new_contents: #
  # 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
  # limitations under the License.
  #
  
  """A word-counting workflow."""
  
  # pytype: skip-file
  
  from __future__ import absolute_import
  
  import argparse
  import logging
  import re
  
  from past.builtins import unicode
  
  import apache_beam as beam
  from apache_beam.io import ReadFromText
  from apache_beam.io import WriteToText
  from apache_beam.metrics import Metrics
  from apache_beam.metrics.metric import MetricsFilter
  from apache_beam.options.pipeline_options import PipelineOptions
  from apache_beam.options.pipeline_options import SetupOptions
  
  
  class WordExtractingDoFn(beam.DoFn):
    """Parse each line of input text into words."""
    def __init__(self):
      # TODO(BEAM-6158): Revert the workaround once we can pickle super() on py3.
      # super(WordExtractingDoFn, self).__init__()
      beam.DoFn.__init__(self)
      self.words_counter = Metrics.counter(self.__class__, 'words')
      self.word_lengths_counter = Metrics.counter(self.__class__, 'word_lengths')
      self.word_lengths_dist = Metrics.distribution(
          self.__class__, 'word_len_dist')
      self.empty_line_counter = Metrics.counter(self.__class__, 'empty_lines')
  
    def process(self, element):
      """Returns an iterator over the words of this element.
  
      The element is a line of text.  If the line is blank, note that, too.
  
      Args:
        element: the element being processed
  
      Returns:
        The processed element.
      """
      text_line = element.strip()
      if not text_line:
        self.empty_line_counter.inc(1)
      words = re.findall(r'[\w\']+', text_line, re.UNICODE)
      for w in words:
        self.words_counter.inc()
        self.word_lengths_counter.inc(len(w))
        self.word_lengths_dist.update(len(w))
      return words
  
  
  def run(argv=None, save_main_session=True):
    """Main entry point; defines and runs the wordcount pipeline."""
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        dest='input',
        default='gs://dataflow-samples/shakespeare/kinglear.txt',
        help='Input file to process.')
    parser.add_argument(
        '--output',
        dest='output',
        required=True,
        help='Output file to write results to.')
    known_args, pipeline_args = parser.parse_known_args(argv)
  
    # We use the save_main_session option because one or more DoFn's in this
    # workflow rely on global context (e.g., a module imported at module level).
    pipeline_options = PipelineOptions(pipeline_args)
    pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
    p = beam.Pipeline(options=pipeline_options)
  
    # Read the text file[pattern] into a PCollection.
    lines = p | 'read' >> ReadFromText(known_args.input)
  
    # Count the occurrences of each word.
    def count_ones(word_ones):
      (word, ones) = word_ones
      return (word, sum(ones))
  
    counts = (
        lines
        | 'split' >>
        (beam.ParDo(WordExtractingDoFn()).with_output_types(unicode))
        | 'pair_with_one' >> beam.Map(lambda x: (x, 1))
        | 'group' >> beam.GroupByKey()
        | 'count' >> beam.Map(count_ones))
  
    # Format the counts into a PCollection of strings.
    def format_result(word_count):
      (word, count) = word_count
      return '%s: %d' % (word, count)
  
    output = counts | 'format' >> beam.Map(format_result)
  
    # Write the output using a "Write" transform that has side effects.
    # pylint: disable=expression-not-assigned
    output | 'write' >> WriteToText(known_args.output)
  
    result = p.run()
    result.wait_until_finish()
  
    # Do not query metrics when creating a template which doesn't run
    if (not hasattr(result, 'has_job')  # direct runner
        or result.has_job):  # not just a template creation
      empty_lines_filter = MetricsFilter().with_name('empty_lines')
      query_result = result.metrics().query(empty_lines_filter)
      if query_result['counters']:
        empty_lines_counter = query_result['counters'][0]
        logging.info('number of empty lines: %d', empty_lines_counter.result)
  
      word_lengths_filter = MetricsFilter().with_name('word_len_dist')
      query_result = result.metrics().query(word_lengths_filter)
      if query_result['distributions']:
        word_lengths_dist = query_result['distributions'][0]
        logging.info('average word length: %d', word_lengths_dist.result.mean)
  
  
  if __name__ == '__main__':
    logging.getLogger().setLevel(logging.INFO)
    run()
  
subject: Move wordcount with metrics to its own file.