# Copyright 2018 Google Inc. 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
#
#     https://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 argparse
import os
import time
import logging
import subprocess
import requests

from tensorflow.python.lib.io import file_io  #pylint: disable=no-name-in-module


def main():
  parser = argparse.ArgumentParser(description='ML Trainer')
  parser.add_argument(
      '--model_name',
      help='...',
      required=True)

  parser.add_argument(
      '--model_path',
      help='...',
      required=True)

  parser.add_argument('--cluster', type=str,
                      help='GKE cluster set up for kubeflow. If set, zone must be provided. ' +
                           'If not set, assuming this runs in a GKE container and current ' +
                           'cluster is used.')
  parser.add_argument('--zone', type=str, help='zone of the kubeflow cluster.')
  parser.add_argument('--namespace', type=str, default='default')
  args = parser.parse_args()

  # KUBEFLOW_NAMESPACE = 'kubeflow'

  # Make sure model dir exists before proceeding
  retries = 0
  sleeptime = 5
  while retries < 20:
    try:
      model_dir = os.path.join(args.model_path, file_io.list_directory(args.model_path)[-1])
      print("model subdir: %s" % model_dir)
      break
    except Exception as e:  #pylint: disable=broad-except
      print(e)
      print("Sleeping %s seconds to sync with GCS..." % sleeptime)
      time.sleep(sleeptime)
      retries += 1
      sleeptime *= 2
  if retries >= 20:
    print("could not get model subdir from %s, exiting" % args.model_path)
    exit(1)

  logging.getLogger().setLevel(logging.INFO)
  args_dict = vars(args)
  if args.cluster and args.zone:
    cluster = args_dict.pop('cluster')  #pylint: disable=unused-variable
    zone = args_dict.pop('zone')  #pylint: disable=unused-variable
  else:
    # Get cluster name and zone from metadata
    metadata_server = "http://metadata/computeMetadata/v1/instance/"
    metadata_flavor = {'Metadata-Flavor' : 'Google'}
    cluster = requests.get(metadata_server + "attributes/cluster-name",
                           headers=metadata_flavor).text
    zone = requests.get(metadata_server + "zone",
                        headers=metadata_flavor).text.split('/')[-1]

  # logging.info('Getting credentials for GKE cluster %s.' % cluster)
  # subprocess.call(['gcloud', 'container', 'clusters', 'get-credentials', cluster,
  #                  '--zone', zone])

  logging.info('Generating training template.')

  template_file = os.path.join(
      os.path.dirname(os.path.realpath(__file__)), 'tf-serve-template.yaml')
  target_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'tf-serve.yaml')

  with open(template_file, 'r') as f:
    with open(target_file, "w") as target:
      data = f.read()
      changed = data.replace('MODEL_NAME', args.model_name)
      changed1 = changed.replace('KUBEFLOW_NAMESPACE', args.namespace)
      changed2 = changed1.replace('MODEL_PATH', args.model_path)
      target.write(changed2)


  logging.info('deploying model serving.')
  subprocess.call(['kubectl', 'create', '-f', '/ml/tf-serve.yaml'])


if __name__ == "__main__":
  main()