text: # -*- coding: utf-8 -*-
  
  # Copyright 2020 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  from typing import Dict, Optional, Sequence, Tuple, Union
  
  from google.auth import credentials as auth_credentials
  
  from google.cloud.aiplatform import datasets
  from google.cloud.aiplatform.datasets import _datasources
  from google.cloud.aiplatform import initializer
  from google.cloud.aiplatform import schema
  from google.cloud.aiplatform import utils
  
  
  class TabularDataset(datasets._ColumnNamesDataset):
      """Managed tabular dataset resource for Vertex AI."""
  
      _supported_metadata_schema_uris: Optional[Tuple[str]] = (
          schema.dataset.metadata.tabular,
      )
  
      @classmethod
      def create(
          cls,
          display_name: str,
          gcs_source: Optional[Union[str, Sequence[str]]] = None,
          bq_source: Optional[str] = None,
          project: Optional[str] = None,
          location: Optional[str] = None,
          credentials: Optional[auth_credentials.Credentials] = None,
          request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
          labels: Optional[Dict[str, str]] = None,
          encryption_spec_key_name: Optional[str] = None,
          sync: bool = True,
      ) -> "TabularDataset":
          """Creates a new tabular dataset.
  
          Args:
              display_name (str):
                  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.
              gcs_source (Union[str, Sequence[str]]):
                  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.
                  examples:
                      str: "gs://bucket/file.csv"
                      Sequence[str]: ["gs://bucket/file1.csv", "gs://bucket/file2.csv"]
              bq_source (str):
                  BigQuery URI to the input table.
                  example:
                      "bq://project.dataset.table_name"
              project (str):
                  Project to upload this model to. Overrides project set in
                  aiplatform.init.
              location (str):
                  Location to upload this model to. Overrides location set in
                  aiplatform.init.
              credentials (auth_credentials.Credentials):
                  Custom credentials to use to upload this model. Overrides
                  credentials set in aiplatform.init.
              request_metadata (Sequence[Tuple[str, str]]):
                  Strings which should be sent along with the request as metadata.
              labels (Dict[str, str]):
                  Optional. Labels with user-defined metadata to organize your Tensorboards.
                  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 Tensorboard
                  (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.
              encryption_spec_key_name (Optional[str]):
                  Optional. The Cloud KMS resource identifier of the customer
                  managed encryption key used to protect the dataset. 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.
  
                  If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
  
                  Overrides encryption_spec_key_name set in aiplatform.init.
              sync (bool):
                  Whether to execute this method synchronously. If False, this method
                  will be executed in concurrent Future and any downstream object will
                  be immediately returned and synced when the Future has completed.
  
          Returns:
              tabular_dataset (TabularDataset):
                  Instantiated representation of the managed tabular dataset resource.
          """
  
          utils.validate_display_name(display_name)
          if labels:
              utils.validate_labels(labels)
  
          api_client = cls._instantiate_client(location=location, credentials=credentials)
  
          metadata_schema_uri = schema.dataset.metadata.tabular
  
          datasource = _datasources.create_datasource(
              metadata_schema_uri=metadata_schema_uri,
              gcs_source=gcs_source,
              bq_source=bq_source,
          )
  
          return cls._create_and_import(
              api_client=api_client,
              parent=initializer.global_config.common_location_path(
                  project=project, location=location
              ),
              display_name=display_name,
              metadata_schema_uri=metadata_schema_uri,
              datasource=datasource,
              project=project or initializer.global_config.project,
              location=location or initializer.global_config.location,
              credentials=credentials or initializer.global_config.credentials,
              request_metadata=request_metadata,
              labels=labels,
              encryption_spec=initializer.global_config.get_encryption_spec(
                  encryption_spec_key_name=encryption_spec_key_name
              ),
              sync=sync,
          )
  
      def import_data(self):
          raise NotImplementedError(
              f"{self.__class__.__name__} class does not support 'import_data'"
          )
  