🧑‍💻 Make iterator error handler optional and pin poetry-publish

[garlontas]

Summary by Sourcery

Clarify error handler typing in iterator utilities and pin the poetry publish GitHub Action to a specific commit for publishing.

Enhancements:

• Annotate iterator utility error handler parameters as Optional to better reflect nullable usage.

CI:

• Pin the JRubics/poetry-publish GitHub Action in the Python publish workflow to a specific commit SHA for more deterministic releases.

[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

This PR tightens type annotations in the itertools helpers by making error handler parameters explicitly Optional and pins the poetry-publish GitHub Action to a specific commit for safer, reproducible publishing.

Class diagram for updated itertools tools type annotations

classDiagram
    class tools_module {
        +dropwhile(predicate, iterable, handler Optional_ErrorHandler)
        +reduce(function, sequence, initial, handler Optional_ErrorHandler)
    }

    class ErrorHandler {
    }

    tools_module ..> ErrorHandler : uses

    class Optional_ErrorHandler {
    }

    Optional_ErrorHandler o--> ErrorHandler : wraps

Loading

File-Level Changes

Change	Details	Files
Make error handler arguments explicitly optional in itertools helper functions.	Import Optional from typing to support optional type hints. Update dropwhile handler parameter type from ErrorHandler to Optional[ErrorHandler]. Update reduce handler parameter type from ErrorHandler to Optional[ErrorHandler].	pystreamapi/_itertools/tools.py
Pin the poetry-publish GitHub Action to a fixed commit SHA for the Python publish workflow.	Change the GitHub Actions workflow to use JRubics/poetry-publish by commit hash instead of a floating v2.1 tag.	.github/workflows/python-publish.yml

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[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
100.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[sourcery-ai]

Hey - I've found 1 issue, and left some high level feedback:

• Now that handler is explicitly Optional[ErrorHandler], consider also adding concrete type hints for predicate, iterable/sequence, and function in dropwhile and reduce to improve static checking and clarify expected call signatures.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Now that `handler` is explicitly `Optional[ErrorHandler]`, consider also adding concrete type hints for `predicate`, `iterable`/`sequence`, and `function` in `dropwhile` and `reduce` to improve static checking and clarify expected call signatures.

## Individual Comments

### Comment 1
<location path="pystreamapi/_itertools/tools.py" line_range="27" />
<code_context>


-def reduce(function, sequence, initial=_initial_missing, handler: ErrorHandler = None):
+def reduce(function, sequence, initial=_initial_missing, handler: Optional[ErrorHandler] = None):
     """
     Apply a function of two arguments cumulatively to the items of a sequence
</code_context>
<issue_to_address>
**suggestion:** Consider giving `initial` a more precise type to improve type checking around `reduce`.

Now that `handler` is typed, it’d be helpful to also give `initial` a more precise type instead of relying on the untyped `_initial_missing` sentinel. For example, introduce an accumulator `TypeVar` and use `initial: Union[_Missing, AccumulatorType] = _initial_missing` (or a `Sentinel` protocol) so static type checkers can infer `reduce`’s return type more accurately and avoid `Any` propagation.

Suggested implementation:

```python
def dropwhile(predicate, iterable, handler: Optional[ErrorHandler] = None):
    """
    Drop items from the iterable while predicate(item) is true.
    Afterward, return every element until the iterable is exhausted.
class _InitialMissing:
    """Sentinel type used to detect when no initial accumulator is provided."""
    __slots__ = ()


_initial_missing = _InitialMissing()

AccumulatorT = TypeVar("AccumulatorT")


def reduce(function, sequence, initial: Union[_InitialMissing, AccumulatorT] = _initial_missing, handler: Optional[ErrorHandler] = None) -> AccumulatorT:

```

To fully type this change you should also:
1. Ensure the necessary typing imports are present at the top of `pystreamapi/_itertools/tools.py`, e.g.:
   - Add `TypeVar` and `Union` to the existing `typing` imports:
   `from typing import Optional, TypeVar, Union`
2. Optionally, if the rest of the `reduce` signature is already typed elsewhere in the file, align `function` and `sequence` parameter types with existing `TypeVar`s (e.g., `Iterable[T]`, `Callable[[AccumulatorT, T], AccumulatorT]`) so that static type checkers can fully infer `AccumulatorT`.
</issue_to_address>

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[sourcery-ai]

suggestion: Consider giving initial a more precise type to improve type checking around reduce.

Now that handler is typed, it’d be helpful to also give initial a more precise type instead of relying on the untyped _initial_missing sentinel. For example, introduce an accumulator TypeVar and use initial: Union[_Missing, AccumulatorType] = _initial_missing (or a Sentinel protocol) so static type checkers can infer reduce’s return type more accurately and avoid Any propagation.

Suggested implementation:

def dropwhile(predicate, iterable, handler: Optional[ErrorHandler] = None):
    """
    Drop items from the iterable while predicate(item) is true.
    Afterward, return every element until the iterable is exhausted.
class _InitialMissing:
    """Sentinel type used to detect when no initial accumulator is provided."""
    __slots__ = ()


_initial_missing = _InitialMissing()

AccumulatorT = TypeVar("AccumulatorT")


def reduce(function, sequence, initial: Union[_InitialMissing, AccumulatorT] = _initial_missing, handler: Optional[ErrorHandler] = None) -> AccumulatorT:

To fully type this change you should also:

1. Ensure the necessary typing imports are present at the top of pystreamapi/_itertools/tools.py, e.g.:
   • Add TypeVar and Union to the existing typing imports:
     from typing import Optional, TypeVar, Union
2. Optionally, if the rest of the reduce signature is already typed elsewhere in the file, align function and sequence parameter types with existing TypeVars (e.g., Iterable[T], Callable[[AccumulatorT, T], AccumulatorT]) so that static type checkers can fully infer AccumulatorT.