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  # to you under the Apache License, Version 2.0 (the
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  #
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  # "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.
  """
  Base operator for SQL to GCS operators.
  """
  
  import abc
  import json
  import warnings
  from tempfile import NamedTemporaryFile
  from typing import Optional, Sequence, Union
  
  import unicodecsv as csv
  
  from airflow.models import BaseOperator
  from airflow.providers.google.cloud.hooks.gcs import GCSHook
  from airflow.utils.decorators import apply_defaults
  
  
  class BaseSQLToGCSOperator(BaseOperator):
      """
      :param sql: The SQL to execute.
      :type sql: str
      :param bucket: The bucket to upload to.
      :type bucket: str
      :param filename: The filename to use as the object name when uploading
          to Google Cloud Storage. A ``{}`` should be specified in the filename
          to allow the operator to inject file numbers in cases where the
          file is split due to size.
      :type filename: str
      :param schema_filename: If set, the filename to use as the object name
          when uploading a .json file containing the BigQuery schema fields
          for the table that was dumped from the database.
      :type schema_filename: str
      :param approx_max_file_size_bytes: This operator supports the ability
          to split large table dumps into multiple files (see notes in the
          filename param docs above). This param allows developers to specify the
          file size of the splits. Check https://cloud.google.com/storage/quotas
          to see the maximum allowed file size for a single object.
      :type approx_max_file_size_bytes: long
      :param export_format: Desired format of files to be exported.
      :type export_format: str
      :param field_delimiter: The delimiter to be used for CSV files.
      :type field_delimiter: str
      :param gzip: Option to compress file for upload (does not apply to schemas).
      :type gzip: bool
      :param schema: The schema to use, if any. Should be a list of dict or
          a str. Pass a string if using Jinja template, otherwise, pass a list of
          dict. Examples could be seen: https://cloud.google.com/bigquery/docs
          /schemas#specifying_a_json_schema_file
      :type schema: str or list
      :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud Platform.
      :type gcp_conn_id: str
      :param google_cloud_storage_conn_id: (Deprecated) The connection ID used to connect to Google Cloud
          Platform. This parameter has been deprecated. You should pass the gcp_conn_id parameter instead.
      :type google_cloud_storage_conn_id: str
      :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.
      :type delegate_to: str
      :param parameters: a parameters dict that is substituted at query runtime.
      :type parameters: dict
      :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).
      :type impersonation_chain: Union[str, Sequence[str]]
      """
      template_fields = ('sql', 'bucket', 'filename', 'schema_filename', 'schema', 'parameters',
                         'impersonation_chain',)
      template_ext = ('.sql',)
      ui_color = '#a0e08c'
  
      @apply_defaults
      def __init__(self, *,  # pylint: disable=too-many-arguments
                   sql,
                   bucket,
                   filename,
                   schema_filename=None,
                   approx_max_file_size_bytes=1900000000,
                   export_format='json',
                   field_delimiter=',',
                   gzip=False,
                   schema=None,
                   parameters=None,
                   gcp_conn_id='google_cloud_default',
                   google_cloud_storage_conn_id=None,
                   delegate_to=None,
                   impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
                   **kwargs):
          super().__init__(**kwargs)
  
          if google_cloud_storage_conn_id:
              warnings.warn(
                  "The google_cloud_storage_conn_id parameter has been deprecated. You should pass "
                  "the gcp_conn_id parameter.", DeprecationWarning, stacklevel=3)
              gcp_conn_id = google_cloud_storage_conn_id
  
          self.sql = sql
          self.bucket = bucket
          self.filename = filename
          self.schema_filename = schema_filename
          self.approx_max_file_size_bytes = approx_max_file_size_bytes
          self.export_format = export_format.lower()
          self.field_delimiter = field_delimiter
          self.gzip = gzip
          self.schema = schema
          self.parameters = parameters
          self.gcp_conn_id = gcp_conn_id
          self.delegate_to = delegate_to
          self.impersonation_chain = impersonation_chain
  
      def execute(self, context):
          self.log.info("Executing query")
          cursor = self.query()
  
          self.log.info("Writing local data files")
          files_to_upload = self._write_local_data_files(cursor)
          # If a schema is set, create a BQ schema JSON file.
          if self.schema_filename:
              self.log.info("Writing local schema file")
              files_to_upload.append(self._write_local_schema_file(cursor))
  
          # Flush all files before uploading
          for tmp_file in files_to_upload:
              tmp_file['file_handle'].flush()
  
          self.log.info("Uploading %d files to GCS.", len(files_to_upload))
          self._upload_to_gcs(files_to_upload)
  
          self.log.info("Removing local files")
          # Close all temp file handles.
          for tmp_file in files_to_upload:
              tmp_file['file_handle'].close()
  
      def convert_types(self, schema, col_type_dict, row):
          """Convert values from DBAPI to output-friendly formats."""
          return [
              self.convert_type(value, col_type_dict.get(name))
              for name, value in zip(schema, row)
          ]
  
      def _write_local_data_files(self, cursor):
          """
          Takes a cursor, and writes results to a local file.
  
          :return: A dictionary where keys are filenames to be used as object
              names in GCS, and values are file handles to local files that
              contain the data for the GCS objects.
          """
          schema = list(map(lambda schema_tuple: schema_tuple[0], cursor.description))
          col_type_dict = self._get_col_type_dict()
          file_no = 0
          tmp_file_handle = NamedTemporaryFile(delete=True)
          if self.export_format == 'csv':
              file_mime_type = 'text/csv'
          else:
              file_mime_type = 'application/json'
          files_to_upload = [{
              'file_name': self.filename.format(file_no),
              'file_handle': tmp_file_handle,
              'file_mime_type': file_mime_type
          }]
          self.log.info("Current file count: %d", len(files_to_upload))
  
          if self.export_format == 'csv':
              csv_writer = self._configure_csv_file(tmp_file_handle, schema)
  
          for row in cursor:
              # Convert datetime objects to utc seconds, and decimals to floats.
              # Convert binary type object to string encoded with base64.
              row = self.convert_types(schema, col_type_dict, row)
  
              if self.export_format == 'csv':
                  csv_writer.writerow(row)
              else:
                  row_dict = dict(zip(schema, row))
  
                  tmp_file_handle.write(
                      json.dumps(row_dict, sort_keys=True, ensure_ascii=False).encode("utf-8")
                  )
  
                  # Append newline to make dumps BigQuery compatible.
                  tmp_file_handle.write(b'\n')
  
              # Stop if the file exceeds the file size limit.
              if tmp_file_handle.tell() >= self.approx_max_file_size_bytes:
                  file_no += 1
                  tmp_file_handle = NamedTemporaryFile(delete=True)
                  files_to_upload.append({
                      'file_name': self.filename.format(file_no),
                      'file_handle': tmp_file_handle,
                      'file_mime_type': file_mime_type
                  })
                  self.log.info("Current file count: %d", len(files_to_upload))
                  if self.export_format == 'csv':
                      csv_writer = self._configure_csv_file(tmp_file_handle, schema)
  
          return files_to_upload
  
      def _configure_csv_file(self, file_handle, schema):
          """Configure a csv writer with the file_handle and write schema
          as headers for the new file.
          """
          csv_writer = csv.writer(file_handle, encoding='utf-8',
                                  delimiter=self.field_delimiter)
          csv_writer.writerow(schema)
          return csv_writer
  
      @abc.abstractmethod
      def query(self):
          """Execute DBAPI query."""
  
      @abc.abstractmethod
      def field_to_bigquery(self, field):
          """Convert a DBAPI field to BigQuery schema format."""
  
      @abc.abstractmethod
      def convert_type(self, value, schema_type):
          """Convert a value from DBAPI to output-friendly formats."""
  
      def _get_col_type_dict(self):
          """
          Return a dict of column name and column type based on self.schema if not None.
          """
          schema = []
          if isinstance(self.schema, str):
              schema = json.loads(self.schema)
          elif isinstance(self.schema, list):
              schema = self.schema
          elif self.schema is not None:
              self.log.warning('Using default schema due to unexpected type.'
                               'Should be a string or list.')
  
          col_type_dict = {}
          try:
              col_type_dict = {col['name']: col['type'] for col in schema}
          except KeyError:
              self.log.warning('Using default schema due to missing name or type. Please '
                               'refer to: https://cloud.google.com/bigquery/docs/schemas'
                               '#specifying_a_json_schema_file')
          return col_type_dict
  
      def _write_local_schema_file(self, cursor):
          """
          Takes a cursor, and writes the BigQuery schema for the results to a
          local file system. Schema for database will be read from cursor if
          not specified.
  
          :return: A dictionary where key is a filename to be used as an object
              name in GCS, and values are file handles to local files that
              contains the BigQuery schema fields in .json format.
          """
          if self.schema:
              self.log.info("Using user schema")
              schema = self.schema
          else:
              self.log.info("Starts generating schema")
              schema = [self.field_to_bigquery(field) for field in cursor.description]
  
          if isinstance(schema, list):
              schema = json.dumps(schema, sort_keys=True)
  
          self.log.info('Using schema for %s', self.schema_filename)
          self.log.debug("Current schema: %s", schema)
  
          tmp_schema_file_handle = NamedTemporaryFile(delete=True)
          tmp_schema_file_handle.write(schema.encode('utf-8'))
          schema_file_to_upload = {
              'file_name': self.schema_filename,
              'file_handle': tmp_schema_file_handle,
              'file_mime_type': 'application/json',
          }
          return schema_file_to_upload
  
      def _upload_to_gcs(self, files_to_upload):
          """
          Upload all of the file splits (and optionally the schema .json file) to
          Google Cloud Storage.
          """
          hook = GCSHook(
              gcp_conn_id=self.gcp_conn_id,
              delegate_to=self.delegate_to,
              impersonation_chain=self.impersonation_chain,
          )
          for tmp_file in files_to_upload:
              hook.upload(self.bucket, tmp_file.get('file_name'),
                          tmp_file.get('file_handle').name,
                          mime_type=tmp_file.get('file_mime_type'),
                          gzip=self.gzip if tmp_file.get('file_name') != self.schema_filename else False)
  