def _test_flatten_input_visitor(self, input_type, output_type, num_inputs):
    p = TestPipeline()
    inputs = []
    for _ in range(num_inputs):
      input_pcoll = PCollection(p)
      input_pcoll.element_type = input_type
      inputs.append(input_pcoll)
    output_pcoll = PCollection(p)
    output_pcoll.element_type = output_type

    flatten = AppliedPTransform(None, beam.Flatten(), "label", inputs)
    flatten.add_output(output_pcoll, None)
    DataflowRunner.flatten_input_visitor().visit_transform(flatten)
    for _ in range(num_inputs):
      self.assertEqual(inputs[0].element_type, output_type)
 def test_group_by_key_input_visitor_with_valid_inputs(self):
   p = TestPipeline()
   pcoll1 = PCollection(p)
   pcoll2 = PCollection(p)
   pcoll3 = PCollection(p)
   for transform in [_GroupByKeyOnly(), beam.GroupByKey()]:
     pcoll1.element_type = None
     pcoll2.element_type = typehints.Any
     pcoll3.element_type = typehints.KV[typehints.Any, typehints.Any]
     for pcoll in [pcoll1, pcoll2, pcoll3]:
       applied = AppliedPTransform(None, transform, "label", [pcoll])
       applied.outputs[None] = PCollection(None)
       DataflowRunner.group_by_key_input_visitor().visit_transform(
           applied)
       self.assertEqual(pcoll.element_type,
                        typehints.KV[typehints.Any, typehints.Any])
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 def test_group_by_key_input_visitor_for_non_gbk_transforms(self):
     p = TestPipeline()
     pcoll = PCollection(p)
     for transform in [beam.Flatten(), beam.Map(lambda x: x)]:
         pcoll.element_type = typehints.Any
         DataflowRunner.group_by_key_input_visitor().visit_transform(
             AppliedPTransform(None, transform, "label", [pcoll]))
         self.assertEqual(pcoll.element_type, typehints.Any)
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    def test_group_by_key_input_visitor_with_valid_inputs(self):
        p = TestPipeline()
        pcoll1 = PCollection(p)
        pcoll2 = PCollection(p)
        pcoll3 = PCollection(p)

        pcoll1.element_type = None
        pcoll2.element_type = typehints.Any
        pcoll3.element_type = typehints.KV[typehints.Any, typehints.Any]
        for pcoll in [pcoll1, pcoll2, pcoll3]:
            applied = AppliedPTransform(None, beam.GroupByKey(), "label",
                                        [pcoll])
            applied.outputs[None] = PCollection(None)
            DataflowRunner.group_by_key_input_visitor().visit_transform(
                applied)
            self.assertEqual(pcoll.element_type, typehints.KV[typehints.Any,
                                                              typehints.Any])
 def test_group_by_key_input_visitor_with_invalid_inputs(self):
   p = TestPipeline()
   pcoll1 = PCollection(p)
   pcoll2 = PCollection(p)
   for transform in [_GroupByKeyOnly(), beam.GroupByKey()]:
     pcoll1.element_type = typehints.TupleSequenceConstraint
     pcoll2.element_type = typehints.Set
     err_msg = "Input to GroupByKey must be of Tuple or Any type"
     for pcoll in [pcoll1, pcoll2]:
       with self.assertRaisesRegexp(ValueError, err_msg):
         DataflowRunner.group_by_key_input_visitor().visit_transform(
             AppliedPTransform(None, transform, "label", [pcoll]))
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    def test_side_input_visitor(self):
        p = TestPipeline()
        pc = p | beam.Create([])

        transform = beam.Map(lambda x, y, z: (x, y, z),
                             beam.pvalue.AsSingleton(pc),
                             beam.pvalue.AsMultiMap(pc))
        applied_transform = AppliedPTransform(None, transform, "label", [pc])
        DataflowRunner.side_input_visitor().visit_transform(applied_transform)
        self.assertEqual(2, len(applied_transform.side_inputs))
        for side_input in applied_transform.side_inputs:
            self.assertEqual(common_urns.side_inputs.MULTIMAP.urn,
                             side_input._side_input_data().access_pattern)
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    def test_group_by_key_input_visitor_with_invalid_inputs(self):
        p = TestPipeline()
        pcoll1 = PCollection(p)
        pcoll2 = PCollection(p)

        pcoll1.element_type = str
        pcoll2.element_type = typehints.Set
        err_msg = (r"Input to 'label' must be compatible with KV\[Any, Any\]. "
                   "Found .*")
        for pcoll in [pcoll1, pcoll2]:
            with self.assertRaisesRegex(ValueError, err_msg):
                DataflowRunner.group_by_key_input_visitor().visit_transform(
                    AppliedPTransform(None, beam.GroupByKey(), "label",
                                      [pcoll]))
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 def test_group_by_key_input_visitor_with_valid_inputs(self):
     p = TestPipeline()
     pcoll1 = PCollection(p)
     pcoll2 = PCollection(p)
     pcoll3 = PCollection(p)
     for transform in [beam.GroupByKeyOnly(), beam.GroupByKey()]:
         pcoll1.element_type = None
         pcoll2.element_type = typehints.Any
         pcoll3.element_type = typehints.KV[typehints.Any, typehints.Any]
         for pcoll in [pcoll1, pcoll2, pcoll3]:
             runner.group_by_key_input_visitor().visit_transform(
                 AppliedPTransform(None, transform, "label", [pcoll]))
             self.assertEqual(pcoll.element_type,
                              typehints.KV[typehints.Any, typehints.Any])