Fix acc_all_stderr grouping by question_id only (drops paragraph_id)

[Chessing234]

Bug

`acc_all_stderr` in `lm_eval/api/metrics.py` silently merges MultiRC questions from different paragraphs into a single bucket, so the stderr it produces disagrees with the point estimate that `acc_all` computes for the very same rows.

Root cause

`acc_all` and `acc_all_stderr` are a paired metric/stderr for the `acc_all` registered metric — a MultiRC-style "a question counts correct iff every answer row for that question is correct" metric. The metric function keys the per-question bucket by `(paragraph_id, question_id)`:

```python
def acc_all(items):
...
for doc, pred in zip(docs, preds):
paragraph_id = doc["idx"]["paragraph"]
question_id = doc["idx"]["question"]
if (paragraph_id, question_id) not in question_scoring_dict:
question_scoring_dict[(paragraph_id, question_id)] = []
...
```

but the stderr function right below it drops `paragraph_id` entirely and keys by just `question_id`:

```python
def acc_all_stderr(items):
...
for doc, pred in zip(docs, preds):
question_id = doc["idx"]["question"]
if question_id not in question_scoring_dict:
question_scoring_dict[question_id] = []
...
```

In SuperGLUE MultiRC, `question_id` is only unique within a paragraph — the same numeric `question_id` (0, 1, 2, ...) is reused across different paragraphs. Dropping `paragraph_id` from the bucket key conflates unrelated questions from different paragraphs: `all(x)` is then taken over a merged bucket containing answer rows from multiple distinct questions, and the resulting 0/1 list handed to `mean_stderr` has the wrong size and wrong per-question correctness values.

Concrete worked example

Paragraph 1 has question 0 with answers `[T, T]` (both correct). Paragraph 2 has question 0 with answers `[T, F]` (one wrong).

• `acc_all`: keys `(1, 0)` = `[True, True]` → 1, `(2, 0)` = `[True, False]` → 0. `mean = 0.5`.
• `acc_all_stderr` (current): key `0` = `[True, True, True, False]` → `all(...) = False` → 0. `mean_stderr` of `[0]` = 0.

The stderr function sees 1 “question” (the merged bucket) and marks it wrong, even though `acc_all` correctly sees 2 questions with mean 0.5. Any downstream consumer comparing the two numbers will see inconsistent results.

Fix

Mirror `acc_all`'s bucket key in `acc_all_stderr`:

```diff
for doc, pred in zip(docs, preds):

•    paragraph_id = doc[\"idx\"][\"paragraph\"]
     question_id = doc[\"idx\"][\"question\"]
  

•    if question_id not in question_scoring_dict:
  

•        question_scoring_dict[question_id] = []
  

•    if (paragraph_id, question_id) not in question_scoring_dict:
  

•        question_scoring_dict[(paragraph_id, question_id)] = []
  
     gold_label = doc[\"label\"] == 1
  

•    question_scoring_dict[question_id].append(gold_label == pred)
  

•    question_scoring_dict[(paragraph_id, question_id)].append(gold_label == pred)
  

```

No other call sites (grepped `acc_all` across the repo; only `lm_eval/api/metrics.py` references it), so this is a strictly corrective change that makes the stderr bucket over the same question set as the metric.