hmc-cs-mdrissi opened a new issue, #23366:
URL: https://github.com/apache/beam/issues/23366
### What happened?
```python
from typing import Tuple, TypeVar
from apache_beam.typehints.typehints import TypeVariable, is_consistent_with
K = TypeVariable("K")
V = TypeVariable("V")
A = TypeVar("A")
B = TypeVar("B")
print(is_consistent_with(tuple[int, str], Tuple[int, str]))
print(is_consistent_with(tuple[K, V], Tuple[int, str]))
print(is_consistent_with(tuple[A, B], Tuple[int, str]))
```
All of these print False. This is because is_consistent_with/normalize type
machinery in beam does not handle tuple vs typing.Tuple/dict vs
typing.Dict/list vs typing.List/etc equivalents. This leads to beam's runtime
type checking crashing on valid code like,
```python
import apache_beam as beam
def add_parity(n: int) -> tuple[bool, int]:
return (n % 2 == 0, n)
with beam.Pipeline() as p:
numbers = p | "Create" >> beam.Create([1, 2, 3, 4, 5])
numbers = numbers | beam.Map(add_parity)
_ = numbers | beam.GroupByKey() | beam.Map(print)
```
Here add_parity returns tuple, but GroupByKey expects Tuple. python 3.9+
recommends using tuple and some tools will even automatically replace Tuple ->
tuple. So at moment workarounds are either stick with typing.Tuple or disable
beam runtime type checking.
An example of assumption of typing.X generics that fails for pep 585
generics is
[here](https://github.com/apache/beam/blob/a0fe7879aad16816a6435ab5bbf57826dd9ecde7/sdks/python/apache_beam/typehints/typehints.py#L1216).
`getattr(x, '__module__', None) == 'typing'` is not true for builtin generic
equivalents as they come from there original source which may be various
modules (most of basic ones are 'builtins'). There may be other code that needs
changes as `tuple[int, str] != Tuple[int, str]` (equivalent but cpython they
are handled by different types
[technically](https://github.com/python/cpython/issues/97543)).
### Issue Priority
Priority: 2
### Issue Component
Component: sdk-py-core
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