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  """This module contains a Google Cloud Storage to BigQuery operator."""
  
  import json
  import warnings
  from typing import TYPE_CHECKING, Optional, Sequence, Union
  
  from airflow.models import BaseOperator
  from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook
  from airflow.providers.google.cloud.hooks.gcs import GCSHook
  
  if TYPE_CHECKING:
      from airflow.utils.context import Context
  
  
  class GCSToBigQueryOperator(BaseOperator):
      """
      Loads files from Google Cloud Storage into BigQuery.
  
      The schema to be used for the BigQuery table may be specified in one of
      two ways. You may either directly pass the schema fields in, or you may
      point the operator to a Google Cloud Storage object name. The object in
      Google Cloud Storage must be a JSON file with the schema fields in it.
  
      .. seealso::
          For more information on how to use this operator, take a look at the guide:
          :ref:`howto/operator:GCSToBigQueryOperator`
  
      :param bucket: The bucket to load from. (templated)
      :param source_objects: String or List of Google Cloud Storage URIs to load from. (templated)
          If source_format is 'DATASTORE_BACKUP', the list must only contain a single URI.
      :param destination_project_dataset_table: The dotted
          ``(<project>.|<project>:)<dataset>.<table>`` BigQuery table to load data into.
          If ``<project>`` is not included, project will be the project defined in
          the connection json. (templated)
      :param schema_fields: If set, the schema field list as defined here:
          https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.load
          Should not be set when source_format is 'DATASTORE_BACKUP'.
          Parameter must be defined if 'schema_object' is null and autodetect is False.
      :param schema_object: If set, a GCS object path pointing to a .json file that
          contains the schema for the table. (templated)
          Parameter must be defined if 'schema_fields' is null and autodetect is False.
      :param source_format: File format to export.
      :param compression: [Optional] The compression type of the data source.
          Possible values include GZIP and NONE.
          The default value is NONE.
          This setting is ignored for Google Cloud Bigtable,
          Google Cloud Datastore backups and Avro formats.
      :param create_disposition: The create disposition if the table doesn't exist.
      :param skip_leading_rows: Number of rows to skip when loading from a CSV.
      :param write_disposition: The write disposition if the table already exists.
      :param field_delimiter: The delimiter to use when loading from a CSV.
      :param max_bad_records: The maximum number of bad records that BigQuery can
          ignore when running the job.
      :param quote_character: The value that is used to quote data sections in a CSV file.
      :param ignore_unknown_values: [Optional] Indicates if BigQuery should allow
          extra values that are not represented in the table schema.
          If true, the extra values are ignored. If false, records with extra columns
          are treated as bad records, and if there are too many bad records, an
          invalid error is returned in the job result.
      :param allow_quoted_newlines: Whether to allow quoted newlines (true) or not (false).
      :param allow_jagged_rows: Accept rows that are missing trailing optional columns.
          The missing values are treated as nulls. If false, records with missing trailing
          columns are treated as bad records, and if there are too many bad records, an
          invalid error is returned in the job result. Only applicable to CSV, ignored
          for other formats.
      :param encoding: The character encoding of the data. See:
          https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query.tableDefinitions.(key).csvOptions.encoding
          https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#externalDataConfiguration.csvOptions.encoding
      :param max_id_key: If set, the name of a column in the BigQuery table
          that's to be loaded. This will be used to select the MAX value from
          BigQuery after the load occurs. The results will be returned by the
          execute() command, which in turn gets stored in XCom for future
          operators to use. This can be helpful with incremental loads--during
          future executions, you can pick up from the max ID.
      :param bigquery_conn_id: (Optional) The connection ID used to connect to Google Cloud and
          interact with the BigQuery service.
      :param google_cloud_storage_conn_id: (Optional) The connection ID used to connect to Google Cloud
          and interact with the Google Cloud Storage service.
      :param delegate_to: The account to impersonate using domain-wide delegation of authority,
          if any. For this to work, the service account making the request must have
          domain-wide delegation enabled.
      :param schema_update_options: Allows the schema of the destination
          table to be updated as a side effect of the load job.
      :param src_fmt_configs: configure optional fields specific to the source format
      :param external_table: Flag to specify if the destination table should be
          a BigQuery external table. Default Value is False.
      :param time_partitioning: configure optional time partitioning fields i.e.
          partition by field, type and  expiration as per API specifications.
          Note that 'field' is not available in concurrency with
          dataset.table$partition.
      :param cluster_fields: Request that the result of this load be stored sorted
          by one or more columns. BigQuery supports clustering for both partitioned and
          non-partitioned tables. The order of columns given determines the sort order.
          Not applicable for external tables.
      :param autodetect: [Optional] Indicates if we should automatically infer the
          options and schema for CSV and JSON sources. (Default: ``True``).
          Parameter must be set to True if 'schema_fields' and 'schema_object' are undefined.
          It is suggested to set to True if table are create outside of Airflow.
      :param encryption_configuration: [Optional] Custom encryption configuration (e.g., Cloud KMS keys).
          **Example**: ::
  
              encryption_configuration = {
                  "kmsKeyName": "projects/testp/locations/us/keyRings/test-kr/cryptoKeys/test-key"
              }
      :param location: [Optional] The geographic location of the job. Required except for US and EU.
          See details at https://cloud.google.com/bigquery/docs/locations#specifying_your_location
      :param impersonation_chain: Optional service account to impersonate using short-term
          credentials, or chained list of accounts required to get the access_token
          of the last account in the list, which will be impersonated in the request.
          If set as a string, the account must grant the originating account
          the Service Account Token Creator IAM role.
          If set as a sequence, the identities from the list must grant
          Service Account Token Creator IAM role to the directly preceding identity, with first
          account from the list granting this role to the originating account (templated).
      :param labels: [Optional] Labels for the BiqQuery table.
      :param description: [Optional] Description for the BigQuery table.
      """
  
      template_fields: Sequence[str] = (
          'bucket',
          'source_objects',
          'schema_object',
          'destination_project_dataset_table',
          'impersonation_chain',
      )
      template_ext: Sequence[str] = ('.sql',)
      ui_color = '#f0eee4'
  
      def __init__(
          self,
          *,
          bucket,
          source_objects,
          destination_project_dataset_table,
          schema_fields=None,
          schema_object=None,
          source_format='CSV',
          compression='NONE',
          create_disposition='CREATE_IF_NEEDED',
          skip_leading_rows=0,
          write_disposition='WRITE_EMPTY',
          field_delimiter=',',
          max_bad_records=0,
          quote_character=None,
          ignore_unknown_values=False,
          allow_quoted_newlines=False,
          allow_jagged_rows=False,
          encoding="UTF-8",
          max_id_key=None,
          gcp_conn_id='google_cloud_default',
          bigquery_conn_id=None,
          google_cloud_storage_conn_id=None,
          delegate_to=None,
          schema_update_options=(),
          src_fmt_configs=None,
          external_table=False,
          time_partitioning=None,
          cluster_fields=None,
          autodetect=True,
          encryption_configuration=None,
          location=None,
          impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
          labels=None,
          description=None,
          **kwargs,
      ):
          # To preserve backward compatibility. Remove one day
          if bigquery_conn_id or google_cloud_storage_conn_id:
              warnings.warn(
                  "The bigquery_conn_id and google_cloud_storage_conn_id parameters have been deprecated. "
                  "You should pass only gcp_conn_id parameter. "
                  "Will be used bigquery_conn_id or google_cloud_storage_conn_id if gcp_conn_id not passed.",
                  DeprecationWarning,
                  stacklevel=2,
              )
  
          super().__init__(**kwargs)
  
          # GCS config
          if src_fmt_configs is None:
              src_fmt_configs = {}
          if time_partitioning is None:
              time_partitioning = {}
          self.bucket = bucket
          self.source_objects = source_objects
          self.schema_object = schema_object
  
          # BQ config
          self.destination_project_dataset_table = destination_project_dataset_table
          self.schema_fields = schema_fields
          self.source_format = source_format
          self.compression = compression
          self.create_disposition = create_disposition
          self.skip_leading_rows = skip_leading_rows
          self.write_disposition = write_disposition
          self.field_delimiter = field_delimiter
          self.max_bad_records = max_bad_records
          self.quote_character = quote_character
          self.ignore_unknown_values = ignore_unknown_values
          self.allow_quoted_newlines = allow_quoted_newlines
          self.allow_jagged_rows = allow_jagged_rows
          self.external_table = external_table
          self.encoding = encoding
  
          self.max_id_key = max_id_key
          self.gcp_conn_id = gcp_conn_id or bigquery_conn_id or google_cloud_storage_conn_id
          self.delegate_to = delegate_to
  
          self.schema_update_options = schema_update_options
          self.src_fmt_configs = src_fmt_configs
          self.time_partitioning = time_partitioning
          self.cluster_fields = cluster_fields
          self.autodetect = autodetect
          self.encryption_configuration = encryption_configuration
          self.location = location
          self.impersonation_chain = impersonation_chain
  
          self.labels = labels
          self.description = description
  
      def execute(self, context: 'Context'):
          bq_hook = BigQueryHook(
              gcp_conn_id=self.gcp_conn_id,
              delegate_to=self.delegate_to,
              location=self.location,
              impersonation_chain=self.impersonation_chain,
          )
  
          if not self.schema_fields:
              if self.schema_object and self.source_format != 'DATASTORE_BACKUP':
                  gcs_hook = GCSHook(
                      gcp_conn_id=self.gcp_conn_id,
                      delegate_to=self.delegate_to,
                      impersonation_chain=self.impersonation_chain,
                  )
                  blob = gcs_hook.download(
                      bucket_name=self.bucket,
                      object_name=self.schema_object,
                  )
                  schema_fields = json.loads(blob.decode("utf-8"))
              else:
                  schema_fields = None
          else:
              schema_fields = self.schema_fields
  
          self.source_objects = (
              self.source_objects if isinstance(self.source_objects, list) else [self.source_objects]
          )
          source_uris = [f'gs://{self.bucket}/{source_object}' for source_object in self.source_objects]
  
          if self.external_table:
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore", DeprecationWarning)
                  bq_hook.create_external_table(
                      external_project_dataset_table=self.destination_project_dataset_table,
                      schema_fields=schema_fields,
                      source_uris=source_uris,
                      source_format=self.source_format,
                      autodetect=self.autodetect,
                      compression=self.compression,
                      skip_leading_rows=self.skip_leading_rows,
                      field_delimiter=self.field_delimiter,
                      max_bad_records=self.max_bad_records,
                      quote_character=self.quote_character,
                      ignore_unknown_values=self.ignore_unknown_values,
                      allow_quoted_newlines=self.allow_quoted_newlines,
                      allow_jagged_rows=self.allow_jagged_rows,
                      encoding=self.encoding,
                      src_fmt_configs=self.src_fmt_configs,
                      encryption_configuration=self.encryption_configuration,
                      labels=self.labels,
                      description=self.description,
                  )
          else:
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore", DeprecationWarning)
                  bq_hook.run_load(
                      destination_project_dataset_table=self.destination_project_dataset_table,
                      schema_fields=schema_fields,
                      source_uris=source_uris,
                      source_format=self.source_format,
                      autodetect=self.autodetect,
                      create_disposition=self.create_disposition,
                      skip_leading_rows=self.skip_leading_rows,
                      write_disposition=self.write_disposition,
                      field_delimiter=self.field_delimiter,
                      max_bad_records=self.max_bad_records,
                      quote_character=self.quote_character,
                      ignore_unknown_values=self.ignore_unknown_values,
                      allow_quoted_newlines=self.allow_quoted_newlines,
                      allow_jagged_rows=self.allow_jagged_rows,
                      encoding=self.encoding,
                      schema_update_options=self.schema_update_options,
                      src_fmt_configs=self.src_fmt_configs,
                      time_partitioning=self.time_partitioning,
                      cluster_fields=self.cluster_fields,
                      encryption_configuration=self.encryption_configuration,
                      labels=self.labels,
                      description=self.description,
                  )
  
          if self.max_id_key:
              select_command = f'SELECT MAX({self.max_id_key}) FROM `{self.destination_project_dataset_table}`'
              with warnings.catch_warnings():
                  warnings.simplefilter("ignore", DeprecationWarning)
                  job_id = bq_hook.run_query(
                      sql=select_command,
                      use_legacy_sql=False,
                  )
              row = list(bq_hook.get_job(job_id).result())
              if row:
                  max_id = row[0] if row[0] else 0
                  self.log.info(
                      'Loaded BQ data with max %s.%s=%s',
                      self.destination_project_dataset_table,
                      self.max_id_key,
                      max_id,
                  )
              else:
                  raise RuntimeError(f"The {select_command} returned no rows!")
  