#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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
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#

"""A word-counting workflow."""

# pytype: skip-file

from __future__ import absolute_import

import argparse
import logging
import time

import apache_beam as beam
from apache_beam.metrics import Metrics
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.options.pipeline_options import StandardOptions

SLEEP_TIME_SECS = 1

_LOGGER = logging.getLogger(__name__)


class StreamingUserMetricsDoFn(beam.DoFn):
  """Generates user metrics and outputs same element."""
  def __init__(self):
    self.double_message_counter = Metrics.counter(
        self.__class__, 'double_msg_counter_name')
    self.msg_len_dist_metric = Metrics.distribution(
        self.__class__, 'msg_len_dist_metric_name')

  def start_bundle(self):
    time.sleep(SLEEP_TIME_SECS)

  def process(self, element):
    """Returns the processed element and increments the metrics."""

    text_line = element.strip()

    self.double_message_counter.inc()
    self.double_message_counter.inc()
    self.msg_len_dist_metric.update(len(text_line))

    _LOGGER.debug("Done processing returning element array: '%s'", element)

    return [element]

  def finish_bundle(self):
    time.sleep(SLEEP_TIME_SECS)


def run(argv=None):
  """Given an initialized Pipeline applies transforms and runs it."""

  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--output_topic',
      required=True,
      help=(
          'Output PubSub topic of the form '
          '"projects/<PROJECT>/topic/<TOPIC>".'))
  group = parser.add_mutually_exclusive_group(required=True)
  group.add_argument(
      '--input_topic',
      help=(
          'Input PubSub topic of the form '
          '"projects/<PROJECT>/topics/<TOPIC>".'))
  group.add_argument(
      '--input_subscription',
      help=(
          'Input PubSub subscription of the form '
          '"projects/<PROJECT>/subscriptions/<SUBSCRIPTION>."'))
  known_args, pipeline_args = parser.parse_known_args(argv)

  pipeline_options = PipelineOptions(pipeline_args)
  pipeline_options.view_as(SetupOptions).save_main_session = True
  pipeline_options.view_as(StandardOptions).streaming = True
  pipeline = beam.Pipeline(options=pipeline_options)

  _ = (
      pipeline
      | beam.io.ReadFromPubSub(subscription=known_args.input_subscription)
      | 'generate_metrics' >> (beam.ParDo(StreamingUserMetricsDoFn()))
      | 'dump_to_pub' >> beam.io.WriteToPubSub(known_args.output_topic))

  result = pipeline.run()
  result.wait_until_finish()
  return result


if __name__ == '__main__':
  logging.getLogger().setLevel(logging.INFO)
  run()