## full_process()


Process items through a configurable pipeline.


Usage


``` python
full_process(
    items,
    pipeline=None,
    verbose=False,
    max_workers=1,
    timeout=30.0,
)
```


Applies each stage in `pipeline` to the items sequentially (or in parallel if `max_workers > 1`).


## Parameters


`items: list`  
List of items to process.

`pipeline: list = None`  
List of callable stages. Each receives items and returns modified items. If None, uses a default pipeline.

`verbose: bool = ``False`  
If True, print progress information.

`max_workers: int = ``1`  
Number of parallel workers. Use 1 for sequential.

`timeout: float = ``30.0`  
Maximum processing time in seconds per stage.


## Returns


`dict`  
Processing results with keys:

- `"items"` -- processed items
- `"stages_run"` -- number of stages executed
- `"elapsed"` -- total time in seconds


## Raises


`TimeoutError`  
If any stage exceeds the timeout.

`RuntimeError`  
If a pipeline stage fails.


## Notes

Parallel processing uses a thread pool. For CPU-bound stages, consider using `max_workers=1` to avoid GIL contention.


## Examples

``` python
>>> full_process([1, 2, 3])
```

{'items': \[1, 2, 3\], 'stages_run': 0, 'elapsed': 0.0}

``` python
>>> full_process([1, 2, 3], pipeline=[str], verbose=True)
```

Processing stage 1/1… {'items': \['1', '2', '3'\], 'stages_run': 1, 'elapsed': 0.0}