#
# 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 Pipeline class."""

# pytype: skip-file

import copy
import platform
import unittest
import uuid

import mock
import pytest

import apache_beam as beam
from apache_beam import coders
from apache_beam import typehints
from apache_beam.coders import BytesCoder
from apache_beam.io import Read
from apache_beam.io.iobase import SourceBase
from apache_beam.options.pipeline_options import PortableOptions
from apache_beam.pipeline import Pipeline
from apache_beam.pipeline import PipelineOptions
from apache_beam.pipeline import PipelineVisitor
from apache_beam.pipeline import PTransformOverride
from apache_beam.portability import common_urns
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.pvalue import AsSingleton
from apache_beam.pvalue import TaggedOutput
from apache_beam.runners.runner import PipelineRunner
from apache_beam.testing.test_pipeline import TestPipeline
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import equal_to
from apache_beam.transforms import CombineGlobally
from apache_beam.transforms import Create
from apache_beam.transforms import DoFn
from apache_beam.transforms import FlatMap
from apache_beam.transforms import Map
from apache_beam.transforms import ParDo
from apache_beam.transforms import PTransform
from apache_beam.transforms import WindowInto
from apache_beam.transforms.display import DisplayDataItem
from apache_beam.transforms.environments import ProcessEnvironment
from apache_beam.transforms.resources import ResourceHint
from apache_beam.transforms.userstate import BagStateSpec
from apache_beam.transforms.window import FixedWindows
from apache_beam.transforms.window import IntervalWindow
from apache_beam.transforms.window import SlidingWindows
from apache_beam.transforms.window import TimestampedValue
from apache_beam.typehints import TypeCheckError
from apache_beam.utils import windowed_value
from apache_beam.utils.timestamp import MIN_TIMESTAMP


class FakeUnboundedSource(SourceBase):
  """Fake unbounded source. Does not work at runtime"""
  def is_bounded(self):
    return False


class DoubleParDo(beam.PTransform):
  def expand(self, input):
    return input | 'Inner' >> beam.Map(lambda a: a * 2)

  def to_runner_api_parameter(self, context):
    return self.to_runner_api_pickled(context)


class TripleParDo(beam.PTransform):
  def expand(self, input):
    # Keeping labels the same intentionally to make sure that there is no label
    # conflict due to replacement.
    return input | 'Inner' >> beam.Map(lambda a: a * 3)


class ToStringParDo(beam.PTransform):
  def expand(self, input):
    # We use copy.copy() here to make sure the typehint mechanism doesn't
    # automatically infer that the output type is str.
    return input | 'Inner' >> beam.Map(lambda a: copy.copy(str(a)))


class FlattenAndDouble(beam.PTransform):
  def expand(self, pcolls):
    return pcolls | beam.Flatten() | 'Double' >> DoubleParDo()


class FlattenAndTriple(beam.PTransform):
  def expand(self, pcolls):
    return pcolls | beam.Flatten() | 'Triple' >> TripleParDo()


class AddWithProductDoFn(beam.DoFn):
  def process(self, input, a, b):
    yield input + a * b


class AddThenMultiplyDoFn(beam.DoFn):
  def process(self, input, a, b):
    yield (input + a) * b


class AddThenMultiply(beam.PTransform):
  def expand(self, pvalues):
    return pvalues[0] | beam.ParDo(
        AddThenMultiplyDoFn(), AsSingleton(pvalues[1]), AsSingleton(pvalues[2]))


class PipelineTest(unittest.TestCase):
  @staticmethod
  def custom_callable(pcoll):
    return pcoll | '+1' >> FlatMap(lambda x: [x + 1])

  # Some of these tests designate a runner by name, others supply a runner.
  # This variation is just to verify that both means of runner specification
  # work and is not related to other aspects of the tests.

  class CustomTransform(PTransform):
    def expand(self, pcoll):
      return pcoll | '+1' >> FlatMap(lambda x: [x + 1])

  class Visitor(PipelineVisitor):
    def __init__(self, visited):
      self.visited = visited
      self.enter_composite = []
      self.leave_composite = []

    def visit_value(self, value, _):
      self.visited.append(value)

    def enter_composite_transform(self, transform_node):
      self.enter_composite.append(transform_node)

    def leave_composite_transform(self, transform_node):
      self.leave_composite.append(transform_node)

  def test_create(self):
    with TestPipeline() as pipeline:
      pcoll = pipeline | 'label1' >> Create([1, 2, 3])
      assert_that(pcoll, equal_to([1, 2, 3]))

      # Test if initial value is an iterator object.
      pcoll2 = pipeline | 'label2' >> Create(iter((4, 5, 6)))
      pcoll3 = pcoll2 | 'do' >> FlatMap(lambda x: [x + 10])
      assert_that(pcoll3, equal_to([14, 15, 16]), label='pcoll3')

  def test_unexpected_PDone_errmsg(self):
    """
    Test that a nice error message is raised if a transform that
    returns None (i.e. produces no PCollection) is used as input
    to a PTransform.
    """
    class DoNothingTransform(PTransform):
      def expand(self, pcoll):
        return None

    class ParentTransform(PTransform):
      def expand(self, pcoll):
        return pcoll | DoNothingTransform()

    with pytest.raises(TypeCheckError, match=r".*applied to the output"):
      with TestPipeline() as pipeline:
        _ = pipeline | ParentTransform() | beam.Map(lambda x: x + 1)

  @mock.patch('logging.info')
  @pytest.mark.uses_dill
  def test_runner_overrides_default_pickler(self, mock_info):
    pytest.importorskip("dill")
    with mock.patch.object(PipelineRunner,
                           'default_pickle_library_override') as mock_fn:
      mock_fn.return_value = 'dill'
      with TestPipeline() as pipeline:
        pcoll = pipeline | 'label1' >> Create([1, 2, 3])
        assert_that(pcoll, equal_to([1, 2, 3]))

        from apache_beam.internal import dill_pickler
        from apache_beam.internal import pickler
        self.assertIs(pickler.desired_pickle_lib, dill_pickler)
    mock_info.assert_any_call(
        'Runner defaulting to pickling library: %s.', 'dill')

  def test_flatmap_builtin(self):
    with TestPipeline() as pipeline:
      pcoll = pipeline | 'label1' >> Create([1, 2, 3])
      assert_that(pcoll, equal_to([1, 2, 3]))

      pcoll2 = pcoll | 'do' >> FlatMap(lambda x: [x + 10])
      assert_that(pcoll2, equal_to([11, 12, 13]), label='pcoll2')

      pcoll3 = pcoll2 | 'm1' >> Map(lambda x: [x, 12])
      assert_that(
          pcoll3, equal_to([[11, 12], [12, 12], [13, 12]]), label='pcoll3')

      pcoll4 = pcoll3 | 'do2' >> FlatMap(set)
      assert_that(pcoll4, equal_to([11, 12, 12, 12, 13]), label='pcoll4')

  def test_maptuple_builtin(self):
    with TestPipeline() as pipeline:
      pcoll = pipeline | Create([('e1', 'e2')])
      side1 = beam.pvalue.AsSingleton(pipeline | 'side1' >> Create(['s1']))
      side2 = beam.pvalue.AsSingleton(pipeline | 'side2' >> Create(['s2']))

      # A test function with a tuple input, an auxiliary parameter,
      # and some side inputs.
      fn = lambda e1, e2, t=DoFn.TimestampParam, s1=None, s2=None: (
          e1, e2, t, s1, s2)
      assert_that(
          pcoll | 'NoSides' >> beam.core.MapTuple(fn),
          equal_to([('e1', 'e2', MIN_TIMESTAMP, None, None)]),
          label='NoSidesCheck')
      assert_that(
          pcoll | 'StaticSides' >> beam.core.MapTuple(fn, 's1', 's2'),
          equal_to([('e1', 'e2', MIN_TIMESTAMP, 's1', 's2')]),
          label='StaticSidesCheck')
      assert_that(
          pcoll | 'DynamicSides' >> beam.core.MapTuple(fn, side1, side2),
          equal_to([('e1', 'e2', MIN_TIMESTAMP, 's1', 's2')]),
          label='DynamicSidesCheck')
      assert_that(
          pcoll | 'MixedSides' >> beam.core.MapTuple(fn, s2=side2),
          equal_to([('e1', 'e2', MIN_TIMESTAMP, None, 's2')]),
          label='MixedSidesCheck')

  def test_flatmaptuple_builtin(self):
    with TestPipeline() as pipeline:
      pcoll = pipeline | Create([('e1', 'e2')])
      side1 = beam.pvalue.AsSingleton(pipeline | 'side1' >> Create(['s1']))
      side2 = beam.pvalue.AsSingleton(pipeline | 'side2' >> Create(['s2']))

      # A test function with a tuple input, an auxiliary parameter,
      # and some side inputs.
      fn = lambda e1, e2, t=DoFn.TimestampParam, s1=None, s2=None: (
          e1, e2, t, s1, s2)
      assert_that(
          pcoll | 'NoSides' >> beam.core.FlatMapTuple(fn),
          equal_to(['e1', 'e2', MIN_TIMESTAMP, None, None]),
          label='NoSidesCheck')
      assert_that(
          pcoll | 'StaticSides' >> beam.core.FlatMapTuple(fn, 's1', 's2'),
          equal_to(['e1', 'e2', MIN_TIMESTAMP, 's1', 's2']),
          label='StaticSidesCheck')
      assert_that(
          pcoll
          | 'DynamicSides' >> beam.core.FlatMapTuple(fn, side1, side2),
          equal_to(['e1', 'e2', MIN_TIMESTAMP, 's1', 's2']),
          label='DynamicSidesCheck')
      assert_that(
          pcoll | 'MixedSides' >> beam.core.FlatMapTuple(fn, s2=side2),
          equal_to(['e1', 'e2', MIN_TIMESTAMP, None, 's2']),
          label='MixedSidesCheck')

  def test_create_singleton_pcollection(self):
    with TestPipeline() as pipeline:
      pcoll = pipeline | 'label' >> Create([[1, 2, 3]])
      assert_that(pcoll, equal_to([[1, 2, 3]]))

  def test_visit_entire_graph(self):
    pipeline = Pipeline()
    pcoll1 = pipeline | 'pcoll' >> beam.Impulse()
    pcoll2 = pcoll1 | 'do1' >> FlatMap(lambda x: [x + 1])
    pcoll3 = pcoll2 | 'do2' >> FlatMap(lambda x: [x + 1])
    pcoll4 = pcoll2 | 'do3' >> FlatMap(lambda x: [x + 1])
    transform = PipelineTest.CustomTransform()
    pcoll5 = pcoll4 | transform

    visitor = PipelineTest.Visitor(visited=[])
    pipeline.visit(visitor)
    self.assertEqual({pcoll1, pcoll2, pcoll3, pcoll4, pcoll5},
                     set(visitor.visited))
    self.assertEqual(set(visitor.enter_composite), set(visitor.leave_composite))
    self.assertEqual(2, len(visitor.enter_composite))
    self.assertEqual(visitor.enter_composite[1].transform, transform)
    self.assertEqual(visitor.leave_composite[0].transform, transform)

  def test_apply_custom_transform(self):
    with TestPipeline() as pipeline:
      pcoll = pipeline | 'pcoll' >> Create([1, 2, 3])
      result = pcoll | PipelineTest.CustomTransform()
      assert_that(result, equal_to([2, 3, 4]))

  def test_reuse_custom_transform_instance(self):
    pipeline = Pipeline()
    pcoll1 = pipeline | 'pcoll1' >> Create([1, 2, 3])
    pcoll2 = pipeline | 'pcoll2' >> Create([4, 5, 6])
    transform = PipelineTest.CustomTransform()
    pcoll1 | transform
    with self.assertRaises(RuntimeError) as cm:
      pipeline.apply(transform, pcoll2)
    self.assertEqual(
        cm.exception.args[0],
        'A transform with label "CustomTransform" already exists in the '
        'pipeline. To apply a transform with a specified label, write '
        'pvalue | "label" >> transform or use the option '
        '"auto_unique_labels" to automatically generate unique '
        'transform labels. Note "auto_unique_labels" '
        'could cause data loss when updating a pipeline or '
        'reloading the job state. This is not recommended for '
        'streaming jobs.')

  @mock.patch('logging.info')  # Mock the logging.info function
  def test_no_wait_until_finish(self, mock_info):
    with Pipeline(runner='DirectRunner',
                  options=PipelineOptions(["--no_wait_until_finish"])) as p:
      _ = p | beam.Create(['test'])
    mock_info.assert_any_call(
        'Job execution continues without waiting for completion. '
        'Use "wait_until_finish" in PipelineResult to block until finished.')
    p.result.wait_until_finish()

  def test_auto_unique_labels(self):

    opts = PipelineOptions(["--auto_unique_labels"])

    mock_uuids = [mock.Mock(hex='UUID01XXX'), mock.Mock(hex='UUID02XXX')]
    mock_uuid_gen = mock.Mock(side_effect=mock_uuids)

    original_generate_unique_label = Pipeline._generate_unique_label

    def patched_generate_unique_label(self, transform):
      with mock.patch.object(uuid, 'uuid4', return_value=mock_uuid_gen()):
        return original_generate_unique_label(self, transform)

    with mock.patch.object(Pipeline,
                           '_generate_unique_label',
                           patched_generate_unique_label):
      with TestPipeline(options=opts) as pipeline:
        pcoll = pipeline | 'pcoll' >> Create([1, 2, 3])

        def identity(x):
          return x

        pcoll2 = pcoll | Map(identity)
        pcoll3 = pcoll2 | Map(identity)
        pcoll4 = pcoll3 | Map(identity)
        assert_that(pcoll4, equal_to([1, 2, 3]))

    map_id_full_labels = {
        label
        for label in pipeline.applied_labels if "Map(identity)" in label
    }
    map_id_leaf_labels = {label.split(":")[-1] for label in map_id_full_labels}
    # Only the first 6 chars of the UUID hex should be used
    assert map_id_leaf_labels == set(
        ["Map(identity)", "Map(identity)_UUID01", "Map(identity)_UUID02"])

  def test_reuse_cloned_custom_transform_instance(self):
    with TestPipeline() as pipeline:
      pcoll1 = pipeline | 'pc1' >> Create([1, 2, 3])
      pcoll2 = pipeline | 'pc2' >> Create([4, 5, 6])
      transform = PipelineTest.CustomTransform()
      result1 = pcoll1 | transform
      result2 = pcoll2 | 'new_label' >> transform
      assert_that(result1, equal_to([2, 3, 4]), label='r1')
      assert_that(result2, equal_to([5, 6, 7]), label='r2')

  def test_transform_no_super_init(self):
    class AddSuffix(PTransform):
      def __init__(self, suffix):
        # No call to super(...).__init__
        self.suffix = suffix

      def expand(self, pcoll):
        return pcoll | Map(lambda x: x + self.suffix)

    self.assertEqual(['a-x', 'b-x', 'c-x'],
                     sorted(['a', 'b', 'c'] | 'AddSuffix' >> AddSuffix('-x')))

  @unittest.skip("Fails on some platforms with new urllib3.")
  def test_memory_usage(self):
    try:
      import resource
    except ImportError:
      # Skip the test if resource module is not available (e.g. non-Unix os).
      self.skipTest('resource module not available.')
    if platform.mac_ver()[0]:
      # Skip the test on macos, depending on version it returns ru_maxrss in
      # different units.
      self.skipTest('ru_maxrss is not in standard units.')

    def get_memory_usage_in_bytes():
      return resource.getrusage(resource.RUSAGE_SELF).ru_maxrss * (2**10)

    def check_memory(value, memory_threshold):
      memory_usage = get_memory_usage_in_bytes()
      if memory_usage > memory_threshold:
        raise RuntimeError(
            'High memory usage: %d > %d' % (memory_usage, memory_threshold))
      return value

    len_elements = 1000000
    num_elements = 10
    num_maps = 100

    # TODO(robertwb): reduce memory usage of FnApiRunner so that this test
    # passes.
    with TestPipeline(runner='BundleBasedDirectRunner') as pipeline:

      # Consumed memory should not be proportional to the number of maps.
      memory_threshold = (
          get_memory_usage_in_bytes() + (5 * len_elements * num_elements))

      # Plus small additional slack for memory fluctuations during the test.
      memory_threshold += 10 * (2**20)

      biglist = pipeline | 'oom:create' >> Create(
          ['x' * len_elements] * num_elements)
      for i in range(num_maps):
        biglist = biglist | ('oom:addone-%d' % i) >> Map(lambda x: x + 'y')
      result = biglist | 'oom:check' >> Map(check_memory, memory_threshold)
      assert_that(
          result,
          equal_to(['x' * len_elements + 'y' * num_maps] * num_elements))

  def test_aggregator_empty_input(self):
    actual = [] | CombineGlobally(max).without_defaults()
    self.assertEqual(actual, [])

  def test_pipeline_as_context(self):
    def raise_exception(exn):
      raise exn

    with self.assertRaises(Exception):
      with Pipeline() as p:
        # pylint: disable=expression-not-assigned
        p | Create([ValueError('msg')]) | Map(raise_exception)

  def test_ptransform_overrides(self):
    class MyParDoOverride(PTransformOverride):
      def matches(self, applied_ptransform):
        return isinstance(applied_ptransform.transform, DoubleParDo)

      def get_replacement_transform_for_applied_ptransform(
          self, applied_ptransform):
        ptransform = applied_ptransform.transform
        if isinstance(ptransform, DoubleParDo):
          return TripleParDo()
        raise ValueError('Unsupported type of transform: %r' % ptransform)

    p = Pipeline()
    pcoll = p | beam.Create([1, 2, 3]) | 'Multiply' >> DoubleParDo()
    assert_that(pcoll, equal_to([3, 6, 9]))

    p.replace_all([MyParDoOverride()])
    p.run()

  def test_ptransform_override_type_hints(self):
    class NoTypeHintOverride(PTransformOverride):
      def matches(self, applied_ptransform):
        return isinstance(applied_ptransform.transform, DoubleParDo)

      def get_replacement_transform_for_applied_ptransform(
          self, applied_ptransform):
        return ToStringParDo()

    class WithTypeHintOverride(PTransformOverride):
      def matches(self, applied_ptransform):
        return isinstance(applied_ptransform.transform, DoubleParDo)

      def get_replacement_transform_for_applied_ptransform(
          self, applied_ptransform):
        return ToStringParDo().with_input_types(int).with_output_types(str)

    for override, expected_type in [(NoTypeHintOverride(), int),
                                    (WithTypeHintOverride(), str)]:
      p = TestPipeline()
      pcoll = (
          p
          | beam.Create([1, 2, 3])
          | 'Operate' >> DoubleParDo()
          | 'NoOp' >> beam.Map(lambda x: x))

      p.replace_all([override])
      self.assertEqual(pcoll.producer.inputs[0].element_type, expected_type)

  def test_ptransform_override_multiple_inputs(self):
    class MyParDoOverride(PTransformOverride):
      def matches(self, applied_ptransform):
        return isinstance(applied_ptransform.transform, FlattenAndDouble)

      def get_replacement_transform(self, applied_ptransform):
        return FlattenAndTriple()

    p = Pipeline()
    pcoll1 = p | 'pc1' >> beam.Create([1, 2, 3])
    pcoll2 = p | 'pc2' >> beam.Create([4, 5, 6])
    pcoll3 = (pcoll1, pcoll2) | 'FlattenAndMultiply' >> FlattenAndDouble()
    assert_that(pcoll3, equal_to([3, 6, 9, 12, 15, 18]))

    p.replace_all([MyParDoOverride()])
    p.run()

  def test_ptransform_override_side_inputs(self):
    class MyParDoOverride(PTransformOverride):
      def matches(self, applied_ptransform):
        return (
            isinstance(applied_ptransform.transform, ParDo) and
            isinstance(applied_ptransform.transform.fn, AddWithProductDoFn))

      def get_replacement_transform(self, transform):
        return AddThenMultiply()

    p = Pipeline()
    pcoll1 = p | 'pc1' >> beam.Create([2])
    pcoll2 = p | 'pc2' >> beam.Create([3])
    pcoll3 = p | 'pc3' >> beam.Create([4, 5, 6])
    result = pcoll3 | 'Operate' >> beam.ParDo(
        AddWithProductDoFn(), AsSingleton(pcoll1), AsSingleton(pcoll2))
    assert_that(result, equal_to([18, 21, 24]))

    p.replace_all([MyParDoOverride()])
    p.run()

  def test_ptransform_override_replacement_inputs(self):
    class MyParDoOverride(PTransformOverride):
      def matches(self, applied_ptransform):
        return (
            isinstance(applied_ptransform.transform, ParDo) and
            isinstance(applied_ptransform.transform.fn, AddWithProductDoFn))

      def get_replacement_transform(self, transform):
        return AddThenMultiply()

      def get_replacement_inputs(self, applied_ptransform):
        assert len(applied_ptransform.inputs) == 1
        assert len(applied_ptransform.side_inputs) == 2
        # Swap the order of the two side inputs
        return (
            applied_ptransform.inputs[0],
            applied_ptransform.side_inputs[1].pvalue,
            applied_ptransform.side_inputs[0].pvalue)

    p = Pipeline()
    pcoll1 = p | 'pc1' >> beam.Create([2])
    pcoll2 = p | 'pc2' >> beam.Create([3])
    pcoll3 = p | 'pc3' >> beam.Create([4, 5, 6])
    result = pcoll3 | 'Operate' >> beam.ParDo(
        AddWithProductDoFn(), AsSingleton(pcoll1), AsSingleton(pcoll2))
    assert_that(result, equal_to([14, 16, 18]))

    p.replace_all([MyParDoOverride()])
    p.run()

  def test_ptransform_override_multiple_outputs(self):
    class MultiOutputComposite(PTransform):
      def __init__(self):
        self.output_tags = set()

      def expand(self, pcoll):
        def mux_input(x):
          x = x * 2
          if isinstance(x, int):
            yield TaggedOutput('numbers', x)
          else:
            yield TaggedOutput('letters', x)

        multi = pcoll | 'MyReplacement' >> beam.ParDo(mux_input).with_outputs()
        letters = multi.letters | 'LettersComposite' >> beam.Map(
            lambda x: x * 3)
        numbers = multi.numbers | 'NumbersComposite' >> beam.Map(
            lambda x: x * 5)

        return {
            'letters': letters,
            'numbers': numbers,
        }

    class MultiOutputOverride(PTransformOverride):
      def matches(self, applied_ptransform):
        return applied_ptransform.full_label == 'MyMultiOutput'

      def get_replacement_transform_for_applied_ptransform(
          self, applied_ptransform):
        return MultiOutputComposite()

    def mux_input(x):
      if isinstance(x, int):
        yield TaggedOutput('numbers', x)
      else:
        yield TaggedOutput('letters', x)

    with TestPipeline() as p:
      multi = (
          p
          | beam.Create([1, 2, 3, 'a', 'b', 'c'])
          | 'MyMultiOutput' >> beam.ParDo(mux_input).with_outputs())
      letters = multi.letters | 'MyLetters' >> beam.Map(lambda x: x)
      numbers = multi.numbers | 'MyNumbers' >> beam.Map(lambda x: x)

      # Assert that the PCollection replacement worked correctly and that
      # elements are flowing through. The replacement transform first
      # multiples by 2 then the leaf nodes inside the composite multiply by
      # an additional 3 and 5. Use prime numbers to ensure that each
      # transform is getting executed once.
      assert_that(
          letters,
          equal_to(['a' * 2 * 3, 'b' * 2 * 3, 'c' * 2 * 3]),
          label='assert letters')
      assert_that(
          numbers,
          equal_to([1 * 2 * 5, 2 * 2 * 5, 3 * 2 * 5]),
          label='assert numbers')

      # Do the replacement and run the element assertions.
      p.replace_all([MultiOutputOverride()])

    # The following checks the graph to make sure the replacement occurred.
    visitor = PipelineTest.Visitor(visited=[])
    p.visit(visitor)
    pcollections = visitor.visited
    composites = visitor.enter_composite

    # Assert the replacement is in the composite list and retrieve the
    # AppliedPTransform.
    self.assertIn(
        MultiOutputComposite, [t.transform.__class__ for t in composites])
    multi_output_composite = list(
        filter(
            lambda t: t.transform.__class__ == MultiOutputComposite,
            composites))[0]

    # Assert that all of the replacement PCollections are in the graph.
    for output in multi_output_composite.outputs.values():
      self.assertIn(output, pcollections)

    # Assert that all of the "old"/replaced PCollections are not in the graph.
    self.assertNotIn(multi[None], visitor.visited)
    self.assertNotIn(multi.letters, visitor.visited)
    self.assertNotIn(multi.numbers, visitor.visited)

  def test_filter_typehint(self):
    # Check input type hint and output type hint are both specified.
    def always_true_with_all_typehints(x: int) -> bool:
      return True

    # Check only input type hint is specified.
    def always_true_only_inptype(x: int):
      return True

    # Check only output type hint is specified.
    def always_true_only_outptype(x) -> bool:
      return True

    # Check if inp type hint is Any that we can still infer
    # from the input pcollection type
    def always_true_any_inptype(x: typehints.Any) -> bool:
      return True

    for filter_fn in [always_true_with_all_typehints,
                      always_true_only_inptype,
                      always_true_only_outptype,
                      always_true_any_inptype]:
      with TestPipeline() as p:
        pcoll = (
            p
            | beam.Create([1, 2, 3]).with_input_types(int)
            | beam.Filter(filter_fn))
      self.assertEqual(pcoll.element_type, int)

  def test_kv_ptransform_honor_type_hints(self):

    # The return type of this DoFn cannot be inferred by the default
    # Beam type inference
    class StatefulDoFn(DoFn):
      BYTES_STATE = BagStateSpec('bytes', BytesCoder())

      def return_recursive(self, count):
        if count == 0:
          return ["some string"]
        else:
          self.return_recursive(count - 1)

      def process(self, element, counter=DoFn.StateParam(BYTES_STATE)):
        return self.return_recursive(1)

    with TestPipeline() as p:
      pcoll = (
          p
          | beam.Create([(1, 1), (2, 2), (3, 3)])
          | beam.GroupByKey()
          | beam.ParDo(StatefulDoFn()))
    self.assertEqual(pcoll.element_type, typehints.Any)

    with TestPipeline() as p:
      pcoll = (
          p
          | beam.Create([(1, 1), (2, 2), (3, 3)])
          | beam.GroupByKey()
          | beam.ParDo(StatefulDoFn()).with_output_types(str))
    self.assertEqual(pcoll.element_type, str)

  def test_track_pcoll_unbounded(self):
    pipeline = TestPipeline()
    pcoll1 = pipeline | 'read' >> Read(FakeUnboundedSource())
    pcoll2 = pcoll1 | 'do1' >> FlatMap(lambda x: [x + 1])
    pcoll3 = pcoll2 | 'do2' >> FlatMap(lambda x: [x + 1])
    self.assertIs(pcoll1.is_bounded, False)
    self.assertIs(pcoll2.is_bounded, False)
    self.assertIs(pcoll3.is_bounded, False)

  def test_track_pcoll_bounded(self):
    pipeline = TestPipeline()
    pcoll1 = pipeline | 'label1' >> Create([1, 2, 3])
    pcoll2 = pcoll1 | 'do1' >> FlatMap(lambda x: [x + 1])
    pcoll3 = pcoll2 | 'do2' >> FlatMap(lambda x: [x + 1])
    self.assertIs(pcoll1.is_bounded, True)
    self.assertIs(pcoll2.is_bounded, True)
    self.assertIs(pcoll3.is_bounded, True)

  def test_track_pcoll_bounded_flatten(self):
    pipeline = TestPipeline()
    pcoll1_a = pipeline | 'label_a' >> Create([1, 2, 3])
    pcoll2_a = pcoll1_a | 'do_a' >> FlatMap(lambda x: [x + 1])

    pcoll1_b = pipeline | 'label_b' >> Create([1, 2, 3])
    pcoll2_b = pcoll1_b | 'do_b' >> FlatMap(lambda x: [x + 1])

    merged = (pcoll2_a, pcoll2_b) | beam.Flatten()

    self.assertIs(pcoll1_a.is_bounded, True)
    self.assertIs(pcoll2_a.is_bounded, True)
    self.assertIs(pcoll1_b.is_bounded, True)
    self.assertIs(pcoll2_b.is_bounded, True)
    self.assertIs(merged.is_bounded, True)

  def test_track_pcoll_unbounded_flatten(self):
    pipeline = TestPipeline()
    pcoll1_bounded = pipeline | 'label1' >> Create([1, 2, 3])
    pcoll2_bounded = pcoll1_bounded | 'do1' >> FlatMap(lambda x: [x + 1])

    pcoll1_unbounded = pipeline | 'read' >> Read(FakeUnboundedSource())
    pcoll2_unbounded = pcoll1_unbounded | 'do2' >> FlatMap(lambda x: [x + 1])

    merged = (pcoll2_bounded, pcoll2_unbounded) | beam.Flatten()

    self.assertIs(pcoll1_bounded.is_bounded, True)
    self.assertIs(pcoll2_bounded.is_bounded, True)
    self.assertIs(pcoll1_unbounded.is_bounded, False)
    self.assertIs(pcoll2_unbounded.is_bounded, False)
    self.assertIs(merged.is_bounded, False)

  def test_incompatible_pcollection_errmsg(self):
    with pytest.raises(Exception,
                       match=r".*Map\(print\).*Got a PBegin/Pipeline instead."):
      with beam.Pipeline() as pipeline:
        _ = (pipeline | beam.Map(print))

    class ParentTransform(PTransform):
      def expand(self, pcoll):
        return pcoll | beam.Map(print)

    with pytest.raises(
        Exception,
        match=r".*ParentTransform/Map\(print\).*Got a PBegin/Pipeline instead."
    ):
      with beam.Pipeline() as pipeline:
        _ = (pipeline | ParentTransform())

  def test_incompatible_submission_and_runtime_envs_fail_pipeline(self):
    with mock.patch(
        'apache_beam.transforms.environments.sdk_base_version_capability'
    ) as base_version:
      base_version.side_effect = [
          f"beam:version:sdk_base:apache/beam_python3.5_sdk:2.{i}.0"
          for i in range(100)
      ]
      with self.assertRaisesRegex(
          RuntimeError,
          'Pipeline construction environment and pipeline runtime '
          'environment are not compatible.'):
        # TODO(https://github.com/apache/beam/issues/34549): Prism doesn't
        # pass through capabilities as part of the ProcessBundleDescriptor.
        with TestPipeline('FnApiRunner') as p:
          _ = p | Create([None])


class DoFnTest(unittest.TestCase):
  def test_element(self):
    class TestDoFn(DoFn):
      def process(self, element):
        yield element + 10

    with TestPipeline() as pipeline:
      pcoll = pipeline | 'Create' >> Create([1, 2]) | 'Do' >> ParDo(TestDoFn())
      assert_that(pcoll, equal_to([11, 12]))

  def test_side_input_no_tag(self):
    class TestDoFn(DoFn):
      def process(self, element, prefix, suffix):
        return ['%s-%s-%s' % (prefix, element, suffix)]

    with TestPipeline() as pipeline:
      words_list = ['aa', 'bb', 'cc']
      words = pipeline | 'SomeWords' >> Create(words_list)
      prefix = 'zyx'
      suffix = pipeline | 'SomeString' >> Create(['xyz'])  # side in
      result = words | 'DecorateWordsDoFnNoTag' >> ParDo(
          TestDoFn(), prefix, suffix=AsSingleton(suffix))
      assert_that(result, equal_to(['zyx-%s-xyz' % x for x in words_list]))

  def test_side_input_tagged(self):
    class TestDoFn(DoFn):
      def process(self, element, prefix, suffix=DoFn.SideInputParam):
        return ['%s-%s-%s' % (prefix, element, suffix)]

    with TestPipeline() as pipeline:
      words_list = ['aa', 'bb', 'cc']
      words = pipeline | 'SomeWords' >> Create(words_list)
      prefix = 'zyx'
      suffix = pipeline | 'SomeString' >> Create(['xyz'])  # side in
      result = words | 'DecorateWordsDoFnNoTag' >> ParDo(
          TestDoFn(), prefix, suffix=AsSingleton(suffix))
      assert_that(result, equal_to(['zyx-%s-xyz' % x for x in words_list]))

  @pytest.mark.it_validatesrunner
  def test_element_param(self):
    pipeline = TestPipeline()
    input = [1, 2]
    pcoll = (
        pipeline
        | 'Create' >> Create(input)
        | 'Ele param' >> Map(lambda element=DoFn.ElementParam: element))
    assert_that(pcoll, equal_to(input))
    pipeline.run()

  @pytest.mark.it_validatesrunner
  def test_key_param(self):
    pipeline = TestPipeline()
    pcoll = (
        pipeline
        | 'Create' >> Create([('a', 1), ('b', 2)])
        | 'Key param' >> Map(lambda _, key=DoFn.KeyParam: key))
    assert_that(pcoll, equal_to(['a', 'b']))
    pipeline.run()

  def test_window_param(self):
    class TestDoFn(DoFn):
      def process(self, element, window=DoFn.WindowParam):
        yield (element, (float(window.start), float(window.end)))

    with TestPipeline() as pipeline:
      pcoll = (
          pipeline
          | Create([1, 7])
          | Map(lambda x: TimestampedValue(x, x))
          | WindowInto(windowfn=SlidingWindows(10, 5))
          | ParDo(TestDoFn()))
      assert_that(
          pcoll,
          equal_to([(1, (-5, 5)), (1, (0, 10)), (7, (0, 10)), (7, (5, 15))]))
      pcoll2 = pcoll | 'Again' >> ParDo(TestDoFn())
      assert_that(
          pcoll2,
          equal_to([((1, (-5, 5)), (-5, 5)), ((1, (0, 10)), (0, 10)),
                    ((7, (0, 10)), (0, 10)), ((7, (5, 15)), (5, 15))]),
          label='doubled windows')

  def test_windowed_value_param(self):
    with TestPipeline() as pipeline:
      pcoll = (
          pipeline
          | Create([1, 7])
          | Map(lambda x: TimestampedValue(x, x))
          | WindowInto(windowfn=FixedWindows(5))
          | Map(lambda _, wv=DoFn.WindowedValueParam: (wv.value, wv.windows)))
      assert_that(
          pcoll,
          equal_to([(1, [IntervalWindow(0, 5)]), (7, [IntervalWindow(5, 10)])]))  # pylint: disable=too-many-function-args

  def test_timestamp_param(self):
    class TestDoFn(DoFn):
      def process(self, element, timestamp=DoFn.TimestampParam):
        yield timestamp

    with TestPipeline() as pipeline:
      pcoll = pipeline | 'Create' >> Create([1, 2]) | 'Do' >> ParDo(TestDoFn())
      assert_that(pcoll, equal_to([MIN_TIMESTAMP, MIN_TIMESTAMP]))

  def test_timestamp_param_map(self):
    with TestPipeline() as p:
      assert_that(
          p | Create([1, 2]) | beam.Map(lambda _, t=DoFn.TimestampParam: t),
          equal_to([MIN_TIMESTAMP, MIN_TIMESTAMP]))

  def test_pane_info_param(self):
    with TestPipeline() as p:
      pc = p | Create([(None, None)])
      assert_that(
          pc | beam.Map(lambda _, p=DoFn.PaneInfoParam: p),
          equal_to([windowed_value.PANE_INFO_UNKNOWN]),
          label='CheckUngrouped')
      assert_that(
          pc | beam.GroupByKey() | beam.Map(lambda _, p=DoFn.PaneInfoParam: p),
          equal_to([
              windowed_value.PaneInfo(
                  is_first=True,
                  is_last=True,
                  timing=windowed_value.PaneInfoTiming.ON_TIME,
                  index=0,
                  nonspeculative_index=0)
          ]),
          label='CheckGrouped')

  def test_context_params(self):
    def test_map(
        x,
        context_a=DoFn.BundleContextParam(_TestContext, args=('a')),
        context_b=DoFn.BundleContextParam(_TestContext, args=('b')),
        context_c=DoFn.SetupContextParam(_TestContext, args=('c'))):
      return (x, context_a, context_b, context_c)

    self.assertEqual(_TestContext.live_contexts, 0)
    with TestPipeline('FnApiRunner') as p:
      pcoll = p | Create([1, 2]) | beam.Map(test_map)
      assert_that(pcoll, equal_to([(1, 'a', 'b', 'c'), (2, 'a', 'b', 'c')]))
    self.assertEqual(_TestContext.live_contexts, 0)

  def test_incomparable_default(self):
    class IncomparableType(object):
      def __eq__(self, other):
        raise RuntimeError()

      def __ne__(self, other):
        raise RuntimeError()

      def __hash__(self):
        raise RuntimeError()

    # Ensure that we don't use default values in a context where they must be
    # comparable (see BEAM-8301).
    with TestPipeline() as pipeline:
      pcoll = (
          pipeline
          | beam.Create([None])
          | Map(lambda e, x=IncomparableType(): (e, type(x).__name__)))
      assert_that(pcoll, equal_to([(None, 'IncomparableType')]))


class _TestContext:

  live_contexts = 0

  def __init__(self, value):
    self._value = value

  def __enter__(self):
    _TestContext.live_contexts += 1
    return self._value

  def __exit__(self, *args):
    _TestContext.live_contexts -= 1


class Bacon(PipelineOptions):
  @classmethod
  def _add_argparse_args(cls, parser):
    parser.add_argument('--slices', type=int)


class Eggs(PipelineOptions):
  @classmethod
  def _add_argparse_args(cls, parser):
    parser.add_argument('--style', default='scrambled')


class Breakfast(Bacon, Eggs):
  pass


class PipelineOptionsTest(unittest.TestCase):
  def test_flag_parsing(self):
    options = Breakfast(['--slices=3', '--style=sunny side up', '--ignored'])
    self.assertEqual(3, options.slices)
    self.assertEqual('sunny side up', options.style)

  def test_keyword_parsing(self):
    options = Breakfast(['--slices=3', '--style=sunny side up', '--ignored'],
                        slices=10)
    self.assertEqual(10, options.slices)
    self.assertEqual('sunny side up', options.style)

  def test_attribute_setting(self):
    options = Breakfast(slices=10)
    self.assertEqual(10, options.slices)
    options.slices = 20
    self.assertEqual(20, options.slices)

  def test_view_as(self):
    generic_options = PipelineOptions(['--slices=3'])
    self.assertEqual(3, generic_options.view_as(Bacon).slices)
    self.assertEqual(3, generic_options.view_as(Breakfast).slices)

    generic_options.view_as(Breakfast).slices = 10
    self.assertEqual(10, generic_options.view_as(Bacon).slices)

    with self.assertRaises(AttributeError):
      generic_options.slices  # pylint: disable=pointless-statement

    with self.assertRaises(AttributeError):
      generic_options.view_as(Eggs).slices  # pylint: disable=expression-not-assigned

  def test_defaults(self):
    options = Breakfast(['--slices=3'])
    self.assertEqual(3, options.slices)
    self.assertEqual('scrambled', options.style)

  def test_dir(self):
    options = Breakfast()
    self.assertEqual({
        'from_dictionary',
        'get_all_options',
        'slices',
        'style',
        'view_as',
        'display_data',
        'from_runner_api',
        'to_runner_api',
    },
                     {
                         attr
                         for attr in dir(options)
                         if not attr.startswith('_') and attr != 'next'
                     })
    self.assertEqual({
        'from_dictionary',
        'get_all_options',
        'style',
        'view_as',
        'display_data',
        'from_runner_api',
        'to_runner_api',
    },
                     {
                         attr
                         for attr in dir(options.view_as(Eggs))
                         if not attr.startswith('_') and attr != 'next'
                     })


class RunnerApiTest(unittest.TestCase):
  def test_parent_pointer(self):
    class MyPTransform(beam.PTransform):
      def expand(self, p):
        self.p = p
        return p | beam.Create([None])

    p = beam.Pipeline()
    p | MyPTransform()  # pylint: disable=expression-not-assigned
    p = Pipeline.from_runner_api(
        Pipeline.to_runner_api(p, use_fake_coders=True), None, None)
    self.assertIsNotNone(p.transforms_stack[0].parts[0].parent)
    self.assertEqual(
        p.transforms_stack[0].parts[0].parent, p.transforms_stack[0])

  def test_requirements(self):
    p = beam.Pipeline()
    _ = (
        p | beam.Create([])
        | beam.ParDo(lambda x, finalize=beam.DoFn.BundleFinalizerParam: None))
    proto = p.to_runner_api()
    self.assertTrue(
        common_urns.requirements.REQUIRES_BUNDLE_FINALIZATION.urn,
        proto.requirements)

  def test_coder_version_tag_included_in_runner_api_key(self):
    class MyClass:
      def __init__(self, value: int):
        self.value = value

    class VersionedCoder(coders.Coder):
      def encode(self, value):
        return str(value.value).encode()

      def decode(self, encoded):
        return MyClass(int(encoded.decode()))

      def version_tag(self):
        return "v269"

      def to_type_hint(self):
        return MyClass

    coders.registry.register_coder(MyClass, VersionedCoder)
    p = beam.Pipeline()
    _ = (p | beam.Impulse() | beam.Map(lambda _: MyClass(1)))
    pipeline_proto = p.to_runner_api()
    coder_keys = sorted(list(pipeline_proto.components.coders.keys()))

    self.assertListEqual(
        coder_keys,
        [
            'ref_Coder_BytesCoder_1',
            'ref_Coder_GlobalWindowCoder_2',
            'ref_Coder_VersionedCoder_v269_3'
        ])

  def test_annotations(self):
    some_proto = BytesCoder().to_runner_api(None)

    class EmptyTransform(beam.PTransform):
      def expand(self, pcoll):
        return pcoll

      def annotations(self):
        return {'foo': 'some_string'}

    class NonEmptyTransform(beam.PTransform):
      def expand(self, pcoll):
        return pcoll | beam.Map(lambda x: x)

      def annotations(self):
        return {
            'foo': b'some_bytes',
            'proto': some_proto,
        }

    p = beam.Pipeline()
    _ = p | beam.Create([]) | EmptyTransform() | NonEmptyTransform()
    proto = p.to_runner_api()

    seen = 0
    for transform in proto.components.transforms.values():
      if transform.unique_name == 'EmptyTransform':
        seen += 1
        self.assertEqual(transform.annotations['foo'], b'some_string')
      elif transform.unique_name == 'NonEmptyTransform':
        seen += 1
        self.assertEqual(transform.annotations['foo'], b'some_bytes')
        self.assertEqual(
            transform.annotations['proto'], some_proto.SerializeToString())
    self.assertEqual(seen, 2)

  def assertHasAnnotation(self, pipeline_proto, transform, key, value):
    for transform_proto in pipeline_proto.components.transforms.values():
      if transform_proto.unique_name == transform:
        self.assertIn(key, transform_proto.annotations.keys())
        self.assertEqual(transform_proto.annotations[key], value)
        break
    else:
      self.fail(
          "Unknown transform: %r not in %s" % (
              transform,
              sorted([
                  t.unique_name
                  for t in pipeline_proto.components.transforms.values()
              ])))

  def test_pipeline_context_annotations(self):
    p = beam.Pipeline()
    with p.transform_annotations(foo='first'):
      pcoll = p | beam.Create([1, 2, 3]) | 'First' >> beam.Map(lambda x: x + 1)
    with p.transform_annotations(foo='second'):
      _ = pcoll | 'Second' >> beam.Map(lambda x: x * 2)
      with p.transform_annotations(foo='nested', another='more'):
        _ = pcoll | 'Nested' >> beam.Map(lambda x: x * 3)

    proto = p.to_runner_api()
    self.assertHasAnnotation(proto, 'First', 'foo', b'first')
    self.assertHasAnnotation(proto, 'Second', 'foo', b'second')
    self.assertHasAnnotation(proto, 'Nested', 'foo', b'nested')
    self.assertHasAnnotation(proto, 'Nested', 'another', b'more')

  def test_beam_context_annotations(self):
    p = beam.Pipeline()
    with beam.transform_annotations(foo='first'):
      pcoll = p | beam.Create([1, 2, 3]) | 'First' >> beam.Map(lambda x: x + 1)
    with beam.transform_annotations(foo='second'):
      _ = pcoll | 'Second' >> beam.Map(lambda x: x * 2)
      with beam.transform_annotations(foo='nested', another='more'):
        _ = pcoll | 'Nested' >> beam.Map(lambda x: x * 3)

    proto = p.to_runner_api()
    self.assertHasAnnotation(proto, 'First', 'foo', b'first')
    self.assertHasAnnotation(proto, 'Second', 'foo', b'second')
    self.assertHasAnnotation(proto, 'Nested', 'foo', b'nested')
    self.assertHasAnnotation(proto, 'Nested', 'another', b'more')

  def test_transform_ids(self):
    class MyPTransform(beam.PTransform):
      def expand(self, p):
        self.p = p
        return p | beam.Create([None])

    p = beam.Pipeline()
    p | MyPTransform()  # pylint: disable=expression-not-assigned
    runner_api_proto = Pipeline.to_runner_api(p)

    for transform_id in runner_api_proto.components.transforms:
      self.assertRegex(transform_id, r'[a-zA-Z0-9-_]+')

  def test_input_names(self):
    class MyPTransform(beam.PTransform):
      def expand(self, pcolls):
        return pcolls.values() | beam.Flatten()

    p = beam.Pipeline()
    input_names = set('ABC')
    inputs = {x: p | x >> beam.Create([x]) for x in input_names}
    inputs | MyPTransform()  # pylint: disable=expression-not-assigned
    runner_api_proto = Pipeline.to_runner_api(p)

    for transform_proto in runner_api_proto.components.transforms.values():
      if transform_proto.unique_name == 'MyPTransform':
        self.assertEqual(set(transform_proto.inputs.keys()), input_names)
        break
    else:
      self.fail('Unable to find transform.')

  def test_display_data(self):
    class MyParentTransform(beam.PTransform):
      def expand(self, p):
        self.p = p
        return p | beam.Create([None])

      def display_data(self) -> dict:
        parent_dd = super().display_data()
        parent_dd['p_dd_string'] = DisplayDataItem(
            'p_dd_string_value', label='p_dd_string_label')
        parent_dd['p_dd_string_2'] = DisplayDataItem('p_dd_string_value_2')
        parent_dd['p_dd_bool'] = DisplayDataItem(True, label='p_dd_bool_label')
        parent_dd['p_dd_int'] = DisplayDataItem(1, label='p_dd_int_label')
        return parent_dd

    class MyPTransform(MyParentTransform):
      def expand(self, p):
        self.p = p
        return p | beam.Create([None])

      def display_data(self) -> dict:
        parent_dd = super().display_data()
        parent_dd['dd_string'] = DisplayDataItem(
            'dd_string_value', label='dd_string_label')
        parent_dd['dd_string_2'] = DisplayDataItem('dd_string_value_2')
        parent_dd['dd_bool'] = DisplayDataItem(False, label='dd_bool_label')
        parent_dd['dd_double'] = DisplayDataItem(1.1, label='dd_double_label')
        return parent_dd

    p = beam.Pipeline()
    p | MyPTransform()  # pylint: disable=expression-not-assigned

    proto_pipeline = Pipeline.to_runner_api(p, use_fake_coders=True)
    my_transform, = [
        transform
        for transform in proto_pipeline.components.transforms.values()
        if transform.unique_name == 'MyPTransform'
    ]
    self.assertIsNotNone(my_transform)
    self.assertListEqual(
        list(my_transform.display_data),
        [
            beam_runner_api_pb2.DisplayData(
                urn=common_urns.StandardDisplayData.DisplayData.LABELLED.urn,
                payload=beam_runner_api_pb2.LabelledPayload(
                    label='p_dd_string_label',
                    key='p_dd_string',
                    namespace='apache_beam.pipeline_test.MyPTransform',
                    string_value='p_dd_string_value').SerializeToString()),
            beam_runner_api_pb2.DisplayData(
                urn=common_urns.StandardDisplayData.DisplayData.LABELLED.urn,
                payload=beam_runner_api_pb2.LabelledPayload(
                    label='p_dd_string_2',
                    key='p_dd_string_2',
                    namespace='apache_beam.pipeline_test.MyPTransform',
                    string_value='p_dd_string_value_2').SerializeToString()),
            beam_runner_api_pb2.DisplayData(
                urn=common_urns.StandardDisplayData.DisplayData.LABELLED.urn,
                payload=beam_runner_api_pb2.LabelledPayload(
                    label='p_dd_bool_label',
                    key='p_dd_bool',
                    namespace='apache_beam.pipeline_test.MyPTransform',
                    bool_value=True).SerializeToString()),
            beam_runner_api_pb2.DisplayData(
                urn=common_urns.StandardDisplayData.DisplayData.LABELLED.urn,
                payload=beam_runner_api_pb2.LabelledPayload(
                    label='p_dd_int_label',
                    key='p_dd_int',
                    namespace='apache_beam.pipeline_test.MyPTransform',
                    int_value=1).SerializeToString()),
            beam_runner_api_pb2.DisplayData(
                urn=common_urns.StandardDisplayData.DisplayData.LABELLED.urn,
                payload=beam_runner_api_pb2.LabelledPayload(
                    label='dd_string_label',
                    key='dd_string',
                    namespace='apache_beam.pipeline_test.MyPTransform',
                    string_value='dd_string_value').SerializeToString()),
            beam_runner_api_pb2.DisplayData(
                urn=common_urns.StandardDisplayData.DisplayData.LABELLED.urn,
                payload=beam_runner_api_pb2.LabelledPayload(
                    label='dd_string_2',
                    key='dd_string_2',
                    namespace='apache_beam.pipeline_test.MyPTransform',
                    string_value='dd_string_value_2').SerializeToString()),
            beam_runner_api_pb2.DisplayData(
                urn=common_urns.StandardDisplayData.DisplayData.LABELLED.urn,
                payload=beam_runner_api_pb2.LabelledPayload(
                    label='dd_bool_label',
                    key='dd_bool',
                    namespace='apache_beam.pipeline_test.MyPTransform',
                    bool_value=False).SerializeToString()),
            beam_runner_api_pb2.DisplayData(
                urn=common_urns.StandardDisplayData.DisplayData.LABELLED.urn,
                payload=beam_runner_api_pb2.LabelledPayload(
                    label='dd_double_label',
                    key='dd_double',
                    namespace='apache_beam.pipeline_test.MyPTransform',
                    double_value=1.1).SerializeToString()),
        ])

  def test_runner_api_roundtrip_preserves_resource_hints(self):
    p = beam.Pipeline()
    _ = (
        p | beam.Create([1, 2])
        | beam.Map(lambda x: x + 1).with_resource_hints(accelerator='gpu'))

    self.assertEqual(
        p.transforms_stack[0].parts[1].transform.get_resource_hints(),
        {common_urns.resource_hints.ACCELERATOR.urn: b'gpu'})

    for _ in range(3):
      # Verify that DEFAULT environments are recreated during multiple RunnerAPI
      # translation and hints don't get lost.
      p = Pipeline.from_runner_api(Pipeline.to_runner_api(p), None, None)
      self.assertEqual(
          p.transforms_stack[0].parts[1].transform.get_resource_hints(),
          {common_urns.resource_hints.ACCELERATOR.urn: b'gpu'})

  def test_hints_on_composite_transforms_are_propagated_to_subtransforms(self):
    class FooHint(ResourceHint):
      urn = 'foo_urn'

    class BarHint(ResourceHint):
      urn = 'bar_urn'

    class BazHint(ResourceHint):
      urn = 'baz_urn'

    class QuxHint(ResourceHint):
      urn = 'qux_urn'

    class UseMaxValueHint(ResourceHint):
      urn = 'use_max_value_urn'

      @classmethod
      def get_merged_value(
          cls, outer_value: bytes, inner_value: bytes) -> bytes:
        return ResourceHint._use_max(outer_value, inner_value)

    ResourceHint.register_resource_hint('foo_hint', FooHint)
    ResourceHint.register_resource_hint('bar_hint', BarHint)
    ResourceHint.register_resource_hint('baz_hint', BazHint)
    ResourceHint.register_resource_hint('qux_hint', QuxHint)
    ResourceHint.register_resource_hint('use_max_value_hint', UseMaxValueHint)

    @beam.ptransform_fn
    def SubTransform(pcoll):
      return pcoll | beam.Map(lambda x: x + 1).with_resource_hints(
          foo_hint='set_on_subtransform', use_max_value_hint='10')

    @beam.ptransform_fn
    def CompositeTransform(pcoll):
      return pcoll | beam.Map(lambda x: x * 2) | SubTransform()

    p = beam.Pipeline()
    _ = (
        p | beam.Create([1, 2])
        | CompositeTransform().with_resource_hints(
            foo_hint='should_be_overriden_by_subtransform',
            bar_hint='set_on_composite',
            baz_hint='set_on_composite',
            use_max_value_hint='100'))
    options = PortableOptions([
        '--resource_hint=baz_hint=should_be_overriden_by_composite',
        '--resource_hint=qux_hint=set_via_options',
        '--environment_type=PROCESS',
        '--environment_option=process_command=foo',
        '--sdk_location=container',
    ])
    environment = ProcessEnvironment.from_options(options)
    proto = Pipeline.to_runner_api(p, default_environment=environment)

    for t in proto.components.transforms.values():
      if "CompositeTransform/SubTransform/Map" in t.unique_name:
        environment = proto.components.environments.get(t.environment_id)
        self.assertEqual(
            environment.resource_hints.get('foo_urn'), b'set_on_subtransform')
        self.assertEqual(
            environment.resource_hints.get('bar_urn'), b'set_on_composite')
        self.assertEqual(
            environment.resource_hints.get('baz_urn'), b'set_on_composite')
        self.assertEqual(
            environment.resource_hints.get('qux_urn'), b'set_via_options')
        self.assertEqual(
            environment.resource_hints.get('use_max_value_urn'), b'100')
        found = True
    assert found

  def test_environments_with_same_resource_hints_are_reused(self):
    class HintX(ResourceHint):
      urn = 'X_urn'

    class HintY(ResourceHint):
      urn = 'Y_urn'

    class HintIsOdd(ResourceHint):
      urn = 'IsOdd_urn'

    ResourceHint.register_resource_hint('X', HintX)
    ResourceHint.register_resource_hint('Y', HintY)
    ResourceHint.register_resource_hint('IsOdd', HintIsOdd)

    p = beam.Pipeline()
    num_iter = 4
    for i in range(num_iter):
      _ = (
          p
          | f'NoHintCreate_{i}' >> beam.Create([1, 2])
          | f'NoHint_{i}' >> beam.Map(lambda x: x + 1))
      _ = (
          p
          | f'XCreate_{i}' >> beam.Create([1, 2])
          |
          f'HintX_{i}' >> beam.Map(lambda x: x + 1).with_resource_hints(X='X'))
      _ = (
          p
          | f'XYCreate_{i}' >> beam.Create([1, 2])
          | f'HintXY_{i}' >> beam.Map(lambda x: x + 1).with_resource_hints(
              X='X', Y='Y'))
      _ = (
          p
          | f'IsOddCreate_{i}' >> beam.Create([1, 2])
          | f'IsOdd_{i}' >>
          beam.Map(lambda x: x + 1).with_resource_hints(IsOdd=str(i % 2 != 0)))

    proto = Pipeline.to_runner_api(p)
    count_x = count_xy = count_is_odd = count_no_hints = 0
    env_ids = set()
    for _, t in proto.components.transforms.items():
      env = proto.components.environments[t.environment_id]
      if t.unique_name.startswith('HintX_'):
        count_x += 1
        env_ids.add(t.environment_id)
        self.assertEqual(env.resource_hints, {'X_urn': b'X'})

      if t.unique_name.startswith('HintXY_'):
        count_xy += 1
        env_ids.add(t.environment_id)
        self.assertEqual(env.resource_hints, {'X_urn': b'X', 'Y_urn': b'Y'})

      if t.unique_name.startswith('NoHint_'):
        count_no_hints += 1
        env_ids.add(t.environment_id)
        self.assertEqual(env.resource_hints, {})

      if t.unique_name.startswith('IsOdd_'):
        count_is_odd += 1
        env_ids.add(t.environment_id)
        self.assertTrue(
            env.resource_hints == {'IsOdd_urn': b'True'} or
            env.resource_hints == {'IsOdd_urn': b'False'})
    assert count_x == count_is_odd == count_xy == count_no_hints == num_iter
    assert num_iter > 1

    self.assertEqual(len(env_ids), 5)

  def test_multiple_application_of_the_same_transform_set_different_hints(self):
    class FooHint(ResourceHint):
      urn = 'foo_urn'

    class UseMaxValueHint(ResourceHint):
      urn = 'use_max_value_urn'

      @classmethod
      def get_merged_value(
          cls, outer_value: bytes, inner_value: bytes) -> bytes:
        return ResourceHint._use_max(outer_value, inner_value)

    ResourceHint.register_resource_hint('foo_hint', FooHint)
    ResourceHint.register_resource_hint('use_max_value_hint', UseMaxValueHint)

    @beam.ptransform_fn
    def SubTransform(pcoll):
      return pcoll | beam.Map(lambda x: x + 1)

    @beam.ptransform_fn
    def CompositeTransform(pcoll):
      sub = SubTransform()
      return (
          pcoll
          | 'first' >> sub.with_resource_hints(foo_hint='first_application')
          | 'second' >> sub.with_resource_hints(foo_hint='second_application'))

    p = beam.Pipeline()
    _ = (p | beam.Create([1, 2]) | CompositeTransform())
    proto = Pipeline.to_runner_api(p)
    count = 0
    for t in proto.components.transforms.values():
      if "CompositeTransform/first/Map" in t.unique_name:
        environment = proto.components.environments.get(t.environment_id)
        self.assertEqual(
            b'first_application', environment.resource_hints.get('foo_urn'))
        count += 1
      if "CompositeTransform/second/Map" in t.unique_name:
        environment = proto.components.environments.get(t.environment_id)
        self.assertEqual(
            b'second_application', environment.resource_hints.get('foo_urn'))
        count += 1
    assert count == 2

  def test_environments_are_deduplicated(self):
    def file_artifact(path, hash, staged_name):
      return beam_runner_api_pb2.ArtifactInformation(
          type_urn=common_urns.artifact_types.FILE.urn,
          type_payload=beam_runner_api_pb2.ArtifactFilePayload(
              path=path, sha256=hash).SerializeToString(),
          role_urn=common_urns.artifact_roles.STAGING_TO.urn,
          role_payload=beam_runner_api_pb2.ArtifactStagingToRolePayload(
              staged_name=staged_name).SerializeToString(),
      )

    proto = beam_runner_api_pb2.Pipeline(
        components=beam_runner_api_pb2.Components(
            transforms={
                f'transform{ix}': beam_runner_api_pb2.PTransform(
                    environment_id=f'e{ix}')
                for ix in range(8)
            },
            environments={
                # Same hash and destination.
                'e1': beam_runner_api_pb2.Environment(
                    dependencies=[file_artifact('a1', 'x', 'dest')]),
                'e2': beam_runner_api_pb2.Environment(
                    dependencies=[file_artifact('a2', 'x', 'dest')]),
                'e3': beam_runner_api_pb2.Environment(
                    dependencies=[file_artifact('a3', 'y', 'dest')
                                  ]),  # Different hash.
                'e4': beam_runner_api_pb2.Environment(
                    dependencies=[file_artifact('a4', 'y', 'dest2')
                                  ]),  # Different destination.
                'e5': beam_runner_api_pb2.Environment(
                    dependencies=[
                        file_artifact('a1', 'x', 'dest'),
                        file_artifact('b1', 'xb', 'destB')
                    ]),  # Multiple files with same hash and destinations.
                'e6': beam_runner_api_pb2.Environment(
                    dependencies=[
                        file_artifact('a2', 'x', 'dest'),
                        file_artifact('b2', 'xb', 'destB')
                    ]),
                'e7': beam_runner_api_pb2.Environment(
                    dependencies=[
                        file_artifact('a1', 'x', 'dest'),
                        file_artifact('b2', 'y', 'destB')
                    ]),  # Overlapping, but not identical, files.
                'e0': beam_runner_api_pb2.Environment(
                    resource_hints={'hint': b'value'},
                    dependencies=[file_artifact('a1', 'x', 'dest')]
                ),  # Same files as first, but differing other properties.
            }))
    Pipeline.merge_compatible_environments(proto)

    # These environments are equivalent.
    self.assertEqual(
        proto.components.transforms['transform1'].environment_id,
        proto.components.transforms['transform2'].environment_id)

    self.assertEqual(
        proto.components.transforms['transform5'].environment_id,
        proto.components.transforms['transform6'].environment_id)

    # These are not.
    self.assertNotEqual(
        proto.components.transforms['transform1'].environment_id,
        proto.components.transforms['transform3'].environment_id)
    self.assertNotEqual(
        proto.components.transforms['transform4'].environment_id,
        proto.components.transforms['transform3'].environment_id)
    self.assertNotEqual(
        proto.components.transforms['transform6'].environment_id,
        proto.components.transforms['transform7'].environment_id)
    self.assertNotEqual(
        proto.components.transforms['transform1'].environment_id,
        proto.components.transforms['transform0'].environment_id)

    self.assertEqual(len(proto.components.environments), 6)

  def test_multiple_outputs_composite_ptransform(self):
    """
    Test that a composite PTransform with multiple outputs is represented
    correctly in the pipeline proto.
    """
    class SalesSplitter(beam.DoFn):
      def process(self, element):
        price = element['price']
        if price > 100:
          yield beam.pvalue.TaggedOutput('premium_sales', element)
        else:
          yield beam.pvalue.TaggedOutput('standard_sales', element)

    class ParentSalesSplitter(beam.PTransform):
      def expand(self, pcoll):
        return pcoll | beam.ParDo(SalesSplitter()).with_outputs(
            'premium_sales', 'standard_sales')

    sales_data = [
        {
            'item': 'Laptop', 'price': 1200
        },
        {
            'item': 'Mouse', 'price': 25
        },
        {
            'item': 'Keyboard', 'price': 75
        },
        {
            'item': 'Monitor', 'price': 350
        },
        {
            'item': 'Headphones', 'price': 90
        },
    ]

    with beam.Pipeline() as pipeline:
      sales_records = pipeline | 'Create Sales' >> beam.Create(sales_data)
      _ = sales_records | 'Split Sales' >> ParentSalesSplitter()
    current_transforms = list(pipeline.transforms_stack)
    all_applied_transforms = {
        xform.full_label: xform
        for xform in current_transforms
    }
    while current_transforms:
      xform = current_transforms.pop()
      all_applied_transforms[xform.full_label] = xform
      current_transforms.extend(xform.parts)
    xform = all_applied_transforms['Split Sales']
    # Confirm that Split Sales correctly has two outputs as specified by
    #  ParDo.with_outputs in ParentSalesSplitter.
    assert len(xform.outputs) == 2


if __name__ == '__main__':
  unittest.main()