Django: Multiple, Grouped Annotations with Filtered ManyToMany

I'm bumping up against an issue with a series of grouped annotations on a Django QuerySet which is being filtered by a ManyToMany. Since I've been staring at my screen for too long, lets imagine a series of models about Wine tastings:

class Event(model.Model):
     # Some Fields

class Wine(models.Model):
     # Some Fields

class Tasting(models.Model):
    event = models.ManyToManyField(Event)
    wine = models.ForeignKey(Wine)
    score = models.IntegerField()

I want to get some aggregations on the data by wine, optionally filtering on certain events. With this sample Tasting data (which is what I'll be running the aggregation on):

| wine_id | score | event_ids |
| ------- | ----- | --------- |
| 1       | 50    | [1]       |
| 1       | 50    | [1]       |
| 1       | 50    | [1, 2]    |
| 2       | 100   | [1, 2]    |
| 2       | 150   | [1, 2]    |
| 3       | 75    | [1]       |

The expected output, for the above data is:

[
    {'wine_id': 1, 'total_scores': 150, 'average_scores': 50},
    {'wine_id': 2, 'total_scores': 250, 'average_scores': 125},
    {'wine_id': 3, 'total_scores': 75, 'average_scores': 75},
]

Attempt 1

Just some regular values and annotation

Tasting.objects.filter(
   event__in=Event.objects.filter(id__in=[1,2])
).distinct().values('wine_id').annotate(
   total_scores=Sum('score'),
   average_scores=Avg('scores'),
)

Which outputs:

[
    {'wine_id': 1, 'total_scores': 200, 'average_scores': 50},  # Total score too high
    {'wine_id': 2, 'total_scores': 250, 'average_scores': 125},
    {'wine_id': 3, 'total_scores': 75, 'average_scores': 75},
]

Hrm, so it looks like I'm butting up against that same issue that crops up with multiple annotations -- due to the join when filtering the events, one of the wine_1 rows is counted twice: once for each event.

Attempt 2

So, looking at a bunch of suggestions from that Django issue (such as this answer, I figured I could tackle the problem with Subqueries, which led me to this beast:

total_subquery = Subquery(Tasting.objects.filter(wine_id=OuterRef('wine_id')).annotate(
   total_scores=Sum('score'),
).values('total_scores'))

average_subquery = Subquery(Tasting.objects.filter(wine_id=OuterRef('wine_id')).annotate(
   average_scores=Avg('scores'),
).values('average_scores'))

Tasting.objects.filter(
   event__in=Event.objects.filter(id__in=[1,2])
).distinct().values('wine_id').annotate(
   total_scores=total_subquery,
   average_scores=average_subquery,
)

So, initially, this looked correct:

[
    {'wine_id': 1, 'total_scores': 150, 'average_scores': 50},
    {'wine_id': 2, 'total_scores': 250, 'average_scores': 125},
    {'wine_id': 3, 'total_scores': 75, 'average_scores': 75},
]

Huzzah! BUT, what if we change the filter to only include Event 2:

Tasting.objects.filter(
   event__in=Event.objects.filter(id__in=[2])
).distinct().values('wine_id').annotate(
   total_scores=total_subquery,
   average_scores=average_subquery,
)

In this case, I still get back the data for ALL events. This makes intuitive sense, since the subqueries don't have any knowledge of the outer filter. However, if I change the OuterRef values on the subquery (to something like filter(pk=OuterRef('pk'))), then the proper grouping on the subqueries falls apart. If I re-add the event filtering at the subquery level, then we get the same duplicate row issue that we had in our first attempt.

I can get the proper values by simple fetching all the data and then doing the aggregation in Python, but this has serious performance costs for larger datasets. Is there a way to do this aggregation entirely through the ORM?