will be `model-converged`. Note, node_hour
      = actual_hour * number_of_nodes_involved. For modelType
      `cloud`(default), the budget must be between 8,000 and 800,000 milli
      node hours, inclusive. The default value is 192,000 which represents one
      day in wall time, considering 8 nodes are used. For model types `mobile-
      tf-low-latency-1`, `mobile-tf-versatile-1`, `mobile-tf-high-accuracy-1`,
      the training budget must be between 1,000 and 100,000 milli node hours,
      inclusive. The default value is 24,000 which represents one day in wall
      time on a single node that is used.
    disableEarlyStopping: Use the entire training budget. This disables the
      early stopping feature. When false the early stopping feature is
      enabled, which means that AutoML Image Classification might stop
      training before the entire training budget has been used.
    evaluationExplanationSpec: Specification for explaining the evaluation
      results. If specified, the ModelEvaluation of the trained Model will
      include explanations for predictions on the test data. Each prediction
      will be explained twice, using integrated gradients and XRAI attribution
      methods respectively.
    modelType: A ModelTypeValueValuesEnum attribute.
    multiLabel: If false, a single-label (multi-class) Model will be trained
      (i.e. assuming that for each image just up to one annotation may be
      applicable). If true, a multi-label Model will be trained (i.e. assuming
      that for each image multiple annotations may be applicable).
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD: A Model best tailored to be used within Google Cloud, and which
        cannot be exported. Default.
      MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) as TensorFlow or Core ML model and used on a
        mobile or edge device afterwards. Expected to have low latency, but
        may have lower prediction quality than other mobile models.
      MOBILE_TF_VERSATILE_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) as TensorFlow or Core ML model and used on a
        mobile or edge device with afterwards.
      MOBILE_TF_HIGH_ACCURACY_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) as TensorFlow or Core ML model and used on a
        mobile or edge device afterwards. Expected to have a higher latency,
        but should also have a higher prediction quality than other mobile
        models.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD = 1
    MOBILE_TF_LOW_LATENCY_1 = 2
    MOBILE_TF_VERSATILE_1 = 3
    MOBILE_TF_HIGH_ACCURACY_1 = 4

  baseModelId = _messages.StringField(1)
  budgetMilliNodeHours = _messages.IntegerField(2)
  disableEarlyStopping = _messages.BooleanField(3)
  evaluationExplanationSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageExplanationSpecGroup', 4)
  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 5)
  multiLabel = _messages.BooleanField(6)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClassificationMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageClas
  sificationMetadata object.

  Enums:
    SuccessfulStopReasonValueValuesEnum: For successful job completions, this
      is the reason why the job has finished.

  Fields:
    costMilliNodeHours: The actual training cost of creating this model,
      expressed in milli node hours, i.e. 1,000 value in this field means 1
      node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
    successfulStopReason: For successful job completions, this is the reason
      why the job has finished.
  """

  class SuccessfulStopReasonValueValuesEnum(_messages.Enum):
    r"""For successful job completions, this is the reason why the job has
    finished.

    Values:
      SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set.
      BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached.
      MODEL_CONVERGED: Further training of the Model ceased to increase its
        quality, since it already has converged.
    """
    SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
    BUDGET_REACHED = 1
    MODEL_CONVERGED = 2

  costMilliNodeHours = _messages.IntegerField(1)
  successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageExplanationSpec(_messages.Message):
  r"""Specification of a specified explanation attribution method.

  Fields:
    stepCount: The number of steps for approximating the path integral. A good
      value to start is 50 and gradually increase until the sum to diff
      property is within the desired error range. Valid range of its value is
      [1, 100], inclusively.
    visualization: Visualization configurations for image explanation.
  """

  stepCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  visualization = _messages.MessageField('GoogleCloudAiplatformV1beta1ExplanationMetadataInputMetadataVisualization', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageExplanationSpecGroup(_messages.Message):
  r"""Specification for explaining the evaluation results.

  Fields:
    imageGcsDestination: The Cloud Storage location to store the images
      explaining the evaluation result of the images in the test data.
    integratedGradients: Specification for explaining the evaluation results
      using integrated gradients attribution method. The full ExplanationSpec
      used for explaining the evaluation can be found in the
      ModelEvaluation.explanationSpecs object.
    xrai: Specification for explaining the evaluation results using XRAI
      attribution method. The full ExplanationSpec used for explaining the
      evaluation can be found in the ModelEvaluation.explanationSpecs object.
  """

  imageGcsDestination = _messages.StringField(1)
  integratedGradients = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageExplanationSpec', 2)
  xrai = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageExplanationSpec', 3)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetection(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Image Object Detection
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionMetadata', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObje
  ctDetectionInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    budgetMilliNodeHours: The training budget of creating this model,
      expressed in milli node hours i.e. 1,000 value in this field means 1
      node hour. The actual metadata.costMilliNodeHours will be equal or less
      than this value. If further model training ceases to provide any
      improvements, it will stop without using the full budget and the
      metadata.successfulStopReason will be `model-converged`. Note, node_hour
      = actual_hour * number_of_nodes_involved. For modelType
      `cloud`(default), the budget must be between 20,000 and 900,000 milli
      node hours, inclusive. The default value is 216,000 which represents one
      day in wall time, considering 9 nodes are used. For model types `mobile-
      tf-low-latency-1`, `mobile-tf-versatile-1`, `mobile-tf-high-accuracy-1`
      the training budget must be between 1,000 and 100,000 milli node hours,
      inclusive. The default value is 24,000 which represents one day in wall
      time on a single node that is used.
    disableEarlyStopping: Use the entire training budget. This disables the
      early stopping feature. When false the early stopping feature is
      enabled, which means that AutoML Image Object Detection might stop
      training before the entire training budget has been used.
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD_HIGH_ACCURACY_1: A model best tailored to be used within Google
        Cloud, and which cannot be exported. Expected to have a higher
        latency, but should also have a higher prediction quality than other
        cloud models.
      CLOUD_LOW_LATENCY_1: A model best tailored to be used within Google
        Cloud, and which cannot be exported. Expected to have a low latency,
        but may have lower prediction quality than other cloud models.
      MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available
        within Google Cloud can also be exported (see
        ModelService.ExportModel) and used on a mobile or edge device with
        TensorFlow afterwards. Expected to have low latency, but may have
        lower prediction quality than other mobile models.
      MOBILE_TF_VERSATILE_1: A model that, in addition to being available
        within Google Cloud can also be exported (see
        ModelService.ExportModel) and used on a mobile or edge device with
        TensorFlow afterwards.
      MOBILE_TF_HIGH_ACCURACY_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) and used on a mobile or edge device with
        TensorFlow afterwards. Expected to have a higher latency, but should
        also have a higher prediction quality than other mobile models.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD_HIGH_ACCURACY_1 = 1
    CLOUD_LOW_LATENCY_1 = 2
    MOBILE_TF_LOW_LATENCY_1 = 3
    MOBILE_TF_VERSATILE_1 = 4
    MOBILE_TF_HIGH_ACCURACY_1 = 5

  budgetMilliNodeHours = _messages.IntegerField(1)
  disableEarlyStopping = _messages.BooleanField(2)
  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 3)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObjectDetectionMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageObje
  ctDetectionMetadata object.

  Enums:
    SuccessfulStopReasonValueValuesEnum: For successful job completions, this
      is the reason why the job has finished.

  Fields:
    costMilliNodeHours: The actual training cost of creating this model,
      expressed in milli node hours, i.e. 1,000 value in this field means 1
      node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
    successfulStopReason: For successful job completions, this is the reason
      why the job has finished.
  """

  class SuccessfulStopReasonValueValuesEnum(_messages.Enum):
    r"""For successful job completions, this is the reason why the job has
    finished.

    Values:
      SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set.
      BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached.
      MODEL_CONVERGED: Further training of the Model ceased to increase its
        quality, since it already has converged.
    """
    SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
    BUDGET_REACHED = 1
    MODEL_CONVERGED = 2

  costMilliNodeHours = _messages.IntegerField(1)
  successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentation(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Image Segmentation
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentationInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentationMetadata', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentationInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegm
  entationInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    baseModelId: The ID of the `base` model. If it is specified, the new model
      will be trained based on the `base` model. Otherwise, the new model will
      be trained from scratch. The `base` model must be in the same Project
      and Location as the new Model to train, and have the same modelType.
    budgetMilliNodeHours: The training budget of creating this model,
      expressed in milli node hours i.e. 1,000 value in this field means 1
      node hour. The actual metadata.costMilliNodeHours will be equal or less
      than this value. If further model training ceases to provide any
      improvements, it will stop without using the full budget and the
      metadata.successfulStopReason will be `model-converged`. Note, node_hour
      = actual_hour * number_of_nodes_involved. Or actaul_wall_clock_hours =
      train_budget_milli_node_hours / (number_of_nodes_involved * 1000) For
      modelType `cloud-high-accuracy-1`(default), the budget must be between
      20,000 and 2,000,000 milli node hours, inclusive. The default value is
      192,000 which represents one day in wall time (1000 milli * 24 hours * 8
      nodes).
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD_HIGH_ACCURACY_1: A model to be used via prediction calls to uCAIP
        API. Expected to have a higher latency, but should also have a higher
        prediction quality than other models.
      CLOUD_LOW_ACCURACY_1: A model to be used via prediction calls to uCAIP
        API. Expected to have a lower latency but relatively lower prediction
        quality.
      MOBILE_TF_LOW_LATENCY_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) as TensorFlow model and used on a mobile or
        edge device afterwards. Expected to have low latency, but may have
        lower prediction quality than other mobile models.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD_HIGH_ACCURACY_1 = 1
    CLOUD_LOW_ACCURACY_1 = 2
    MOBILE_TF_LOW_LATENCY_1 = 3

  baseModelId = _messages.StringField(1)
  budgetMilliNodeHours = _messages.IntegerField(2)
  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 3)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegmentationMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlImageSegm
  entationMetadata object.

  Enums:
    SuccessfulStopReasonValueValuesEnum: For successful job completions, this
      is the reason why the job has finished.

  Fields:
    costMilliNodeHours: The actual training cost of creating this model,
      expressed in milli node hours, i.e. 1,000 value in this field means 1
      node hour. Guaranteed to not exceed inputs.budgetMilliNodeHours.
    successfulStopReason: For successful job completions, this is the reason
      why the job has finished.
  """

  class SuccessfulStopReasonValueValuesEnum(_messages.Enum):
    r"""For successful job completions, this is the reason why the job has
    finished.

    Values:
      SUCCESSFUL_STOP_REASON_UNSPECIFIED: Should not be set.
      BUDGET_REACHED: The inputs.budgetMilliNodeHours had been reached.
      MODEL_CONVERGED: Further training of the Model ceased to increase its
        quality, since it already has converged.
    """
    SUCCESSFUL_STOP_REASON_UNSPECIFIED = 0
    BUDGET_REACHED = 1
    MODEL_CONVERGED = 2

  costMilliNodeHours = _messages.IntegerField(1)
  successfulStopReason = _messages.EnumField('SuccessfulStopReasonValueValuesEnum', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTables(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Tables Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputs', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesMetadata', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputs(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputs
  object.

  Fields:
    disableEarlyStopping: Use the entire training budget. This disables the
      early stopping feature. By default, the early stopping feature is
      enabled, which means that AutoML Tables might stop training before the
      entire training budget has been used.
    exportEvaluatedDataItemsConfig: Configuration for exporting test set
      predictions to a BigQuery table. If this configuration is absent, then
      the export is not performed.
    optimizationObjective: Objective function the model is optimizing towards.
      The training process creates a model that maximizes/minimizes the value
      of the objective function over the validation set. The supported
      optimization objectives depend on the prediction type. If the field is
      not set, a default objective function is used. classification (binary):
      "maximize-au-roc" (default) - Maximize the area under the receiver
      operating characteristic (ROC) curve. "minimize-log-loss" - Minimize log
      loss. "maximize-au-prc" - Maximize the area under the precision-recall
      curve. "maximize-precision-at-recall" - Maximize precision for a
      specified recall value. "maximize-recall-at-precision" - Maximize recall
      for a specified precision value. classification (multi-class):
      "minimize-log-loss" (default) - Minimize log loss. regression:
      "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
      "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" -
      Minimize root-mean-squared log error (RMSLE).
    optimizationObjectivePrecisionValue: Required when optimization_objective
      is "maximize-recall-at-precision". Must be between 0 and 1, inclusive.
    optimizationObjectiveRecallValue: Required when optimization_objective is
      "maximize-precision-at-recall". Must be between 0 and 1, inclusive.
    predictionType: The type of prediction the Model is to produce.
      "classification" - Predict one out of multiple target values is picked
      for each row. "regression" - Predict a value based on its relation to
      other values. This type is available only to columns that contain
      semantically numeric values, i.e. integers or floating point number,
      even if stored as e.g. strings.
    targetColumn: The column name of the target column that the model is to
      predict.
    trainBudgetMilliNodeHours: Required. The train budget of creating this
      model, expressed in milli node hours i.e. 1,000 value in this field
      means 1 node hour. The training cost of the model will not exceed this
      budget. The final cost will be attempted to be close to the budget,
      though may end up being (even) noticeably smaller - at the backend's
      discretion. This especially may happen when further model training
      ceases to provide any improvements. If the budget is set to a value
      known to be insufficient to train a model for the given dataset, the
      training won't be attempted and will error. The train budget must be
      between 1,000 and 72,000 milli node hours, inclusive.
    transformations: Each transformation will apply transform function to
      given input column. And the result will be used for training. When
      creating transformation for BigQuery Struct column, the column should be
      flattened using "." as the delimiter.
    weightColumnName: Column name that should be used as the weight column.
      Higher values in this column give more importance to the row during
      model training. The column must have numeric values between 0 and 10000
      inclusively; 0 means the row is ignored for training. If weight column
      field is not set, then all rows are assumed to have equal weight of 1.
  """

  disableEarlyStopping = _messages.BooleanField(1)
  exportEvaluatedDataItemsConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig', 2)
  optimizationObjective = _messages.StringField(3)
  optimizationObjectivePrecisionValue = _messages.FloatField(4, variant=_messages.Variant.FLOAT)
  optimizationObjectiveRecallValue = _messages.FloatField(5, variant=_messages.Variant.FLOAT)
  predictionType = _messages.StringField(6)
  targetColumn = _messages.StringField(7)
  trainBudgetMilliNodeHours = _messages.IntegerField(8)
  transformations = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation', 9, repeated=True)
  weightColumnName = _messages.StringField(10)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformation(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInp
  utsTransformation object.

  Fields:
    auto: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTable
      sInputsTransformationAutoTransformation attribute.
    categorical: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAuto
      MlTablesInputsTransformationCategoricalTransformation attribute.
    numeric: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTa
      blesInputsTransformationNumericTransformation attribute.
    repeatedCategorical: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefini
      tionAutoMlTablesInputsTransformationCategoricalArrayTransformation
      attribute.
    repeatedNumeric: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinition
      AutoMlTablesInputsTransformationNumericArrayTransformation attribute.
    repeatedText: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAut
      oMlTablesInputsTransformationTextArrayTransformation attribute.
    text: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTable
      sInputsTransformationTextTransformation attribute.
    timestamp: A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMl
      TablesInputsTransformationTimestampTransformation attribute.
  """

  auto = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationAutoTransformation', 1)
  categorical = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalTransformation', 2)
  numeric = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericTransformation', 3)
  repeatedCategorical = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalArrayTransformation', 4)
  repeatedNumeric = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericArrayTransformation', 5)
  repeatedText = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextArrayTransformation', 6)
  text = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextTransformation', 7)
  timestamp = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTimestampTransformation', 8)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationAutoTransformation(_messages.Message):
  r"""Training pipeline will infer the proper transformation based on the
  statistic of dataset.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalArrayTransformation(_messages.Message):
  r"""Treats the column as categorical array and performs following
  transformation functions. * For each element in the array, convert the
  category name to a dictionary lookup index and generate an embedding for
  each index. Combine the embedding of all elements into a single embedding
  using the mean. * Empty arrays treated as an embedding of zeroes.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationCategoricalTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  categorical string as is--no change to case, punctuation, spelling, tense,
  and so on. * Convert the category name to a dictionary lookup index and
  generate an embedding for each index. * Categories that appear less than 5
  times in the training dataset are treated as the "unknown" category. The
  "unknown" category gets its own special lookup index and resulting
  embedding.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericArrayTransformation(_messages.Message):
  r"""Treats the column as numerical array and performs following
  transformation functions. * All transformations for Numerical types applied
  to the average of the all elements. * The average of empty arrays is treated
  as zero.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationNumericTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  value converted to float32. * The z_score of the value. * log(value+1) when
  the value is greater than or equal to 0. Otherwise, this transformation is
  not applied and the value is considered a missing value. * z_score of
  log(value+1) when the value is greater than or equal to 0. Otherwise, this
  transformation is not applied and the value is considered a missing value. *
  A boolean value that indicates whether the value is valid.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextArrayTransformation(_messages.Message):
  r"""Treats the column as text array and performs following transformation
  functions. * Concatenate all text values in the array into a single text
  value using a space (" ") as a delimiter, and then treat the result as a
  single text value. Apply the transformations for Text columns. * Empty
  arrays treated as an empty text.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTextTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. * The
  text as is--no change to case, punctuation, spelling, tense, and so on. *
  Tokenize text to words. Convert each words to a dictionary lookup index and
  generate an embedding for each index. Combine the embedding of all elements
  into a single embedding using the mean. * Tokenization is based on unicode
  script boundaries. * Missing values get their own lookup index and resulting
  embedding. * Stop-words receive no special treatment and are not removed.

  Fields:
    columnName: A string attribute.
  """

  columnName = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesInputsTransformationTimestampTransformation(_messages.Message):
  r"""Training pipeline will perform following transformation functions. *
  Apply the transformation functions for Numerical columns. * Determine the
  year, month, day,and weekday. Treat each value from the * timestamp as a
  Categorical column. * Invalid numerical values (for example, values that
  fall outside of a typical timestamp range, or are extreme values) receive no
  special treatment and are not removed.

  Fields:
    columnName: A string attribute.
    invalidValuesAllowed: If invalid values is allowed, the training pipeline
      will create a boolean feature that indicated whether the value is valid.
      Otherwise, the training pipeline will discard the input row from
      trainining data.
    timeFormat: The format in which that time field is expressed. The
      time_format must either be one of: * `unix-seconds` * `unix-
      milliseconds` * `unix-microseconds` * `unix-nanoseconds` (for
      respectively number of seconds, milliseconds, microseconds and
      nanoseconds since start of the Unix epoch); or be written in `strftime`
      syntax. If time_format is not set, then the default format is RFC 3339
      `date-time` format, where `time-offset` = `"Z"` (e.g.
      1985-04-12T23:20:50.52Z)
  """

  columnName = _messages.StringField(1)
  invalidValuesAllowed = _messages.BooleanField(2)
  timeFormat = _messages.StringField(3)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTablesMetadata(_messages.Message):
  r"""Model metadata specific to AutoML Tables.

  Fields:
    trainCostMilliNodeHours: Output only. The actual training cost of the
      model, expressed in milli node hours, i.e. 1,000 value in this field
      means 1 node hour. Guaranteed to not exceed the train budget.
  """

  trainCostMilliNodeHours = _messages.IntegerField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextClassification(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Text Classification
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextClassificationInputs', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextClassificationInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextClass
  ificationInputs object.

  Fields:
    multiLabel: A boolean attribute.
  """

  multiLabel = _messages.BooleanField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextExtraction(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Text Extraction Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextExtractionInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextExtra
  ctionInputs object.
  """



class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextSentiment(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Text Sentiment Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextSentimentInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlTextSenti
  mentInputs object.

  Fields:
    sentimentMax: A sentiment is expressed as an integer ordinal, where higher
      value means a more positive sentiment. The range of sentiments that will
      be used is between 0 and sentimentMax (inclusive on both ends), and all
      the values in the range must be represented in the dataset before a
      model can be created. Only the Annotations with this sentimentMax will
      be used for training. sentimentMax value must be between 1 and 10
      (inclusive).
  """

  sentimentMax = _messages.IntegerField(1, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoActionRecognition(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Video Action Recognition
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoActionRecognitionInputs', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoActionRecognitionInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoActi
  onRecognitionInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD: A model best tailored to be used within Google Cloud, and which c
        annot be exported. Default.
      MOBILE_VERSATILE_1: A model that, in addition to being available within
        Google Cloud, can also be exported (see ModelService.ExportModel) as a
        TensorFlow or TensorFlow Lite model and used on a mobile or edge
        device afterwards.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD = 1
    MOBILE_VERSATILE_1 = 2

  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoClassification(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Video Classification
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoClassificationInputs', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoClassificationInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoClas
  sificationInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD: A model best tailored to be used within Google Cloud, and which
        cannot be exported. Default.
      MOBILE_VERSATILE_1: A model that, in addition to being available within
        Google Cloud, can also be exported (see ModelService.ExportModel) as a
        TensorFlow or TensorFlow Lite model and used on a mobile or edge
        device afterwards.
      MOBILE_JETSON_VERSATILE_1: A model that, in addition to being available
        within Google Cloud, can also be exported (see
        ModelService.ExportModel) to a Jetson device afterwards.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD = 1
    MOBILE_VERSATILE_1 = 2
    MOBILE_JETSON_VERSATILE_1 = 3

  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoObjectTracking(_messages.Message):
  r"""A TrainingJob that trains and uploads an AutoML Video ObjectTracking
  Model.

  Fields:
    inputs: The input parameters of this TrainingJob.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoObjectTrackingInputs', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoObjectTrackingInputs(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionAutoMlVideoObje
  ctTrackingInputs object.

  Enums:
    ModelTypeValueValuesEnum:

  Fields:
    modelType: A ModelTypeValueValuesEnum attribute.
  """

  class ModelTypeValueValuesEnum(_messages.Enum):
    r"""ModelTypeValueValuesEnum enum type.

    Values:
      MODEL_TYPE_UNSPECIFIED: Should not be set.
      CLOUD: A model best tailored to be used within Google Cloud, and which c
        annot be exported. Default.
      MOBILE_VERSATILE_1: A model that, in addition to being available within
        Google Cloud, can also be exported (see ModelService.ExportModel) as a
        TensorFlow or TensorFlow Lite model and used on a mobile or edge
        device afterwards.
      MOBILE_CORAL_VERSATILE_1: A versatile model that is meant to be exported
        (see ModelService.ExportModel) and used on a Google Coral device.
      MOBILE_CORAL_LOW_LATENCY_1: A model that trades off quality for low
        latency, to be exported (see ModelService.ExportModel) and used on a
        Google Coral device.
      MOBILE_JETSON_VERSATILE_1: A versatile model that is meant to be
        exported (see ModelService.ExportModel) and used on an NVIDIA Jetson
        device.
      MOBILE_JETSON_LOW_LATENCY_1: A model that trades off quality for low
        latency, to be exported (see ModelService.ExportModel) and used on an
        NVIDIA Jetson device.
    """
    MODEL_TYPE_UNSPECIFIED = 0
    CLOUD = 1
    MOBILE_VERSATILE_1 = 2
    MOBILE_CORAL_VERSATILE_1 = 3
    MOBILE_CORAL_LOW_LATENCY_1 = 4
    MOBILE_JETSON_VERSATILE_1 = 5
    MOBILE_JETSON_LOW_LATENCY_1 = 6

  modelType = _messages.EnumField('ModelTypeValueValuesEnum', 1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionCustomJobMetadata(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionCustomJobMetadata
  object.

  Fields:
    backingCustomJob: The resource name of the CustomJob that has been created
      to carry out this custom task.
  """

  backingCustomJob = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionCustomTask(_messages.Message):
  r"""A TrainingJob that trains a custom code Model.

  Fields:
    inputs: The input parameters of this CustomTask.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1CustomJobSpec', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionCustomJobMetadata', 2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionExportEvaluatedDataItemsConfig(_messages.Message):
  r"""Configuration for exporting test set predictions to a BigQuery table.

  Fields:
    destinationBigqueryUri: URI of desired destination BigQuery table.
      Expected format: bq://:: If not specified, then results are exported to
      the following auto-created BigQuery table:
      :export_evaluated_examples__.evaluated_examples
    overrideExistingTable: If true and an export destination is specified,
      then the contents of the destination are overwritten. Otherwise, if the
      export destination already exists, then the export operation fails.
  """

  destinationBigqueryUri = _messages.StringField(1)
  overrideExistingTable = _messages.BooleanField(2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningJobMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterT
  uningJobMetadata object.

  Fields:
    backingHyperparameterTuningJob: The resource name of the
      HyperparameterTuningJob that has been created to carry out this
      HyperparameterTuning task.
    bestTrialBackingCustomJob: The resource name of the CustomJob that has
      been created to run the best Trial of this HyperparameterTuning task.
  """

  backingHyperparameterTuningJob = _messages.StringField(1)
  bestTrialBackingCustomJob = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningJobSpec(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterT
  uningJobSpec object.

  Fields:
    maxFailedTrialCount: The number of failed Trials that need to be seen
      before failing the HyperparameterTuningJob. If set to 0, AI Platform
      decides how many Trials must fail before the whole job fails.
    maxTrialCount: The desired total number of Trials.
    parallelTrialCount: The desired number of Trials to run in parallel.
    studySpec: Study configuration of the HyperparameterTuningJob.
    trialJobSpec: The spec of a trial job. The same spec applies to the
      CustomJobs created in all the trials.
  """

  maxFailedTrialCount = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  maxTrialCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  parallelTrialCount = _messages.IntegerField(3, variant=_messages.Variant.INT32)
  studySpec = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpec', 4)
  trialJobSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1CustomJobSpec', 5)


class GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningTask(_messages.Message):
  r"""A TrainingJob that tunes Hypererparameters of a custom code Model.

  Fields:
    inputs: The input parameters of this HyperparameterTuningTask.
    metadata: The metadata information.
  """

  inputs = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningJobSpec', 1)
  metadata = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTrainingjobDefinitionHyperparameterTuningJobMetadata', 2)


class GoogleCloudAiplatformV1beta1SchemaVertex(_messages.Message):
  r"""A vertex represents a 2D point in the image. NOTE: the normalized vertex
  coordinates are relative to the original image and range from 0 to 1.

  Fields:
    x: X coordinate.
    y: Y coordinate.
  """

  x = _messages.FloatField(1)
  y = _messages.FloatField(2)


class GoogleCloudAiplatformV1beta1SchemaVideoActionRecognitionAnnotation(_messages.Message):
  r"""Annotation details specific to video action recognition.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    timeSegment: This Annotation applies to the time period represented by the
      TimeSegment. If it's not set, the Annotation applies to the whole video.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  timeSegment = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTimeSegment', 3)


class GoogleCloudAiplatformV1beta1SchemaVideoClassificationAnnotation(_messages.Message):
  r"""Annotation details specific to video classification.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    timeSegment: This Annotation applies to the time period represented by the
      TimeSegment. If it's not set, the Annotation applies to the whole video.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  timeSegment = _messages.MessageField('GoogleCloudAiplatformV1beta1SchemaTimeSegment', 3)


class GoogleCloudAiplatformV1beta1SchemaVideoDataItem(_messages.Message):
  r"""Payload of Video DataItem.

  Fields:
    gcsUri: Required. Google Cloud Storage URI points to the original video in
      user's bucket. The video is up to 50 GB in size and up to 3 hour in
      duration.
    mimeType: Output only. The mime type of the content of the video. Only the
      videos in below listed mime types are supported. Supported mime_type: -
      video/mp4 - video/avi - video/quicktime
  """

  gcsUri = _messages.StringField(1)
  mimeType = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaVideoDatasetMetadata(_messages.Message):
  r"""The metadata of Datasets that contain Video DataItems.

  Fields:
    dataItemSchemaUri: Points to a YAML file stored on Google Cloud Storage
      describing payload of the Video DataItems that belong to this Dataset.
    gcsBucket: Google Cloud Storage Bucket name that contains the blob data of
      this Dataset.
  """

  dataItemSchemaUri = _messages.StringField(1)
  gcsBucket = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1SchemaVideoObjectTrackingAnnotation(_messages.Message):
  r"""Annotation details specific to video object tracking.

  Fields:
    annotationSpecId: The resource Id of the AnnotationSpec that this
      Annotation pertains to.
    displayName: The display name of the AnnotationSpec that this Annotation
      pertains to.
    instanceId: The instance of the object, expressed as a positive integer.
      Used to track the same object across different frames.
    timeOffset: A time (frame) of a video to which this annotation pertains.
      Represented as the duration since the video's start.
    xMax: The rightmost coordinate of the bounding box.
    xMin: The leftmost coordinate of the bounding box.
    yMax: The bottommost coordinate of the bounding box.
    yMin: The topmost coordinate of the bounding box.
  """

  annotationSpecId = _messages.StringField(1)
  displayName = _messages.StringField(2)
  instanceId = _messages.IntegerField(3)
  timeOffset = _messages.StringField(4)
  xMax = _messages.FloatField(5)
  xMin = _messages.FloatField(6)
  yMax = _messages.FloatField(7)
  yMin = _messages.FloatField(8)


class GoogleCloudAiplatformV1beta1SchemaVisualInspectionClassificationLabelSavedQueryMetadata(_messages.Message):
  r"""A GoogleCloudAiplatformV1beta1SchemaVisualInspectionClassificationLabelS
  avedQueryMetadata object.

  Fields:
    multiLabel: Whether or not the classification label is multi_label.
  """

  multiLabel = _messages.BooleanField(1)


class GoogleCloudAiplatformV1beta1SchemaVisualInspectionMaskSavedQueryMetadata(_messages.Message):
  r"""A
  GoogleCloudAiplatformV1beta1SchemaVisualInspectionMaskSavedQueryMetadata
  object.
  """



class GoogleCloudAiplatformV1beta1SmoothGradConfig(_messages.Message):
  r"""Config for SmoothGrad approximation of gradients. When enabled, the
  gradients are approximated by averaging the gradients from noisy samples in
  the vicinity of the inputs. Adding noise can help improve the computed
  gradients. Refer to this paper for more details:
  https://arxiv.org/pdf/1706.03825.pdf

  Fields:
    featureNoiseSigma: This is similar to noise_sigma, but provides additional
      flexibility. A separate noise sigma can be provided for each feature,
      which is useful if their distributions are different. No noise is added
      to features that are not set. If this field is unset, noise_sigma will
      be used for all features.
    noiseSigma: This is a single float value and will be used to add noise to
      all the features. Use this field when all features are normalized to
      have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring,
      where features are normalized to have 0-mean and 1-variance. For more
      details about normalization: https://tinyurl.com/dgc-normalization. For
      best results the recommended value is about 10% - 20% of the standard
      deviation of the input feature. Refer to section 3.2 of the SmoothGrad
      paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the
      distribution is different per feature, set feature_noise_sigma instead
      for each feature.
    noisySampleCount: The number of gradient samples to use for approximation.
      The higher this number, the more accurate the gradient is, but the
      runtime complexity increases by this factor as well. Valid range of its
      value is [1, 50]. Defaults to 3.
  """

  featureNoiseSigma = _messages.MessageField('GoogleCloudAiplatformV1beta1FeatureNoiseSigma', 1)
  noiseSigma = _messages.FloatField(2, variant=_messages.Variant.FLOAT)
  noisySampleCount = _messages.IntegerField(3, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1SpecialistPool(_messages.Message):
  r"""SpecialistPool represents customers' own workforce to work on their data
  labeling jobs. It includes a group of specialist managers who are
  responsible for managing the labelers in this pool as well as customers'
  data labeling jobs associated with this pool. Customers create specialist
  pool as well as start data labeling jobs on Cloud, managers and labelers
  work with the jobs using CrowdCompute console.

  Fields:
    displayName: Required. The user-defined name of the SpecialistPool. The
      name can be up to 128 characters long and can be consist of any UTF-8
      characters. This field should be unique on project-level.
    name: Required. The resource name of the SpecialistPool.
    pendingDataLabelingJobs: Output only. The resource name of the pending
      data labeling jobs.
    specialistManagerEmails: The email addresses of the specialists in the
      SpecialistPool.
    specialistManagersCount: Output only. The number of Specialists in this
      SpecialistPool.
  """

  displayName = _messages.StringField(1)
  name = _messages.StringField(2)
  pendingDataLabelingJobs = _messages.StringField(3, repeated=True)
  specialistManagerEmails = _messages.StringField(4, repeated=True)
  specialistManagersCount = _messages.IntegerField(5, variant=_messages.Variant.INT32)


class GoogleCloudAiplatformV1beta1StudySpec(_messages.Message):
  r"""Represents specification of a Study.

  Enums:
    AlgorithmValueValuesEnum: The search algorithm specified for the Study.
    MeasurementSelectionTypeValueValuesEnum: Describe which measurement
      selection type will be used
    ObservationNoiseValueValuesEnum: The observation noise level of the study.
      Currently only supported by the Vizier service. Not supported by
      HyperparamterTuningJob or TrainingPipeline.

  Fields:
    algorithm: The search algorithm specified for the Study.
    measurementSelectionType: Describe which measurement selection type will
      be used
    metrics: Required. Metric specs for the Study.
    observationNoise: The observation noise level of the study. Currently only
      supported by the Vizier service. Not supported by HyperparamterTuningJob
      or TrainingPipeline.
    parameters: Required. The set of parameters to tune.
  """

  class AlgorithmValueValuesEnum(_messages.Enum):
    r"""The search algorithm specified for the Study.

    Values:
      ALGORITHM_UNSPECIFIED: The default algorithm used by AI Platform
        Optimization service.
      GRID_SEARCH: Simple grid search within the feasible space. To use grid
        search, all parameters must be `INTEGER`, `CATEGORICAL`, or
        `DISCRETE`.
      RANDOM_SEARCH: Simple random search within the feasible space.
    """
    ALGORITHM_UNSPECIFIED = 0
    GRID_SEARCH = 1
    RANDOM_SEARCH = 2

  class MeasurementSelectionTypeValueValuesEnum(_messages.Enum):
    r"""Describe which measurement selection type will be used

    Values:
      MEASUREMENT_SELECTION_TYPE_UNSPECIFIED: Will be treated as
        LAST_MEASUREMENT.
      LAST_MEASUREMENT: Use the last measurement reported.
      BEST_MEASUREMENT: Use the best measurement reported.
    """
    MEASUREMENT_SELECTION_TYPE_UNSPECIFIED = 0
    LAST_MEASUREMENT = 1
    BEST_MEASUREMENT = 2

  class ObservationNoiseValueValuesEnum(_messages.Enum):
    r"""The observation noise level of the study. Currently only supported by
    the Vizier service. Not supported by HyperparamterTuningJob or
    TrainingPipeline.

    Values:
      OBSERVATION_NOISE_UNSPECIFIED: The default noise level chosen by the AI
        Platform service.
      LOW: AI Platform Vizier assumes that the objective function is (nearly)
        perfectly reproducible, and will never repeat the same Trial
        parameters.
      HIGH: AI Platform Vizier will estimate the amount of noise in metric
        evaluations, it may repeat the same Trial parameters more than once.
    """
    OBSERVATION_NOISE_UNSPECIFIED = 0
    LOW = 1
    HIGH = 2

  algorithm = _messages.EnumField('AlgorithmValueValuesEnum', 1)
  measurementSelectionType = _messages.EnumField('MeasurementSelectionTypeValueValuesEnum', 2)
  metrics = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecMetricSpec', 3, repeated=True)
  observationNoise = _messages.EnumField('ObservationNoiseValueValuesEnum', 4)
  parameters = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpec', 5, repeated=True)


class GoogleCloudAiplatformV1beta1StudySpecMetricSpec(_messages.Message):
  r"""Represents a metric to optimize.

  Enums:
    GoalValueValuesEnum: Required. The optimization goal of the metric.

  Fields:
    goal: Required. The optimization goal of the metric.
    metricId: Required. The ID of the metric. Must not contain whitespaces and
      must be unique amongst all MetricSpecs.
  """

  class GoalValueValuesEnum(_messages.Enum):
    r"""Required. The optimization goal of the metric.

    Values:
      GOAL_TYPE_UNSPECIFIED: Goal Type will default to maximize.
      MAXIMIZE: Maximize the goal metric.
      MINIMIZE: Minimize the goal metric.
    """
    GOAL_TYPE_UNSPECIFIED = 0
    MAXIMIZE = 1
    MINIMIZE = 2

  goal = _messages.EnumField('GoalValueValuesEnum', 1)
  metricId = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpec(_messages.Message):
  r"""Represents a single parameter to optimize.

  Enums:
    ScaleTypeValueValuesEnum: How the parameter should be scaled. Leave unset
      for `CATEGORICAL` parameters.

  Fields:
    categoricalValueSpec: The value spec for a 'CATEGORICAL' parameter.
    conditionalParameterSpecs: A conditional parameter node is active if the
      parameter's value matches the conditional node's parent_value_condition.
      If two items in conditional_parameter_specs have the same name, they
      must have disjoint parent_value_condition.
    discreteValueSpec: The value spec for a 'DISCRETE' parameter.
    doubleValueSpec: The value spec for a 'DOUBLE' parameter.
    integerValueSpec: The value spec for an 'INTEGER' parameter.
    parameterId: Required. The ID of the parameter. Must not contain
      whitespaces and must be unique amongst all ParameterSpecs.
    scaleType: How the parameter should be scaled. Leave unset for
      `CATEGORICAL` parameters.
  """

  class ScaleTypeValueValuesEnum(_messages.Enum):
    r"""How the parameter should be scaled. Leave unset for `CATEGORICAL`
    parameters.

    Values:
      SCALE_TYPE_UNSPECIFIED: By default, no scaling is applied.
      UNIT_LINEAR_SCALE: Scales the feasible space to (0, 1) linearly.
      UNIT_LOG_SCALE: Scales the feasible space logarithmically to (0, 1). The
        entire feasible space must be strictly positive.
      UNIT_REVERSE_LOG_SCALE: Scales the feasible space "reverse"
        logarithmically to (0, 1). The result is that values close to the top
        of the feasible space are spread out more than points near the bottom.
        The entire feasible space must be strictly positive.
    """
    SCALE_TYPE_UNSPECIFIED = 0
    UNIT_LINEAR_SCALE = 1
    UNIT_LOG_SCALE = 2
    UNIT_REVERSE_LOG_SCALE = 3

  categoricalValueSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec', 1)
  conditionalParameterSpecs = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec', 2, repeated=True)
  discreteValueSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec', 3)
  doubleValueSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec', 4)
  integerValueSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec', 5)
  parameterId = _messages.StringField(6)
  scaleType = _messages.EnumField('ScaleTypeValueValuesEnum', 7)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpecCategoricalValueSpec(_messages.Message):
  r"""Value specification for a parameter in `CATEGORICAL` type.

  Fields:
    values: Required. The list of possible categories.
  """

  values = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpec(_messages.Message):
  r"""Represents a parameter spec with condition from its parent parameter.

  Fields:
    parameterSpec: Required. The spec for a conditional parameter.
    parentCategoricalValues: The spec for matching values from a parent
      parameter of `CATEGORICAL` type.
    parentDiscreteValues: The spec for matching values from a parent parameter
      of `DISCRETE` type.
    parentIntValues: The spec for matching values from a parent parameter of
      `INTEGER` type.
  """

  parameterSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpec', 1)
  parentCategoricalValues = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition', 2)
  parentDiscreteValues = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition', 3)
  parentIntValues = _messages.MessageField('GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueCondition', 4)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition(_messages.Message):
  r"""Represents the spec to match categorical values from parent parameter.

  Fields:
    values: Required. Matches values of the parent parameter of 'CATEGORICAL'
      type. All values must exist in `categorical_value_spec` of parent
      parameter.
  """

  values = _messages.StringField(1, repeated=True)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition(_messages.Message):
  r"""Represents the spec to match discrete values from parent parameter.

  Fields:
    values: Required. Matches values of the parent parameter of 'DISCRETE'
      type. All values must exist in `discrete_value_spec` of parent
      parameter. The Epsilon of the value matching is 1e-10.
  """

  values = _messages.FloatField(1, repeated=True)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpecConditionalParameterSpecIntValueCondition(_messages.Message):
  r"""Represents the spec to match integer values from parent parameter.

  Fields:
    values: Required. Matches values of the parent parameter of 'INTEGER'
      type. All values must lie in `integer_value_spec` of parent parameter.
  """

  values = _messages.IntegerField(1, repeated=True)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpecDiscreteValueSpec(_messages.Message):
  r"""Value specification for a parameter in `DISCRETE` type.

  Fields:
    values: Required. A list of possible values. The list should be in
      increasing order and at least 1e-10 apart. For instance, this parameter
      might have possible settings of 1.5, 2.5, and 4.0. This list should not
      contain more than 1,000 values.
  """

  values = _messages.FloatField(1, repeated=True)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpecDoubleValueSpec(_messages.Message):
  r"""Value specification for a parameter in `DOUBLE` type.

  Fields:
    maxValue: Required. Inclusive maximum value of the parameter.
    minValue: Required. Inclusive minimum value of the parameter.
  """

  maxValue = _messages.FloatField(1)
  minValue = _messages.FloatField(2)


class GoogleCloudAiplatformV1beta1StudySpecParameterSpecIntegerValueSpec(_messages.Message):
  r"""Value specification for a parameter in `INTEGER` type.

  Fields:
    maxValue: Required. Inclusive maximum value of the parameter.
    minValue: Required. Inclusive minimum value of the parameter.
  """

  maxValue = _messages.IntegerField(1)
  minValue = _messages.IntegerField(2)


class GoogleCloudAiplatformV1beta1UndeployIndexOperationMetadata(_messages.Message):
  r"""Runtime operation information for IndexEndpointService.UndeployIndex.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1UndeployIndexResponse(_messages.Message):
  r"""Response message for IndexEndpointService.UndeployIndex."""


class GoogleCloudAiplatformV1beta1UndeployModelOperationMetadata(_messages.Message):
  r"""Runtime operation information for EndpointService.UndeployModel.

  Fields:
    genericMetadata: The operation generic information.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1UndeployModelResponse(_messages.Message):
  r"""Response message for EndpointService.UndeployModel."""


class GoogleCloudAiplatformV1beta1UpdateIndexOperationMetadata(_messages.Message):
  r"""Runtime operation information for IndexService.UpdateIndex.

  Fields:
    genericMetadata: The operation generic information.
    nearestNeighborSearchOperationMetadata: The operation metadata with regard
      to ScaNN Index operation.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)
  nearestNeighborSearchOperationMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1NearestNeighborSearchOperationMetadata', 2)


class GoogleCloudAiplatformV1beta1UpdateSpecialistPoolOperationMetadata(_messages.Message):
  r"""Runtime operation metadata for
  SpecialistPoolService.UpdateSpecialistPool.

  Fields:
    genericMetadata: The operation generic information.
    specialistPool: Output only. The name of the SpecialistPool to which the
      specialists are being added. Format: `projects/{project_id}/locations/{l
      ocation_id}/specialistPools/{specialist_pool}`
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)
  specialistPool = _messages.StringField(2)


class GoogleCloudAiplatformV1beta1UploadModelOperationMetadata(_messages.Message):
  r"""Details of ModelService.UploadModel operation.

  Fields:
    genericMetadata: The common part of the operation metadata.
  """

  genericMetadata = _messages.MessageField('GoogleCloudAiplatformV1beta1GenericOperationMetadata', 1)


class GoogleCloudAiplatformV1beta1UploadModelResponse(_messages.Message):
  r"""Response message of ModelService.UploadModel operation.

  Fields:
    model: The name of the uploaded Model resource. Format:
      `projects/{project}/locations/{location}/models/{model}`
  """

  model = _messages.StringField(1)


class GoogleCloudAiplatformV1beta1WorkerPoolSpec(_messages.Message):
  r"""Represents the spec of a worker pool in a job.

  Fields:
    containerSpec: The custom container task.
    diskSpec: Disk spec.
    machineSpec: Optional. Immutable. The specification of a single machine.
    pythonPackageSpec: The Python packaged task.
    replicaCount: Optional. The number of worker replicas to use for this
      worker pool.
  """

  containerSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1ContainerSpec', 1)
  diskSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1DiskSpec', 2)
  machineSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1MachineSpec', 3)
  pythonPackageSpec = _messages.MessageField('GoogleCloudAiplatformV1beta1PythonPackageSpec', 4)
  replicaCount = _messages.IntegerField(5)


class GoogleCloudAiplatformV1beta1XraiAttribution(_messages.Message):
  r"""An explanation method that redistributes Integrated Gradients
  attributions to segmented regions, taking advantage of the model's fully
  differentiable structure. Refer to this paper for more details:
  https://arxiv.org/abs/1906.02825 Supported only by image Models.

  Fields:
    smoothGradConfig: Config for SmoothGrad approximation of gradients. When
      enabled, the gradients are approximated by averaging the gradients from
      noisy samples in the vicinity of the inputs. Adding noise can help
      improve the computed gradients. Refer to this paper for more details:
      https://arxiv.org/pdf/1706.03825.pdf
    stepCount: Required. The number of steps for approximating the path
      integral. A good value to start is 50 and gradually increase until the
      sum to diff property is met within the desired error range. Valid range
      of its value is [1, 100], inclusively.
  """

  smoothGradConfig = _messages.MessageField('GoogleCloudAiplatformV1beta1SmoothGradConfig', 1)
  stepCount = _messages.IntegerField(2, variant=_messages.Variant.INT32)


class GoogleLongrunningListOperationsResponse(_messages.Message):
  r"""The response message for Operations.ListOperations.

  Fields:
    nextPageToken: The standard List next-page token.
    operations: A list of operations that matches the specified filter in the
      request.
  """

  nextPageToken = _messages.StringField(1)
  operations = _messages.MessageField('GoogleLongrunningOperation', 2, repeated=True)


class GoogleLongrunningOperation(_messages.Message):
  r"""This resource represents a long-running operation that is the result of
  a network API call.

  Messages:
    MetadataValue: Service-specific metadata associated with the operation. It
      typically contains progress information and common metadata such as
      create time. Some services might not provide such metadata. Any method
      that returns a long-running operation should document the metadata type,
      if any.
    ResponseValue: The normal response of the operation in case of success. If
      the original method returns no data on success, such as `Delete`, the
      response is `google.protobuf.Empty`. If the original method is standard
      `Get`/`Create`/`Update`, the response should be the resource. For other
      methods, the response should have the type `XxxResponse`, where `Xxx` is
      the original method name. For example, if the original method name is
      `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.

  Fields:
    done: If the value is `false`, it means the operation is still in
      progress. If `true`, the operation is completed, and either `error` or
      `response` is available.
    error: The error result of the operation in case of failure or
      cancellation.
    metadata: Service-specific metadata associated with the operation. It
      typically contains progress information and common metadata such as
      create time. Some services might not provide such metadata. Any method
      that returns a long-running operation should document the metadata type,
      if any.
    name: The server-assigned name, which is only unique within the same
      service that originally returns it. If you use the default HTTP mapping,
      the `name` should be a resource name ending with
      `operations/{unique_id}`.
    response: The normal response of the operation in case of success. If the
      original method returns no data on success, such as `Delete`, the
      response is `google.protobuf.Empty`. If the original method is standard
      `Get`/`Create`/`Update`, the response should be the resource. For other
      methods, the response should have the type `XxxResponse`, where `Xxx` is
      the original method name. For example, if the original method name is
      `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class MetadataValue(_messages.Message):
    r"""Service-specific metadata associated with the operation. It typically
    contains progress information and common metadata such as create time.
    Some services might not provide such metadata. Any method that returns a
    long-running operation should document the metadata type, if any.

    Messages:
      AdditionalProperty: An additional property for a MetadataValue object.

    Fields:
      additionalProperties: Properties of the object. Contains field @type
        with type URL.
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a MetadataValue object.

      Fields:
        key: Name of the additional property.
        value: A extra_types.JsonValue attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('extra_types.JsonValue', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  @encoding.MapUnrecognizedFields('additionalProperties')
  class ResponseValue(_messages.Message):
    r"""The normal response of the operation in case of success. If the
    original method returns no data on success, such as `Delete`, the response
    is `google.protobuf.Empty`. If the original method is standard
    `Get`/`Create`/`Update`, the response should be the resource. For other
    methods, the response should have the type `XxxResponse`, where `Xxx` is
    the original method name. For example, if the original method name is
    `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.

    Messages:
      AdditionalProperty: An additional property for a ResponseValue object.

    Fields:
      additionalProperties: Properties of the object. Contains field @type
        with type URL.
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a ResponseValue object.

      Fields:
        key: Name of the additional property.
        value: A extra_types.JsonValue attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('extra_types.JsonValue', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  done = _messages.BooleanField(1)
  error = _messages.MessageField('GoogleRpcStatus', 2)
  metadata = _messages.MessageField('MetadataValue', 3)
  name = _messages.StringField(4)
  response = _messages.MessageField('ResponseValue', 5)


class GoogleProtobufEmpty(_messages.Message):
  r"""A generic empty message that you can re-use to avoid defining duplicated
  empty messages in your APIs. A typical example is to use it as the request
  or the response type of an API method. For instance: service Foo { rpc
  Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON
  representation for `Empty` is empty JSON object `{}`.
  """



class GoogleRpcStatus(_messages.Message):
  r"""The `Status` type defines a logical error model that is suitable for
  different programming environments, including REST APIs and RPC APIs. It is
  used by [gRPC](https://github.com/grpc). Each `Status` message contains
  three pieces of data: error code, error message, and error details. You can
  find out more about this error model and how to work with it in the [API
  Design Guide](https://cloud.google.com/apis/design/errors).

  Messages:
    DetailsValueListEntry: A DetailsValueListEntry object.

  Fields:
    code: The status code, which should be an enum value of google.rpc.Code.
    details: A list of messages that carry the error details. There is a
      common set of message types for APIs to use.
    message: A developer-facing error message, which should be in English. Any
      user-facing error message should be localized and sent in the
      google.rpc.Status.details field, or localized by the client.
  """

  @encoding.MapUnrecognizedFields('additionalProperties')
  class DetailsValueListEntry(_messages.Message):
    r"""A DetailsValueListEntry object.

    Messages:
      AdditionalProperty: An additional property for a DetailsValueListEntry
        object.

    Fields:
      additionalProperties: Properties of the object. Contains field @type
        with type URL.
    """

    class AdditionalProperty(_messages.Message):
      r"""An additional property for a DetailsValueListEntry object.

      Fields:
        key: Name of the additional property.
        value: A extra_types.JsonValue attribute.
      """

      key = _messages.StringField(1)
      value = _messages.MessageField('extra_types.JsonValue', 2)

    additionalProperties = _messages.MessageField('AdditionalProperty', 1, repeated=True)

  code = _messages.IntegerField(1, variant=_messages.Variant.INT32)
  details = _messages.MessageField('DetailsValueListEntry', 2, repeated=True)
  message = _messages.StringField(3)


class GoogleTypeColor(_messages.Message):
  r"""Represents a color in the RGBA color space. This representation is
  designed for simplicity of conversion to/from color representations in
  various languages over compactness; for example, the fields of this
  representation can be trivially provided to the constructor of
  "java.awt.Color" in Java; it can also be trivially provided to UIColor's
  "+colorWithRed:green:blue:alpha" method in iOS; and, with just a little
  work, it can be easily formatted into a CSS "rgba()" string in JavaScript,
  as well. Note: this proto does not carry information about the absolute
  color space that should be used to interpret the RGB value (e.g. sRGB, Adobe
  RGB, DCI-P3, BT.2020, etc.). By default, applications SHOULD assume the sRGB
  color space. Note: when color equality needs to be decided, implementations,
  unless documented otherwise, will treat two colors to be equal if all their
  red, green, blue and alpha values each differ by at most 1e-5. Example
  (Java): import com.google.type.Color; // ... public static java.awt.Color
  fromProto(Color protocolor) { float alpha = protocolor.hasAlpha() ?
  protocolor.getAlpha().getValue() : 1.0; return new java.awt.Color(
  protocolor.getRed(), protocolor.getGreen(), protocolor.getBlue(), alpha); }
  public static Color toProto(java.awt.Color color) { float red = (float)
  color.getRed(); float green = (float) color.getGreen(); float blue = (float)
  color.getBlue(); float denominator = 255.0; Color.Builder resultBuilder =
  Color .newBuilder() .setRed(red / denominator) .setGreen(green /
  denominator) .setBlue(blue / denominator); int alpha = color.getAlpha(); if
  (alpha != 255) { result.setAlpha( FloatValue .newBuilder()
  .setValue(((float) alpha) / denominator) .build()); } return
  resultBuilder.build(); } // ... Example (iOS / Obj-C): // ... static
  UIColor* fromProto(Color* protocolor) { float red = [protocolor red]; float
  green = [protocolor green]; float blue = [protocolor blue]; FloatValue*
  alpha_wrapper = [protocolor alpha]; float alpha = 1.0; if (alpha_wrapper !=
  nil) { alpha = [alpha_wrapper value]; } return [UIColor colorWithRed:red
  green:green blue:blue alpha:alpha]; } static Color* toProto(UIColor* color)
  { CGFloat red, green, blue, alpha; if (![color getRed:&red green:&green
  blue:&blue alpha:&alpha]) { return nil; } Color* result = [[Color alloc]
  init]; [result setRed:red]; [result setGreen:green]; [result setBlue:blue];
  if (alpha <= 0.9999) { [result setAlpha:floatWrapperWithValue(alpha)]; }
  [result autorelease]; return result; } // ... Example (JavaScript): // ...
  var protoToCssColor = function(rgb_color) { var redFrac = rgb_color.red ||
  0.0; var greenFrac = rgb_color.green || 0.0; var blueFrac = rgb_color.blue
  || 0.0; var red = Math.floor(redFrac * 255); var green =
  Math.floor(greenFrac * 255); var blue = Math.floor(blueFrac * 255); if
  (!('alpha' in rgb_color)) { return rgbToCssColor_(red, green, blue); } var
  alphaFrac = rgb_color.alpha.value || 0.0; var rgbParams = [red, green,
  blue].join(','); return ['rgba(', rgbParams, ',', alphaFrac, ')'].join('');
  }; var rgbToCssColor_ = function(red, green, blue) { var rgbNumber = new
  Number((red << 16) | (green << 8) | blue); var hexString =
  rgbNumber.toString(16); var missingZeros = 6 - hexString.length; var
  resultBuilder = ['#']; for (var i = 0; i < missingZeros; i++) {
  resultBuilder.push('0'); } resultBuilder.push(hexString); return
  resultBuilder.join(''); }; // ...

  Fields:
    alpha: The fraction of this color that should be applied to the pixel.
      That is, the final pixel color is defined by the equation: pixel color =
      alpha * (this color) + (1.0 - alpha) * (background color) This means
      that a value of 1.0 corresponds to a solid color, whereas a value of 0.0
      corresponds to a completely transparent color. This uses a wrapper
      message rather than a simple float scalar so that it is possible to
      distinguish between a default value and the value being unset. If
      omitted, this color object is to be rendered as a solid color (as if the
      alpha value had been explicitly given with a value of 1.0).
    blue: The amount of blue in the color as a value in the interval [0, 1].
    green: The amount of green in the color as a value in the interval [0, 1].
    red: The amount of red in the color as a value in the interval [0, 1].
  """

  alpha = _messages.FloatField(1, variant=_messages.Variant.FLOAT)
  blue = _messages.FloatField(2, variant=_messages.Variant.FLOAT)
  green = _messages.FloatField(3, variant=_messages.Variant.FLOAT)
  red = _messages.FloatField(4, variant=_messages.Variant.FLOAT)


class GoogleTypeMoney(_messages.Message):
  r"""Represents an amount of money with its currency type.

  Fields:
    currencyCode: The three-letter currency code defined in ISO 4217.
    nanos: Number of nano (10^-9) units of the amount. The value must be
      between -999,999,999 and +999,999,999 inclusive. If `units` is positive,
      `nanos` must be positive or zero. If `units` is zero, `nanos` can be
      positive, zero, or negative. If `units` is negative, `nanos` must be
      negative or zero. For example $-1.75 is represented as `units`=-1 and
      `nanos`=-750,000,000.
    units: The whole units of the amount. For example if `currencyCode` is
      `"USD"`, then 1 unit is one US dollar.
  """

  currencyCode = _messages.StringField(1)
  nanos = _messages.IntegerField(2, variant=_messages.Variant.INT32)
  units = _messages.IntegerField(3)


class StandardQueryParameters(_messages.Message):
  r"""Query parameters accepted by all methods.

  Enums:
    FXgafvValueValuesEnum: V1 error format.
    AltValueValuesEnum: Data format for response.

  Fields:
    f__xgafv: V1 error format.
    access_token: OAuth access token.
    alt: Data format for response.
    callback: JSONP
    fields: Selector specifying which fields to include in a partial response.
    key: API key. Your API key identifies your project and provides you with
      API access, quota, and reports. Required unless you provide an OAuth 2.0
      token.
    oauth_token: OAuth 2.0 token for the current user.
    prettyPrint: Returns response with indentations and line breaks.
    quotaUser: Available to use for quota purposes for server-side
      applications. Can be any arbitrary string assigned to a user, but should
      not exceed 40 characters.
    trace: A tracing token of the form "token:<tokenid>" to include in api
      requests.
    uploadType: Legacy upload protocol for media (e.g. "media", "multipart").
    upload_protocol: Upload protocol for media (e.g. "raw", "multipart").
  """

  class AltValueValuesEnum(_messages.Enum):
    r"""Data format for response.

    Values:
      json: Responses with Content-Type of application/json
      media: Media download with context-dependent Content-Type
      proto: Responses with Content-Type of application/x-protobuf
    """
    json = 0
    media = 1
    proto = 2

  class FXgafvValueValuesEnum(_messages.Enum):
    r"""V1 error format.

    Values:
      _1: v1 error format
      _2: v2 error format
    """
    _1 = 0
    _2 = 1

  f__xgafv = _messages.EnumField('FXgafvValueValuesEnum', 1)
  access_token = _messages.StringField(2)
  alt = _messages.EnumField('AltValueValuesEnum', 3, default='json')
  callback = _messages.StringField(4)
  fields = _messages.StringField(5)
  key = _messages.StringField(6)
  oauth_token = _messages.StringField(7)
  prettyPrint = _messages.BooleanField(8, default=True)
  quotaUser = _messages.StringField(9)
  trace = _messages.StringField(10)
  uploadType = _messages.StringField(11)
  upload_protocol = _messages.StringField(12)


encoding.AddCustomJsonFieldMapping(
    StandardQueryParameters, 'f__xgafv', '$.xgafv')
encoding.AddCustomJsonEnumMapping(
    StandardQueryParameters.FXgafvValueValuesEnum, '_1', '1')
encoding.AddCustomJsonEnumMapping(
    StandardQueryParameters.FXgafvValueValuesEnum, '_2', '2')