# Copyright 2022 The Kubeflow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import logging

import google.auth
import google.auth.transport.requests
from google_cloud_pipeline_components.container.v1.bigquery.utils import bigquery_util
from google_cloud_pipeline_components.container.v1.gcp_launcher.utils import artifact_util
from google_cloud_pipeline_components.types.artifact_types import BQMLModel
import requests


def bigquery_ml_reconstruction_loss_job(
    type,
    project,
    location,
    model_name,
    table_name,
    query_statement,
    payload,
    job_configuration_query_override,
    gcp_resources,
    executor_input,
):
  """Create and poll BigQuery ML Reconstruction Loss job till it reaches a final state.

  This follows the typical launching logic:
  1. Read if the BigQuery job already exists in gcp_resources
     - If already exists, jump to step 3 and poll the job status. This happens
     if the launcher container experienced unexpected termination, such as
     preemption
  2. Deserialize the payload into the job spec and create the bigquery job
  3. Poll the BigQuery job status every
  job_remote_runner._POLLING_INTERVAL_IN_SECONDS seconds
     - If the bigquery job is succeeded, return succeeded
     - If the bigquery job is pending/running, continue polling the status

  Also retry on ConnectionError up to
  job_remote_runner._CONNECTION_ERROR_RETRY_LIMIT times during the poll.


  Args:
      type: BigQuery ML Reconstruction Loss job type.
      project: Project to launch the query job.
      location: location to launch the query job. For more details, see
        https://cloud.google.com/bigquery/docs/locations#specifying_your_location
      model_name: BigQuery ML model name. For more details, see
        https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-reconstruction-loss#reconstruction_loss_model_name
      table_name: BigQuery table id of the input table that contains the input
        data. For more details, see
        https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-reconstruction-loss#reconstruction_loss_table_name
      query_statement: query statement string used to generate the input data.
        For more details, see
        https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-reconstruction-loss#reconstruction_loss_query_statement
      payload: A json serialized Job proto. For more details, see
        https://cloud.google.com/bigquery/docs/reference/rest/v2/Job
      job_configuration_query_override: A json serialized JobConfigurationQuery
        proto. For more details, see
        https://cloud.google.com/bigquery/docs/reference/rest/v2/Job#JobConfigurationQuery
      gcp_resources: File path for storing `gcp_resources` output parameter.
      executor_input: A json serialized pipeline executor input.
  """
  if not (not query_statement) ^ (not table_name):
    raise ValueError(
        'One and only one of query_statement and table_name should be '
        'populated for BigQuery ML Reconstruction Loss job.'
    )

  input_data_sql = ''
  if table_name:
    input_data_sql = ', TABLE %s' % bigquery_util.back_quoted_if_needed(
        table_name
    )
  if query_statement:
    input_data_sql = ', (%s)' % query_statement

  job_configuration_query_override_json = json.loads(
      job_configuration_query_override, strict=False
  )
  job_configuration_query_override_json['query'] = (
      'SELECT * FROM ML.RECONSTRUCTION_LOSS(MODEL %s%s)'
      % (bigquery_util.back_quoted_if_needed(model_name), input_data_sql)
  )

  # For ML reconstruction loss job, as the returned results is the same as the
  # number of input, which can be very large. In this case we would like to ask
  # users to insert a destination table into the job config.
  return bigquery_util.bigquery_query_job(
      type,
      project,
      location,
      payload,
      json.dumps(job_configuration_query_override_json),
      gcp_resources,
      executor_input,
  )
