import argparse
import logging

from common import _utils


def main(argv=None):
  parser = argparse.ArgumentParser(description='ML Trainer')
  parser.add_argument('--project', type=str, help='Google Cloud project ID to use.')
  parser.add_argument('--region', type=str, help='Which zone to run the analyzer.')
  parser.add_argument('--cluster', type=str, help='The name of the cluster to run job.')
  parser.add_argument('--package', type=str,
                      help='GCS Path of XGBoost distributed trainer package.')
  parser.add_argument('--output', type=str, help='GCS path to use for output.')
  parser.add_argument('--conf', type=str, help='GCS path of the training json config file.')
  parser.add_argument('--rounds', type=int, help='Number of rounds to train.')
  parser.add_argument('--workers', type=int, help='Number of workers to use for training.')
  parser.add_argument('--train', type=str, help='GCS path of the training libsvm file pattern.')
  parser.add_argument('--eval', type=str, help='GCS path of the eval libsvm file pattern.')
  parser.add_argument('--analysis', type=str, help='GCS path of the analysis input.')
  parser.add_argument('--target', type=str, help='Target column name.')
  args = parser.parse_args()

  logging.getLogger().setLevel(logging.INFO)
  api = _utils.get_client()
  logging.info('Submitting job...')
  spark_args = [args.conf, str(args.rounds), str(args.workers), args.analysis, args.target,
                args.train, args.eval, args.output]
  job_id = _utils.submit_spark_job(
      api, args.project, args.region, args.cluster, [args.package],
      'ml.dmlc.xgboost4j.scala.example.spark.XGBoostTrainer', spark_args)
  logging.info('Job request submitted. Waiting for completion...')
  _utils.wait_for_job(api, args.project, args.region, job_id)
  with open('/output.txt', 'w') as f:
    f.write(args.output)

  logging.info('Job completed.')


if __name__== "__main__":
  main()
