text: # coding=utf-8
  #
  # 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.
  #
  
  from __future__ import absolute_import
  from __future__ import print_function
  
  
  def partition_function(test=None):
    # pylint: disable=line-too-long, expression-not-assigned
    # [START partition_function]
    import apache_beam as beam
  
    durations = ['annual', 'biennial', 'perennial']
  
    def by_duration(plant, num_partitions):
      return durations.index(plant['duration'])
  
    with beam.Pipeline() as pipeline:
      annuals, biennials, perennials = (
          pipeline
          | 'Gardening plants' >> beam.Create([
              {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},
              {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},
              {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},
              {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},
              {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},
          ])
          | 'Partition' >> beam.Partition(by_duration, len(durations))
      )
  
      annuals | 'Annuals' >> beam.Map(lambda x: print('annual: {}'.format(x)))
      biennials | 'Biennials' >> beam.Map(lambda x: print('biennial: {}'.format(x)))
      perennials | 'Perennials' >> beam.Map(lambda x: print('perennial: {}'.format(x)))
      # [END partition_function]
      # pylint: enable=line-too-long, expression-not-assigned
      if test:
        test(annuals, biennials, perennials)
  
  
  def partition_lambda(test=None):
    # pylint: disable=line-too-long, expression-not-assigned
    # [START partition_lambda]
    import apache_beam as beam
  
    durations = ['annual', 'biennial', 'perennial']
  
    with beam.Pipeline() as pipeline:
      annuals, biennials, perennials = (
          pipeline
          | 'Gardening plants' >> beam.Create([
              {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},
              {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},
              {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},
              {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},
              {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},
          ])
          | 'Partition' >> beam.Partition(
              lambda plant, num_partitions: durations.index(plant['duration']),
              len(durations),
          )
      )
  
      annuals | 'Annuals' >> beam.Map(lambda x: print('annual: {}'.format(x)))
      biennials | 'Biennials' >> beam.Map(lambda x: print('biennial: {}'.format(x)))
      perennials | 'Perennials' >> beam.Map(lambda x: print('perennial: {}'.format(x)))
      # [END partition_lambda]
      # pylint: enable=line-too-long, expression-not-assigned
      if test:
        test(annuals, biennials, perennials)
  
  
  def partition_multiple_arguments(test=None):
    # pylint: disable=expression-not-assigned
    # [START partition_multiple_arguments]
    import apache_beam as beam
    import json
  
    def split_dataset(plant, num_partitions, ratio):
      assert num_partitions == len(ratio)
      bucket = sum(map(ord, json.dumps(plant))) % sum(ratio)
      total = 0
      for i, part in enumerate(ratio):
        total += part
        if bucket < total:
          return i
      return len(ratio) - 1
  
    with beam.Pipeline() as pipeline:
      train_dataset, test_dataset = (
          pipeline
          | 'Gardening plants' >> beam.Create([
              {'icon': '🍓', 'name': 'Strawberry', 'duration': 'perennial'},
              {'icon': '🥕', 'name': 'Carrot', 'duration': 'biennial'},
              {'icon': '🍆', 'name': 'Eggplant', 'duration': 'perennial'},
              {'icon': '🍅', 'name': 'Tomato', 'duration': 'annual'},
              {'icon': '🥔', 'name': 'Potato', 'duration': 'perennial'},
          ])
          | 'Partition' >> beam.Partition(split_dataset, 2, ratio=[8, 2])
      )
  
      train_dataset | 'Train' >> beam.Map(lambda x: print('train: {}'.format(x)))
      test_dataset | 'Test'  >> beam.Map(lambda x: print('test: {}'.format(x)))
      # [END partition_multiple_arguments]
      # pylint: enable=expression-not-assigned
      if test:
        test(train_dataset, test_dataset)
  