Revert three commits related to supporting custom coder in reshuffle

- Fix custom coder not being used in Reshuffle (global window) (#33339)
- Fix custom coders not being used in Reshuffle (non global window) #33363
- Add missing to_type_hint to WindowedValueCoder #33403

sdks/python/apache_beam/coders/coders.py

@@ -1438,17 +1438,6 @@ def __hash__(self):
     return hash(
         (self.wrapped_value_coder, self.timestamp_coder, self.window_coder))
 
-  @classmethod
-  def from_type_hint(cls, typehint, registry):
-    # type: (Any, CoderRegistry) -> WindowedValueCoder
-    # Ideally this'd take two parameters so that one could hint at
-    # the window type as well instead of falling back to the
-    # pickle coders.
-    return cls(registry.get_coder(typehint.inner_type))
-
-  def to_type_hint(self):
-    return typehints.WindowedValue[self.wrapped_value_coder.to_type_hint()]
-
 
 Coder.register_structured_urn(
     common_urns.coders.WINDOWED_VALUE.urn, WindowedValueCoder)

sdks/python/apache_beam/coders/coders_test.py

@@ -258,12 +258,6 @@ def test_numpy_int(self):
         _ = indata | "CombinePerKey" >> beam.CombinePerKey(sum)
 
 
-class WindowedValueCoderTest(unittest.TestCase):
-  def test_to_type_hint(self):
-    coder = coders.WindowedValueCoder(coders.VarIntCoder())
-    self.assertEqual(coder.to_type_hint(), typehints.WindowedValue[int])  # type: ignore[misc]
-
-
 if __name__ == '__main__':
   logging.getLogger().setLevel(logging.INFO)
   unittest.main()

sdks/python/apache_beam/coders/typecoders.py

@@ -94,8 +94,6 @@ def register_standard_coders(self, fallback_coder):
     self._register_coder_internal(str, coders.StrUtf8Coder)
     self._register_coder_internal(typehints.TupleConstraint, coders.TupleCoder)
     self._register_coder_internal(typehints.DictConstraint, coders.MapCoder)
-    self._register_coder_internal(
-        typehints.WindowedTypeConstraint, coders.WindowedValueCoder)
     # Default fallback coders applied in that order until the first matching
     # coder found.
     default_fallback_coders = [

sdks/python/apache_beam/transforms/util.py

@@ -33,7 +33,6 @@
 from typing import Callable
 from typing import Iterable
 from typing import List
-from typing import Optional
 from typing import Tuple
 from typing import TypeVar
 from typing import Union
@@ -74,13 +73,11 @@
 from apache_beam.transforms.window import TimestampedValue
 from apache_beam.typehints import trivial_inference
 from apache_beam.typehints.decorators import get_signature
-from apache_beam.typehints.native_type_compatibility import TypedWindowedValue
 from apache_beam.typehints.sharded_key_type import ShardedKeyType
 from apache_beam.utils import shared
 from apache_beam.utils import windowed_value
 from apache_beam.utils.annotations import deprecated
 from apache_beam.utils.sharded_key import ShardedKey
-from apache_beam.utils.timestamp import Timestamp
 
 if TYPE_CHECKING:
   from apache_beam.runners.pipeline_context import PipelineContext
@@ -956,10 +953,6 @@ def restore_timestamps(element):
             window.GlobalWindows.windowed_value((key, value), timestamp)
             for (value, timestamp) in values
         ]
-
-      ungrouped = pcoll | Map(reify_timestamps).with_input_types(
-          Tuple[K, V]).with_output_types(
-              Tuple[K, Tuple[V, Optional[Timestamp]]])
     else:
 
       # typing: All conditional function variants must have identical signatures
@@ -973,8 +966,7 @@ def restore_timestamps(element):
         key, windowed_values = element
         return [wv.with_value((key, wv.value)) for wv in windowed_values]
 
-      ungrouped = pcoll | Map(reify_timestamps).with_input_types(
-          Tuple[K, V]).with_output_types(Tuple[K, TypedWindowedValue[V]])
+    ungrouped = pcoll | Map(reify_timestamps).with_output_types(Any)
 
     # TODO(https://github.com/apache/beam/issues/19785) Using global window as
     # one of the standard window. This is to mitigate the Dataflow Java Runner
@@ -1026,8 +1018,7 @@ def expand(self, pcoll):
         pcoll | 'AddRandomKeys' >>
         Map(lambda t: (random.randrange(0, self.num_buckets), t)
             ).with_input_types(T).with_output_types(Tuple[int, T])
-        | ReshufflePerKey().with_input_types(Tuple[int, T]).with_output_types(
-            Tuple[int, T])
+        | ReshufflePerKey()
         | 'RemoveRandomKeys' >> Map(lambda t: t[1]).with_input_types(
             Tuple[int, T]).with_output_types(T))
 

sdks/python/apache_beam/transforms/util_test.py

@@ -1010,60 +1010,6 @@ def format_with_timestamp(element, timestamp=beam.DoFn.TimestampParam):
           equal_to(expected_data),
           label="formatted_after_reshuffle")
 
-  global _Unpicklable
-  global _UnpicklableCoder
-
-  class _Unpicklable(object):
-    def __init__(self, value):
-      self.value = value
-
-    def __getstate__(self):
-      raise NotImplementedError()
-
-    def __setstate__(self, state):
-      raise NotImplementedError()
-
-  class _UnpicklableCoder(beam.coders.Coder):
-    def encode(self, value):
-      return str(value.value).encode()
-
-    def decode(self, encoded):
-      return _Unpicklable(int(encoded.decode()))
-
-    def to_type_hint(self):
-      return _Unpicklable
-
-    def is_deterministic(self):
-      return True
-
-  def test_reshuffle_unpicklable_in_global_window(self):
-    beam.coders.registry.register_coder(_Unpicklable, _UnpicklableCoder)
-
-    with TestPipeline() as pipeline:
-      data = [_Unpicklable(i) for i in range(5)]
-      expected_data = [0, 10, 20, 30, 40]
-      result = (
-          pipeline
-          | beam.Create(data)
-          | beam.WindowInto(GlobalWindows())
-          | beam.Reshuffle()
-          | beam.Map(lambda u: u.value * 10))
-      assert_that(result, equal_to(expected_data))
-
-  def test_reshuffle_unpicklable_in_non_global_window(self):
-    beam.coders.registry.register_coder(_Unpicklable, _UnpicklableCoder)
-
-    with TestPipeline() as pipeline:
-      data = [_Unpicklable(i) for i in range(5)]
-      expected_data = [0, 0, 0, 10, 10, 10, 20, 20, 20, 30, 30, 30, 40, 40, 40]
-      result = (
-          pipeline
-          | beam.Create(data)
-          | beam.WindowInto(window.SlidingWindows(size=3, period=1))
-          | beam.Reshuffle()
-          | beam.Map(lambda u: u.value * 10))
-      assert_that(result, equal_to(expected_data))
-
 
 class WithKeysTest(unittest.TestCase):
   def setUp(self):

sdks/python/apache_beam/typehints/native_type_compatibility.py

@@ -24,13 +24,9 @@
 import sys
 import types
 import typing
-from typing import Generic
-from typing import TypeVar
 
 from apache_beam.typehints import typehints
 
-T = TypeVar('T')
-
 _LOGGER = logging.getLogger(__name__)
 
 # Describes an entry in the type map in convert_to_beam_type.
@@ -220,18 +216,6 @@ def convert_collections_to_typing(typ):
   return typ
 
 
-# During type inference of WindowedValue, we need to pass in the inner value
-# type. This cannot be achieved immediately with WindowedValue class because it
-# is not parameterized. Changing it to a generic class (e.g. WindowedValue[T])
-# could work in theory. However, the class is cythonized and it seems that
-# cython does not handle generic classes well.
-# The workaround here is to create a separate class solely for the type
-# inference purpose. This class should never be used for creating instances.
-class TypedWindowedValue(Generic[T]):
-  def __init__(self, *args, **kwargs):
-    raise NotImplementedError("This class is solely for type inference")
-
-
 def convert_to_beam_type(typ):
   """Convert a given typing type to a Beam type.
 
@@ -283,12 +267,6 @@ def convert_to_beam_type(typ):
     # TODO(https://github.com/apache/beam/issues/20076): Currently unhandled.
     _LOGGER.info('Converting NewType type hint to Any: "%s"', typ)
     return typehints.Any
-  elif typ_module == 'apache_beam.typehints.native_type_compatibility' and \
-      getattr(typ, "__name__", typ.__origin__.__name__) == 'TypedWindowedValue':
-    # Need to pass through WindowedValue class so that it can be converted
-    # to the correct type constraint in Beam
-    # This is needed to fix https://github.com/apache/beam/issues/33356
-    pass
   elif (typ_module != 'typing') and (typ_module != 'collections.abc'):
     # Only translate types from the typing and collections.abc modules.
     return typ
@@ -346,10 +324,6 @@ def convert_to_beam_type(typ):
           match=_match_is_exactly_collection,
           arity=1,
           beam_type=typehints.Collection),
-      _TypeMapEntry(
-          match=_match_issubclass(TypedWindowedValue),
-          arity=1,
-          beam_type=typehints.WindowedValue),
   ]
 
   # Find the first matching entry.

sdks/python/apache_beam/typehints/typehints.py

@@ -1213,15 +1213,6 @@ def type_check(self, instance):
               repr(self.inner_type),
               instance.value.__class__.__name__))
 
-  def bind_type_variables(self, bindings):
-    bound_inner_type = bind_type_variables(self.inner_type, bindings)
-    if bound_inner_type == self.inner_type:
-      return self
-    return WindowedValue[bound_inner_type]
-
-  def __repr__(self):
-    return 'WindowedValue[%s]' % repr(self.inner_type)
-
 
 class GeneratorHint(IteratorHint):
   """A Generator type hint.