# coding=utf-8
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# The ASF licenses this file to You under the Apache License, Version 2.0
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#    http://www.apache.org/licenses/LICENSE-2.0
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# pytype: skip-file

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)
