#
# 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.
#

"""Unit tests for the transform.util classes."""

# pytype: skip-file

from __future__ import absolute_import
from __future__ import division

import logging
import math
import random
import re
import time
import unittest
import warnings
from builtins import object
from builtins import range

# patches unittest.TestCase to be python3 compatible
import future.tests.base  # pylint: disable=unused-import
from nose.plugins.attrib import attr

import apache_beam as beam
from apache_beam import GroupByKey
from apache_beam import Map
from apache_beam import WindowInto
from apache_beam.coders import coders
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import StandardOptions
from apache_beam.portability import common_urns
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.runners import pipeline_context
from apache_beam.testing.test_pipeline import TestPipeline
from apache_beam.testing.test_stream import TestStream
from apache_beam.testing.util import TestWindowedValue
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import contains_in_any_order
from apache_beam.testing.util import equal_to
from apache_beam.transforms import trigger
from apache_beam.transforms import util
from apache_beam.transforms import window
from apache_beam.transforms.core import FlatMapTuple
from apache_beam.transforms.trigger import AfterCount
from apache_beam.transforms.trigger import Repeatedly
from apache_beam.transforms.window import FixedWindows
from apache_beam.transforms.window import GlobalWindow
from apache_beam.transforms.window import GlobalWindows
from apache_beam.transforms.window import IntervalWindow
from apache_beam.transforms.window import Sessions
from apache_beam.transforms.window import SlidingWindows
from apache_beam.transforms.window import TimestampedValue
from apache_beam.typehints import typehints
from apache_beam.typehints.sharded_key_type import ShardedKeyType
from apache_beam.utils import proto_utils
from apache_beam.utils import timestamp
from apache_beam.utils.timestamp import MAX_TIMESTAMP
from apache_beam.utils.timestamp import MIN_TIMESTAMP
from apache_beam.utils.windowed_value import WindowedValue

warnings.filterwarnings(
    'ignore', category=FutureWarning, module='apache_beam.transform.util_test')


class FakeClock(object):
  def __init__(self, now=time.time()):
    self._now = now

  def __call__(self):
    return self._now

  def sleep(self, duration):
    self._now += duration


class BatchElementsTest(unittest.TestCase):
  def test_constant_batch(self):
    # Assumes a single bundle...
    with TestPipeline() as p:
      res = (
          p
          | beam.Create(range(35))
          | util.BatchElements(min_batch_size=10, max_batch_size=10)
          | beam.Map(len))
      assert_that(res, equal_to([10, 10, 10, 5]))

  def test_grows_to_max_batch(self):
    # Assumes a single bundle...
    with TestPipeline() as p:
      res = (
          p
          | beam.Create(range(164))
          | util.BatchElements(
              min_batch_size=1, max_batch_size=50, clock=FakeClock())
          | beam.Map(len))
      assert_that(res, equal_to([1, 1, 2, 4, 8, 16, 32, 50, 50]))

  def test_windowed_batches(self):
    # Assumes a single bundle, in order...
    with TestPipeline() as p:
      res = (
          p
          | beam.Create(range(47), reshuffle=False)
          | beam.Map(lambda t: window.TimestampedValue(t, t))
          | beam.WindowInto(window.FixedWindows(30))
          | util.BatchElements(
              min_batch_size=5, max_batch_size=10, clock=FakeClock())
          | beam.Map(len))
      assert_that(
          res,
          equal_to([
              5,
              5,
              10,
              10,  # elements in [0, 30)
              10,
              7,  # elements in [30, 47)
          ]))

  def test_target_duration(self):
    clock = FakeClock()
    batch_estimator = util._BatchSizeEstimator(
        target_batch_overhead=None, target_batch_duration_secs=10, clock=clock)
    batch_duration = lambda batch_size: 1 + .7 * batch_size
    # 1 + 12 * .7 is as close as we can get to 10 as possible.
    expected_sizes = [1, 2, 4, 8, 12, 12, 12]
    actual_sizes = []
    for _ in range(len(expected_sizes)):
      actual_sizes.append(batch_estimator.next_batch_size())
      with batch_estimator.record_time(actual_sizes[-1]):
        clock.sleep(batch_duration(actual_sizes[-1]))
    self.assertEqual(expected_sizes, actual_sizes)

  def test_target_overhead(self):
    clock = FakeClock()
    batch_estimator = util._BatchSizeEstimator(
        target_batch_overhead=.05, target_batch_duration_secs=None, clock=clock)
    batch_duration = lambda batch_size: 1 + .7 * batch_size
    # At 27 items, a batch takes ~20 seconds with 5% (~1 second) overhead.
    expected_sizes = [1, 2, 4, 8, 16, 27, 27, 27]
    actual_sizes = []
    for _ in range(len(expected_sizes)):
      actual_sizes.append(batch_estimator.next_batch_size())
      with batch_estimator.record_time(actual_sizes[-1]):
        clock.sleep(batch_duration(actual_sizes[-1]))
    self.assertEqual(expected_sizes, actual_sizes)

  def test_variance(self):
    clock = FakeClock()
    variance = 0.25
    batch_estimator = util._BatchSizeEstimator(
        target_batch_overhead=.05,
        target_batch_duration_secs=None,
        variance=variance,
        clock=clock)
    batch_duration = lambda batch_size: 1 + .7 * batch_size
    expected_target = 27
    actual_sizes = []
    for _ in range(util._BatchSizeEstimator._MAX_DATA_POINTS - 1):
      actual_sizes.append(batch_estimator.next_batch_size())
      with batch_estimator.record_time(actual_sizes[-1]):
        clock.sleep(batch_duration(actual_sizes[-1]))
    # Check that we're testing a good range of values.
    stable_set = set(actual_sizes[-20:])
    self.assertGreater(len(stable_set), 3)
    self.assertGreater(
        min(stable_set), expected_target - expected_target * variance)
    self.assertLess(
        max(stable_set), expected_target + expected_target * variance)

  def test_ignore_first_n_batch_size(self):
    clock = FakeClock()
    batch_estimator = util._BatchSizeEstimator(
        clock=clock, ignore_first_n_seen_per_batch_size=2)

    expected_sizes = [
        1, 1, 1, 2, 2, 2, 4, 4, 4, 8, 8, 8, 16, 16, 16, 32, 32, 32, 64, 64, 64
    ]
    actual_sizes = []
    for i in range(len(expected_sizes)):
      actual_sizes.append(batch_estimator.next_batch_size())
      with batch_estimator.record_time(actual_sizes[-1]):
        if i % 3 == 2:
          clock.sleep(0.01)
        else:
          clock.sleep(1)

    self.assertEqual(expected_sizes, actual_sizes)

    # Check we only record the third timing.
    expected_data_batch_sizes = [1, 2, 4, 8, 16, 32, 64]
    actual_data_batch_sizes = [x[0] for x in batch_estimator._data]
    self.assertEqual(expected_data_batch_sizes, actual_data_batch_sizes)
    expected_data_timing = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]
    for i in range(len(expected_data_timing)):
      self.assertAlmostEqual(
          expected_data_timing[i], batch_estimator._data[i][1])

  def test_ignore_next_timing(self):
    clock = FakeClock()
    batch_estimator = util._BatchSizeEstimator(clock=clock)
    batch_estimator.ignore_next_timing()

    expected_sizes = [1, 1, 2, 4, 8, 16]
    actual_sizes = []
    for i in range(len(expected_sizes)):
      actual_sizes.append(batch_estimator.next_batch_size())
      with batch_estimator.record_time(actual_sizes[-1]):
        if i == 0:
          clock.sleep(1)
        else:
          clock.sleep(0.01)

    self.assertEqual(expected_sizes, actual_sizes)

    # Check the first record_time was skipped.
    expected_data_batch_sizes = [1, 2, 4, 8, 16]
    actual_data_batch_sizes = [x[0] for x in batch_estimator._data]
    self.assertEqual(expected_data_batch_sizes, actual_data_batch_sizes)
    expected_data_timing = [0.01, 0.01, 0.01, 0.01, 0.01]
    for i in range(len(expected_data_timing)):
      self.assertAlmostEqual(
          expected_data_timing[i], batch_estimator._data[i][1])

  def _run_regression_test(self, linear_regression_fn, test_outliers):
    xs = [random.random() for _ in range(10)]
    ys = [2 * x + 1 for x in xs]
    a, b = linear_regression_fn(xs, ys)
    self.assertAlmostEqual(a, 1)
    self.assertAlmostEqual(b, 2)

    xs = [1 + random.random() for _ in range(100)]
    ys = [7 * x + 5 + 0.01 * random.random() for x in xs]
    a, b = linear_regression_fn(xs, ys)
    self.assertAlmostEqual(a, 5, delta=0.02)
    self.assertAlmostEqual(b, 7, delta=0.02)

    # Test repeated xs
    xs = [1 + random.random()] * 100
    ys = [7 * x + 5 + 0.01 * random.random() for x in xs]
    a, b = linear_regression_fn(xs, ys)
    self.assertAlmostEqual(a, 0, delta=0.02)
    self.assertAlmostEqual(b, sum(ys) / (len(ys) * xs[0]), delta=0.02)

    if test_outliers:
      xs = [1 + random.random() for _ in range(100)]
      ys = [2 * x + 1 for x in xs]
      a, b = linear_regression_fn(xs, ys)
      self.assertAlmostEqual(a, 1)
      self.assertAlmostEqual(b, 2)

      # An outlier or two doesn't affect the result.
      for _ in range(2):
        xs += [10]
        ys += [30]
        a, b = linear_regression_fn(xs, ys)
        self.assertAlmostEqual(a, 1)
        self.assertAlmostEqual(b, 2)

      # But enough of them, and they're no longer outliers.
      xs += [10] * 10
      ys += [30] * 10
      a, b = linear_regression_fn(xs, ys)
      self.assertLess(a, 0.5)
      self.assertGreater(b, 2.5)

  def test_no_numpy_regression(self):
    self._run_regression_test(
        util._BatchSizeEstimator.linear_regression_no_numpy, False)

  def test_numpy_regression(self):
    try:
      # pylint: disable=wrong-import-order, wrong-import-position
      import numpy as _
    except ImportError:
      self.skipTest('numpy not available')
    self._run_regression_test(
        util._BatchSizeEstimator.linear_regression_numpy, True)


class IdentityWindowTest(unittest.TestCase):
  def test_window_preserved(self):
    expected_timestamp = timestamp.Timestamp(5)
    expected_window = window.IntervalWindow(1.0, 2.0)

    class AddWindowDoFn(beam.DoFn):
      def process(self, element):
        yield WindowedValue(element, expected_timestamp, [expected_window])

    with TestPipeline() as pipeline:
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 4)]
      expected_windows = [
          TestWindowedValue(kv, expected_timestamp, [expected_window])
          for kv in data
      ]
      before_identity = (
          pipeline
          | 'start' >> beam.Create(data)
          | 'add_windows' >> beam.ParDo(AddWindowDoFn()))
      assert_that(
          before_identity,
          equal_to(expected_windows),
          label='before_identity',
          reify_windows=True)
      after_identity = (
          before_identity
          | 'window' >> beam.WindowInto(
              beam.transforms.util._IdentityWindowFn(
                  coders.IntervalWindowCoder())))
      assert_that(
          after_identity,
          equal_to(expected_windows),
          label='after_identity',
          reify_windows=True)

  def test_no_window_context_fails(self):
    expected_timestamp = timestamp.Timestamp(5)
    # Assuming the default window function is window.GlobalWindows.
    expected_window = window.GlobalWindow()

    class AddTimestampDoFn(beam.DoFn):
      def process(self, element):
        yield window.TimestampedValue(element, expected_timestamp)

    with self.assertRaisesRegex(ValueError, r'window.*None.*add_timestamps2'):
      with TestPipeline() as pipeline:
        data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 4)]
        expected_windows = [
            TestWindowedValue(kv, expected_timestamp, [expected_window])
            for kv in data
        ]
        before_identity = (
            pipeline
            | 'start' >> beam.Create(data)
            | 'add_timestamps' >> beam.ParDo(AddTimestampDoFn()))
        assert_that(
            before_identity,
            equal_to(expected_windows),
            label='before_identity',
            reify_windows=True)
        after_identity = (
            before_identity
            | 'window' >> beam.WindowInto(
                beam.transforms.util._IdentityWindowFn(
                    coders.GlobalWindowCoder()))
            # This DoFn will return TimestampedValues, making
            # WindowFn.AssignContext passed to IdentityWindowFn
            # contain a window of None. IdentityWindowFn should
            # raise an exception.
            | 'add_timestamps2' >> beam.ParDo(AddTimestampDoFn()))
        assert_that(
            after_identity,
            equal_to(expected_windows),
            label='after_identity',
            reify_windows=True)


class ReshuffleTest(unittest.TestCase):
  def test_reshuffle_contents_unchanged(self):
    with TestPipeline() as pipeline:
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 3)]
      result = (pipeline | beam.Create(data) | beam.Reshuffle())
      assert_that(result, equal_to(data))

  def test_reshuffle_after_gbk_contents_unchanged(self):
    with TestPipeline() as pipeline:
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 3)]
      expected_result = [(1, [1, 2, 3]), (2, [1, 2]), (3, [1])]

      after_gbk = (
          pipeline
          | beam.Create(data)
          | beam.GroupByKey()
          | beam.MapTuple(lambda k, vs: (k, sorted(vs))))
      assert_that(after_gbk, equal_to(expected_result), label='after_gbk')
      after_reshuffle = after_gbk | beam.Reshuffle()
      assert_that(
          after_reshuffle, equal_to(expected_result), label='after_reshuffle')

  def test_reshuffle_timestamps_unchanged(self):
    with TestPipeline() as pipeline:
      timestamp = 5
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 3)]
      expected_result = [
          TestWindowedValue(v, timestamp, [GlobalWindow()]) for v in data
      ]
      before_reshuffle = (
          pipeline
          | 'start' >> beam.Create(data)
          | 'add_timestamp' >>
          beam.Map(lambda v: beam.window.TimestampedValue(v, timestamp)))
      assert_that(
          before_reshuffle,
          equal_to(expected_result),
          label='before_reshuffle',
          reify_windows=True)
      after_reshuffle = before_reshuffle | beam.Reshuffle()
      assert_that(
          after_reshuffle,
          equal_to(expected_result),
          label='after_reshuffle',
          reify_windows=True)

  def test_reshuffle_windows_unchanged(self):
    with TestPipeline() as pipeline:
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 4)]
      expected_data = [
          TestWindowedValue(v, t - .001, [w])
          for (v, t, w) in [((1, contains_in_any_order([2, 1])),
                             4.0,
                             IntervalWindow(1.0, 4.0)),
                            ((2, contains_in_any_order([2, 1])),
                             4.0,
                             IntervalWindow(1.0, 4.0)), (
                                 (3, [1]), 3.0, IntervalWindow(1.0, 3.0)), (
                                     (1, [4]), 6.0, IntervalWindow(4.0, 6.0))]
      ]
      before_reshuffle = (
          pipeline
          | 'start' >> beam.Create(data)
          | 'add_timestamp' >>
          beam.Map(lambda v: beam.window.TimestampedValue(v, v[1]))
          | 'window' >> beam.WindowInto(Sessions(gap_size=2))
          | 'group_by_key' >> beam.GroupByKey())
      assert_that(
          before_reshuffle,
          equal_to(expected_data),
          label='before_reshuffle',
          reify_windows=True)
      after_reshuffle = before_reshuffle | beam.Reshuffle()
      assert_that(
          after_reshuffle,
          equal_to(expected_data),
          label='after reshuffle',
          reify_windows=True)

  def test_reshuffle_window_fn_preserved(self):
    any_order = contains_in_any_order
    with TestPipeline() as pipeline:
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 4)]
      expected_windows = [
          TestWindowedValue(v, t, [w])
          for (v, t, w) in [((1, 1), 1.0, IntervalWindow(1.0, 3.0)), (
              (2, 1), 1.0, IntervalWindow(1.0, 3.0)), (
                  (3, 1), 1.0, IntervalWindow(1.0, 3.0)), (
                      (1, 2), 2.0, IntervalWindow(2.0, 4.0)), (
                          (2, 2), 2.0,
                          IntervalWindow(2.0, 4.0)), ((1, 4),
                                                      4.0,
                                                      IntervalWindow(4.0, 6.0))]
      ]
      expected_merged_windows = [
          TestWindowedValue(v, t - .001, [w])
          for (v, t,
               w) in [((1, any_order([2, 1])), 4.0, IntervalWindow(1.0, 4.0)), (
                   (2, any_order([2, 1])), 4.0, IntervalWindow(1.0, 4.0)), (
                       (3, [1]), 3.0,
                       IntervalWindow(1.0, 3.0)), ((1, [4]),
                                                   6.0,
                                                   IntervalWindow(4.0, 6.0))]
      ]
      before_reshuffle = (
          pipeline
          | 'start' >> beam.Create(data)
          | 'add_timestamp' >> beam.Map(lambda v: TimestampedValue(v, v[1]))
          | 'window' >> beam.WindowInto(Sessions(gap_size=2)))
      assert_that(
          before_reshuffle,
          equal_to(expected_windows),
          label='before_reshuffle',
          reify_windows=True)
      after_reshuffle = before_reshuffle | beam.Reshuffle()
      assert_that(
          after_reshuffle,
          equal_to(expected_windows),
          label='after_reshuffle',
          reify_windows=True)
      after_group = after_reshuffle | beam.GroupByKey()
      assert_that(
          after_group,
          equal_to(expected_merged_windows),
          label='after_group',
          reify_windows=True)

  def test_reshuffle_global_window(self):
    with TestPipeline() as pipeline:
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 4)]
      expected_data = [(1, [1, 2, 4]), (2, [1, 2]), (3, [1])]
      before_reshuffle = (
          pipeline
          | beam.Create(data)
          | beam.WindowInto(GlobalWindows())
          | beam.GroupByKey()
          | beam.MapTuple(lambda k, vs: (k, sorted(vs))))
      assert_that(
          before_reshuffle, equal_to(expected_data), label='before_reshuffle')
      after_reshuffle = before_reshuffle | beam.Reshuffle()
      assert_that(
          after_reshuffle, equal_to(expected_data), label='after reshuffle')

  def test_reshuffle_sliding_window(self):
    with TestPipeline() as pipeline:
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 4)]
      window_size = 2
      expected_data = [(1, [1, 2, 4]), (2, [1, 2]), (3, [1])] * window_size
      before_reshuffle = (
          pipeline
          | beam.Create(data)
          | beam.WindowInto(SlidingWindows(size=window_size, period=1))
          | beam.GroupByKey()
          | beam.MapTuple(lambda k, vs: (k, sorted(vs))))
      assert_that(
          before_reshuffle, equal_to(expected_data), label='before_reshuffle')
      after_reshuffle = before_reshuffle | beam.Reshuffle()
      # If Reshuffle applies the sliding window function a second time there
      # should be extra values for each key.
      assert_that(
          after_reshuffle, equal_to(expected_data), label='after reshuffle')

  def test_reshuffle_streaming_global_window(self):
    options = PipelineOptions()
    options.view_as(StandardOptions).streaming = True
    with TestPipeline(options=options) as pipeline:
      data = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2), (1, 4)]
      expected_data = [(1, [1, 2, 4]), (2, [1, 2]), (3, [1])]
      before_reshuffle = (
          pipeline
          | beam.Create(data)
          | beam.WindowInto(GlobalWindows())
          | beam.GroupByKey()
          | beam.MapTuple(lambda k, vs: (k, sorted(vs))))
      assert_that(
          before_reshuffle, equal_to(expected_data), label='before_reshuffle')
      after_reshuffle = before_reshuffle | beam.Reshuffle()
      assert_that(
          after_reshuffle, equal_to(expected_data), label='after reshuffle')

  @attr('ValidatesRunner')
  def test_reshuffle_preserves_timestamps(self):
    with TestPipeline() as pipeline:

      # Create a PCollection and assign each element with a different timestamp.
      before_reshuffle = (
          pipeline
          | beam.Create([
              {
                  'name': 'foo', 'timestamp': MIN_TIMESTAMP
              },
              {
                  'name': 'foo', 'timestamp': 0
              },
              {
                  'name': 'bar', 'timestamp': 33
              },
              {
                  'name': 'bar', 'timestamp': 0
              },
          ])
          | beam.Map(
              lambda element: beam.window.TimestampedValue(
                  element, element['timestamp'])))

      # Reshuffle the PCollection above and assign the timestamp of an element
      # to that element again.
      after_reshuffle = before_reshuffle | beam.Reshuffle()

      # Given an element, emits a string which contains the timestamp and the
      # name field of the element.
      def format_with_timestamp(element, timestamp=beam.DoFn.TimestampParam):
        t = str(timestamp)
        if timestamp == MIN_TIMESTAMP:
          t = 'MIN_TIMESTAMP'
        elif timestamp == MAX_TIMESTAMP:
          t = 'MAX_TIMESTAMP'
        return '{} - {}'.format(t, element['name'])

      # Combine each element in before_reshuffle with its timestamp.
      formatted_before_reshuffle = (
          before_reshuffle
          | "Get before_reshuffle timestamp" >> beam.Map(format_with_timestamp))

      # Combine each element in after_reshuffle with its timestamp.
      formatted_after_reshuffle = (
          after_reshuffle
          | "Get after_reshuffle timestamp" >> beam.Map(format_with_timestamp))

      expected_data = [
          'MIN_TIMESTAMP - foo',
          'Timestamp(0) - foo',
          'Timestamp(33) - bar',
          'Timestamp(0) - bar'
      ]

      # Can't compare formatted_before_reshuffle and formatted_after_reshuffle
      # directly, because they are deferred PCollections while equal_to only
      # takes a concrete argument.
      assert_that(
          formatted_before_reshuffle,
          equal_to(expected_data),
          label="formatted_before_reshuffle")
      assert_that(
          formatted_after_reshuffle,
          equal_to(expected_data),
          label="formatted_after_reshuffle")


class WithKeysTest(unittest.TestCase):
  def setUp(self):
    self.l = [1, 2, 3]

  def test_constant_k(self):
    with TestPipeline() as p:
      pc = p | beam.Create(self.l)
      with_keys = pc | util.WithKeys('k')
    assert_that(with_keys, equal_to([('k', 1), ('k', 2), ('k', 3)], ))

  def test_callable_k(self):
    with TestPipeline() as p:
      pc = p | beam.Create(self.l)
      with_keys = pc | util.WithKeys(lambda x: x * x)
    assert_that(with_keys, equal_to([(1, 1), (4, 2), (9, 3)]))


class GroupIntoBatchesTest(unittest.TestCase):
  NUM_ELEMENTS = 10
  BATCH_SIZE = 5

  @staticmethod
  def _create_test_data():
    scientists = [
        "Einstein",
        "Darwin",
        "Copernicus",
        "Pasteur",
        "Curie",
        "Faraday",
        "Newton",
        "Bohr",
        "Galilei",
        "Maxwell"
    ]

    data = []
    for i in range(GroupIntoBatchesTest.NUM_ELEMENTS):
      index = i % len(scientists)
      data.append(("key", scientists[index]))
    return data

  def test_in_global_window(self):
    with TestPipeline() as pipeline:
      collection = pipeline \
                   | beam.Create(GroupIntoBatchesTest._create_test_data()) \
                   | util.GroupIntoBatches(GroupIntoBatchesTest.BATCH_SIZE)
      num_batches = collection | beam.combiners.Count.Globally()
      assert_that(
          num_batches,
          equal_to([
              int(
                  math.ceil(
                      GroupIntoBatchesTest.NUM_ELEMENTS /
                      GroupIntoBatchesTest.BATCH_SIZE))
          ]))

  def test_with_sharded_key_in_global_window(self):
    with TestPipeline() as pipeline:
      collection = (
          pipeline
          | beam.Create(GroupIntoBatchesTest._create_test_data())
          | util.GroupIntoBatches.WithShardedKey(
              GroupIntoBatchesTest.BATCH_SIZE))
      num_batches = collection | beam.combiners.Count.Globally()
      assert_that(
          num_batches,
          equal_to([
              int(
                  math.ceil(
                      GroupIntoBatchesTest.NUM_ELEMENTS /
                      GroupIntoBatchesTest.BATCH_SIZE))
          ]))

  def test_buffering_timer_in_fixed_window_streaming(self):
    window_duration = 6
    max_buffering_duration_secs = 100

    start_time = timestamp.Timestamp(0)
    test_stream = (
        TestStream().add_elements([
            TimestampedValue(value, start_time + i) for i,
            value in enumerate(GroupIntoBatchesTest._create_test_data())
        ]).advance_processing_time(150).advance_watermark_to(
            start_time + window_duration).advance_watermark_to(
                start_time + window_duration +
                1).advance_watermark_to_infinity())

    with TestPipeline(options=StandardOptions(streaming=True)) as pipeline:
      # To trigger the processing time timer, use a fake clock with start time
      # being Timestamp(0).
      fake_clock = FakeClock(now=start_time)

      num_elements_per_batch = (
          pipeline | test_stream
          | "fixed window" >> WindowInto(FixedWindows(window_duration))
          | util.GroupIntoBatches(
              GroupIntoBatchesTest.BATCH_SIZE,
              max_buffering_duration_secs,
              fake_clock)
          | "count elements in batch" >> Map(lambda x: (None, len(x[1])))
          | "global window" >> WindowInto(GlobalWindows())
          | GroupByKey()
          | FlatMapTuple(lambda k, vs: vs))

      # Window duration is 6 and batch size is 5, so output batch size
      # should be 5 (flush because of batch size reached).
      expected_0 = 5
      # There is only one element left in the window so batch size
      # should be 1 (flush because of max buffering duration reached).
      expected_1 = 1
      # Collection has 10 elements, there are only 4 left, so batch size should
      # be 4 (flush because of end of window reached).
      expected_2 = 4
      assert_that(
          num_elements_per_batch,
          equal_to([expected_0, expected_1, expected_2]),
          "assert2")

  def test_buffering_timer_in_global_window_streaming(self):
    max_buffering_duration_secs = 42

    start_time = timestamp.Timestamp(0)
    test_stream = TestStream().advance_watermark_to(start_time)
    for i, value in enumerate(GroupIntoBatchesTest._create_test_data()):
      test_stream.add_elements(
          [TimestampedValue(value, start_time + i)]) \
        .advance_processing_time(5)
    test_stream.advance_watermark_to(
        start_time + GroupIntoBatchesTest.NUM_ELEMENTS + 1) \
      .advance_watermark_to_infinity()

    with TestPipeline(options=StandardOptions(streaming=True)) as pipeline:
      # Set a batch size larger than the total number of elements.
      # Since we're in a global window, we would have been waiting
      # for all the elements to arrive without the buffering time limit.
      batch_size = GroupIntoBatchesTest.NUM_ELEMENTS * 2

      # To trigger the processing time timer, use a fake clock with start time
      # being Timestamp(0). Since the fake clock never really advances during
      # the pipeline execution, meaning that the timer is always set to the same
      # value, the timer will be fired on every element after the first firing.
      fake_clock = FakeClock(now=start_time)

      num_elements_per_batch = (
          pipeline | test_stream
          | WindowInto(
              GlobalWindows(),
              trigger=Repeatedly(AfterCount(1)),
              accumulation_mode=trigger.AccumulationMode.DISCARDING)
          | util.GroupIntoBatches(
              batch_size, max_buffering_duration_secs, fake_clock)
          | 'count elements in batch' >> Map(lambda x: (None, len(x[1])))
          | GroupByKey()
          | FlatMapTuple(lambda k, vs: vs))

      # We will flush twice when the max buffering duration is reached and when
      # the global window ends.
      assert_that(num_elements_per_batch, equal_to([9, 1]))

  def test_output_typehints(self):
    transform = util.GroupIntoBatches.WithShardedKey(
        GroupIntoBatchesTest.BATCH_SIZE)
    unused_input_type = typehints.Tuple[str, str]
    output_type = transform.infer_output_type(unused_input_type)
    self.assertTrue(isinstance(output_type, typehints.TupleConstraint))
    k, v = output_type.tuple_types
    self.assertTrue(isinstance(k, ShardedKeyType))
    self.assertTrue(isinstance(v, typehints.IterableTypeConstraint))

    with TestPipeline() as pipeline:
      collection = (
          pipeline
          | beam.Create([((1, 2), 'a'), ((2, 3), 'b')])
          | util.GroupIntoBatches.WithShardedKey(
              GroupIntoBatchesTest.BATCH_SIZE))
      self.assertTrue(
          collection.element_type,
          typehints.Tuple[
              ShardedKeyType[typehints.Tuple[int, int]],  # type: ignore[misc]
              typehints.Iterable[str]])

  def _test_runner_api_round_trip(self, transform, urn):
    context = pipeline_context.PipelineContext()
    proto = transform.to_runner_api(context)
    self.assertEqual(urn, proto.urn)
    payload = (
        proto_utils.parse_Bytes(
            proto.payload, beam_runner_api_pb2.GroupIntoBatchesPayload))
    self.assertEqual(transform.params.batch_size, payload.batch_size)
    self.assertEqual(
        transform.params.max_buffering_duration_secs * 1000,
        payload.max_buffering_duration_millis)

    transform_from_proto = (
        transform.__class__.from_runner_api_parameter(None, payload, None))
    self.assertTrue(isinstance(transform_from_proto, transform.__class__))
    self.assertEqual(transform.params, transform_from_proto.params)

  def test_runner_api(self):
    batch_size = 10
    max_buffering_duration_secs = [None, 0, 5]

    for duration in max_buffering_duration_secs:
      self._test_runner_api_round_trip(
          util.GroupIntoBatches(batch_size, duration),
          common_urns.group_into_batches_components.GROUP_INTO_BATCHES.urn)
    self._test_runner_api_round_trip(
        util.GroupIntoBatches(batch_size),
        common_urns.group_into_batches_components.GROUP_INTO_BATCHES.urn)

    for duration in max_buffering_duration_secs:
      self._test_runner_api_round_trip(
          util.GroupIntoBatches.WithShardedKey(batch_size, duration),
          common_urns.composites.GROUP_INTO_BATCHES_WITH_SHARDED_KEY.urn)
    self._test_runner_api_round_trip(
        util.GroupIntoBatches.WithShardedKey(batch_size),
        common_urns.composites.GROUP_INTO_BATCHES_WITH_SHARDED_KEY.urn)


class ToStringTest(unittest.TestCase):
  def test_tostring_elements(self):
    with TestPipeline() as p:
      result = (p | beam.Create([1, 1, 2, 3]) | util.ToString.Element())
      assert_that(result, equal_to(["1", "1", "2", "3"]))

  def test_tostring_iterables(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create([("one", "two", "three"), ("four", "five", "six")])
          | util.ToString.Iterables())
      assert_that(result, equal_to(["one,two,three", "four,five,six"]))

  def test_tostring_iterables_with_delimeter(self):
    with TestPipeline() as p:
      data = [("one", "two", "three"), ("four", "five", "six")]
      result = (p | beam.Create(data) | util.ToString.Iterables("\t"))
      assert_that(result, equal_to(["one\ttwo\tthree", "four\tfive\tsix"]))

  def test_tostring_kvs(self):
    with TestPipeline() as p:
      result = (p | beam.Create([("one", 1), ("two", 2)]) | util.ToString.Kvs())
      assert_that(result, equal_to(["one,1", "two,2"]))

  def test_tostring_kvs_delimeter(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create([("one", 1), ("two", 2)]) | util.ToString.Kvs("\t"))
      assert_that(result, equal_to(["one\t1", "two\t2"]))

  def test_tostring_kvs_empty_delimeter(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create([("one", 1), ("two", 2)]) | util.ToString.Kvs(""))
      assert_that(result, equal_to(["one1", "two2"]))


class ReifyTest(unittest.TestCase):
  def test_timestamp(self):
    l = [
        TimestampedValue('a', 100),
        TimestampedValue('b', 200),
        TimestampedValue('c', 300)
    ]
    expected = [
        TestWindowedValue('a', 100, [GlobalWindow()]),
        TestWindowedValue('b', 200, [GlobalWindow()]),
        TestWindowedValue('c', 300, [GlobalWindow()])
    ]
    with TestPipeline() as p:
      # Map(lambda x: x) PTransform is added after Create here, because when
      # a PCollection of TimestampedValues is created with Create PTransform,
      # the timestamps are not assigned to it. Adding a Map forces the
      # PCollection to go through a DoFn so that the PCollection consists of
      # the elements with timestamps assigned to them instead of a PCollection
      # of TimestampedValue(element, timestamp).
      pc = p | beam.Create(l) | beam.Map(lambda x: x)
      reified_pc = pc | util.Reify.Timestamp()
      assert_that(reified_pc, equal_to(expected), reify_windows=True)

  def test_window(self):
    l = [
        GlobalWindows.windowed_value('a', 100),
        GlobalWindows.windowed_value('b', 200),
        GlobalWindows.windowed_value('c', 300)
    ]
    expected = [
        TestWindowedValue(('a', 100, GlobalWindow()), 100, [GlobalWindow()]),
        TestWindowedValue(('b', 200, GlobalWindow()), 200, [GlobalWindow()]),
        TestWindowedValue(('c', 300, GlobalWindow()), 300, [GlobalWindow()])
    ]
    with TestPipeline() as p:
      pc = p | beam.Create(l)
      # Map(lambda x: x) PTransform is added after Create here, because when
      # a PCollection of WindowedValues is created with Create PTransform,
      # the windows are not assigned to it. Adding a Map forces the
      # PCollection to go through a DoFn so that the PCollection consists of
      # the elements with timestamps assigned to them instead of a PCollection
      # of WindowedValue(element, timestamp, window).
      pc = pc | beam.Map(lambda x: x)
      reified_pc = pc | util.Reify.Window()
      assert_that(reified_pc, equal_to(expected), reify_windows=True)

  def test_timestamp_in_value(self):
    l = [
        TimestampedValue(('a', 1), 100),
        TimestampedValue(('b', 2), 200),
        TimestampedValue(('c', 3), 300)
    ]
    expected = [
        TestWindowedValue(('a', TimestampedValue(1, 100)),
                          100, [GlobalWindow()]),
        TestWindowedValue(('b', TimestampedValue(2, 200)),
                          200, [GlobalWindow()]),
        TestWindowedValue(('c', TimestampedValue(3, 300)),
                          300, [GlobalWindow()])
    ]
    with TestPipeline() as p:
      pc = p | beam.Create(l) | beam.Map(lambda x: x)
      reified_pc = pc | util.Reify.TimestampInValue()
      assert_that(reified_pc, equal_to(expected), reify_windows=True)

  def test_window_in_value(self):
    l = [
        GlobalWindows.windowed_value(('a', 1), 100),
        GlobalWindows.windowed_value(('b', 2), 200),
        GlobalWindows.windowed_value(('c', 3), 300)
    ]
    expected = [
        TestWindowedValue(('a', (1, 100, GlobalWindow())),
                          100, [GlobalWindow()]),
        TestWindowedValue(('b', (2, 200, GlobalWindow())),
                          200, [GlobalWindow()]),
        TestWindowedValue(('c', (3, 300, GlobalWindow())),
                          300, [GlobalWindow()])
    ]
    with TestPipeline() as p:
      # Map(lambda x: x) hack is used for the same reason here.
      # Also, this makes the typehint on Reify.WindowInValue work.
      pc = p | beam.Create(l) | beam.Map(lambda x: x)
      reified_pc = pc | util.Reify.WindowInValue()
      assert_that(reified_pc, equal_to(expected), reify_windows=True)


class RegexTest(unittest.TestCase):
  def test_find(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["aj", "xj", "yj", "zj"])
          | util.Regex.find("[xyz]"))
      assert_that(result, equal_to(["x", "y", "z"]))

  def test_find_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("[xyz]")
      result = (p | beam.Create(["aj", "xj", "yj", "zj"]) | util.Regex.find(rc))
      assert_that(result, equal_to(["x", "y", "z"]))

  def test_find_group(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["aj", "xj", "yj", "zj"])
          | util.Regex.find("([xyz])j", group=1))
      assert_that(result, equal_to(["x", "y", "z"]))

  def test_find_empty(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a", "b", "c", "d"])
          | util.Regex.find("[xyz]"))
      assert_that(result, equal_to([]))

  def test_find_group_name(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["aj", "xj", "yj", "zj"])
          | util.Regex.find("(?P<namedgroup>[xyz])j", group="namedgroup"))
      assert_that(result, equal_to(["x", "y", "z"]))

  def test_find_group_name_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("(?P<namedgroup>[xyz])j")
      result = (
          p | beam.Create(["aj", "xj", "yj", "zj"])
          | util.Regex.find(rc, group="namedgroup"))
      assert_that(result, equal_to(["x", "y", "z"]))

  def test_find_all_groups(self):
    data = ["abb ax abbb", "abc qwerty abcabcd xyz"]
    with TestPipeline() as p:
      pcol = (p | beam.Create(data))

      assert_that(
          pcol | 'with default values' >> util.Regex.find_all('a(b*)'),
          equal_to([['abb', 'a', 'abbb'], ['ab', 'ab', 'ab']]),
          label='CheckWithDefaultValues')

      assert_that(
          pcol | 'group 1' >> util.Regex.find_all('a(b*)', 1),
          equal_to([['b', 'b', 'b'], ['bb', '', 'bbb']]),
          label='CheckWithGroup1')

      assert_that(
          pcol | 'group 1 non empty' >> util.Regex.find_all(
              'a(b*)', 1, outputEmpty=False),
          equal_to([['b', 'b', 'b'], ['bb', 'bbb']]),
          label='CheckGroup1NonEmpty')

      assert_that(
          pcol | 'named group' >> util.Regex.find_all(
              'a(?P<namedgroup>b*)', 'namedgroup'),
          equal_to([['b', 'b', 'b'], ['bb', '', 'bbb']]),
          label='CheckNamedGroup')

      assert_that(
          pcol | 'all groups' >> util.Regex.find_all(
              'a(?P<namedgroup>b*)', util.Regex.ALL),
          equal_to([[('ab', 'b'), ('ab', 'b'), ('ab', 'b')],
                    [('abb', 'bb'), ('a', ''), ('abbb', 'bbb')]]),
          label='CheckAllGroups')

      assert_that(
          pcol | 'all non empty groups' >> util.Regex.find_all(
              'a(b*)', util.Regex.ALL, outputEmpty=False),
          equal_to([[('ab', 'b'), ('ab', 'b'), ('ab', 'b')],
                    [('abb', 'bb'), ('abbb', 'bbb')]]),
          label='CheckAllNonEmptyGroups')

  def test_find_kv(self):
    with TestPipeline() as p:
      pcol = (p | beam.Create(['a b c d']))
      assert_that(
          pcol | 'key 1' >> util.Regex.find_kv(
              'a (b) (c)',
              1,
          ),
          equal_to([('b', 'a b c')]),
          label='CheckKey1')

      assert_that(
          pcol | 'key 1 group 1' >> util.Regex.find_kv('a (b) (c)', 1, 2),
          equal_to([('b', 'c')]),
          label='CheckKey1Group1')

  def test_find_kv_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("a (b) (c)")
      result = (p | beam.Create(["a b c"]) | util.Regex.find_kv(rc, 1, 2))
      assert_that(result, equal_to([("b", "c")]))

  def test_find_kv_none(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["x y z"])
          | util.Regex.find_kv("a (b) (c)", 1, 2))
      assert_that(result, equal_to([]))

  def test_match(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a", "x", "y", "z"])
          | util.Regex.matches("[xyz]"))
      assert_that(result, equal_to(["x", "y", "z"]))

    with TestPipeline() as p:
      result = (
          p | beam.Create(["a", "ax", "yby", "zzc"])
          | util.Regex.matches("[xyz]"))
      assert_that(result, equal_to(["y", "z"]))

  def test_match_entire_line(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a", "x", "y", "ay", "zz"])
          | util.Regex.matches("[xyz]$"))
      assert_that(result, equal_to(["x", "y"]))

  def test_match_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("[xyz]")
      result = (p | beam.Create(["a", "x", "y", "z"]) | util.Regex.matches(rc))
      assert_that(result, equal_to(["x", "y", "z"]))

  def test_match_none(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a", "b", "c", "d"])
          | util.Regex.matches("[xyz]"))
      assert_that(result, equal_to([]))

  def test_match_group(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a", "x xxx", "x yyy", "x zzz"])
          | util.Regex.matches("x ([xyz]*)", 1))
      assert_that(result, equal_to(("xxx", "yyy", "zzz")))

  def test_match_group_name(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a", "x xxx", "x yyy", "x zzz"])
          | util.Regex.matches("x (?P<namedgroup>[xyz]*)", 'namedgroup'))
      assert_that(result, equal_to(("xxx", "yyy", "zzz")))

  def test_match_group_name_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("x (?P<namedgroup>[xyz]*)")
      result = (
          p | beam.Create(["a", "x xxx", "x yyy", "x zzz"])
          | util.Regex.matches(rc, 'namedgroup'))
      assert_that(result, equal_to(("xxx", "yyy", "zzz")))

  def test_match_group_empty(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a", "b", "c", "d"])
          | util.Regex.matches("x (?P<namedgroup>[xyz]*)", 'namedgroup'))
      assert_that(result, equal_to([]))

  def test_all_matched(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a x", "x x", "y y", "z z"])
          | util.Regex.all_matches("([xyz]) ([xyz])"))
      expected_result = [["x x", "x", "x"], ["y y", "y", "y"],
                         ["z z", "z", "z"]]
      assert_that(result, equal_to(expected_result))

  def test_all_matched_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("([xyz]) ([xyz])")
      result = (
          p | beam.Create(["a x", "x x", "y y", "z z"])
          | util.Regex.all_matches(rc))
      expected_result = [["x x", "x", "x"], ["y y", "y", "y"],
                         ["z z", "z", "z"]]
      assert_that(result, equal_to(expected_result))

  def test_match_group_kv(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a b c"])
          | util.Regex.matches_kv("a (b) (c)", 1, 2))
      assert_that(result, equal_to([("b", "c")]))

  def test_match_group_kv_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("a (b) (c)")
      pcol = (p | beam.Create(["a b c"]))
      assert_that(
          pcol | 'key 1' >> util.Regex.matches_kv(rc, 1),
          equal_to([("b", "a b c")]),
          label="CheckKey1")

      assert_that(
          pcol | 'key 1 group 2' >> util.Regex.matches_kv(rc, 1, 2),
          equal_to([("b", "c")]),
          label="CheckKey1Group2")

  def test_match_group_kv_none(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["x y z"])
          | util.Regex.matches_kv("a (b) (c)", 1, 2))
      assert_that(result, equal_to([]))

  def test_match_kv_group_names(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["a b c"]) | util.Regex.matches_kv(
              "a (?P<keyname>b) (?P<valuename>c)", 'keyname', 'valuename'))
      assert_that(result, equal_to([("b", "c")]))

  def test_match_kv_group_names_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("a (?P<keyname>b) (?P<valuename>c)")
      result = (
          p | beam.Create(["a b c"])
          | util.Regex.matches_kv(rc, 'keyname', 'valuename'))
      assert_that(result, equal_to([("b", "c")]))

  def test_match_kv_group_name_none(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["x y z"]) | util.Regex.matches_kv(
              "a (?P<keyname>b) (?P<valuename>c)", 'keyname', 'valuename'))
      assert_that(result, equal_to([]))

  def test_replace_all(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["xj", "yj", "zj"])
          | util.Regex.replace_all("[xyz]", "new"))
      assert_that(result, equal_to(["newj", "newj", "newj"]))

  def test_replace_all_mixed(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["abc", "xj", "yj", "zj", "def"])
          | util.Regex.replace_all("[xyz]", 'new'))
      assert_that(result, equal_to(["abc", "newj", "newj", "newj", "def"]))

  def test_replace_all_mixed_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("[xyz]")
      result = (
          p | beam.Create(["abc", "xj", "yj", "zj", "def"])
          | util.Regex.replace_all(rc, 'new'))
      assert_that(result, equal_to(["abc", "newj", "newj", "newj", "def"]))

  def test_replace_first(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["xjx", "yjy", "zjz"])
          | util.Regex.replace_first("[xyz]", 'new'))
      assert_that(result, equal_to(["newjx", "newjy", "newjz"]))

  def test_replace_first_mixed(self):
    with TestPipeline() as p:
      result = (
          p | beam.Create(["abc", "xjx", "yjy", "zjz", "def"])
          | util.Regex.replace_first("[xyz]", 'new'))
      assert_that(result, equal_to(["abc", "newjx", "newjy", "newjz", "def"]))

  def test_replace_first_mixed_pattern(self):
    with TestPipeline() as p:
      rc = re.compile("[xyz]")
      result = (
          p | beam.Create(["abc", "xjx", "yjy", "zjz", "def"])
          | util.Regex.replace_first(rc, 'new'))
      assert_that(result, equal_to(["abc", "newjx", "newjy", "newjz", "def"]))

  def test_split(self):
    with TestPipeline() as p:
      data = ["The  quick   brown fox jumps over    the lazy dog"]
      result = (p | beam.Create(data) | util.Regex.split("\\W+"))
      expected_result = [[
          "The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"
      ]]
      assert_that(result, equal_to(expected_result))

  def test_split_pattern(self):
    with TestPipeline() as p:
      data = ["The  quick   brown fox jumps over    the lazy dog"]
      rc = re.compile("\\W+")
      result = (p | beam.Create(data) | util.Regex.split(rc))
      expected_result = [[
          "The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"
      ]]
      assert_that(result, equal_to(expected_result))

  def test_split_with_empty(self):
    with TestPipeline() as p:
      data = ["The  quick   brown fox jumps over    the lazy dog"]
      result = (p | beam.Create(data) | util.Regex.split("\\s", True))
      expected_result = [[
          'The',
          '',
          'quick',
          '',
          '',
          'brown',
          'fox',
          'jumps',
          'over',
          '',
          '',
          '',
          'the',
          'lazy',
          'dog'
      ]]
      assert_that(result, equal_to(expected_result))

  def test_split_without_empty(self):
    with TestPipeline() as p:
      data = ["The  quick   brown fox jumps over    the lazy dog"]
      result = (p | beam.Create(data) | util.Regex.split("\\s", False))
      expected_result = [[
          "The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"
      ]]
      assert_that(result, equal_to(expected_result))


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