Convert integer ids to UUID early

Downstream code now depends on refs holding UUIDs. Have the yaml
loader convert old style integer ids to UUIDs early rather than
waiting for downstream cleanups.

python/lsst/daf/butler/transfers/_yaml.py

@@ -28,7 +28,7 @@
 from collections import defaultdict
 from collections.abc import Iterable, Mapping
 from datetime import datetime
-from typing import IO, TYPE_CHECKING, Any
+from typing import IO, TYPE_CHECKING, Any, cast
 
 import astropy.time
 import yaml
@@ -64,6 +64,8 @@
 this version of the code.
 """
 
+_refIntId2UUID = defaultdict[int, uuid.UUID](uuid.uuid4)
+
 
 def _uuid_representer(dumper: yaml.Dumper, data: uuid.UUID) -> yaml.Node:
     """Generate YAML representation for UUID.
@@ -338,16 +340,27 @@ def __init__(self, stream: IO, registry: Registry):
             elif data["type"] == "associations":
                 collectionType = CollectionType.from_name(data["collection_type"])
                 if collectionType is CollectionType.TAGGED:
-                    self.tagAssociations[data["collection"]].extend(data["dataset_ids"])
+                    self.tagAssociations[data["collection"]].extend(
+                        [
+                            x if not isinstance(x, int) else cast(DatasetId, _refIntId2UUID[x])
+                            for x in data["dataset_ids"]
+                        ]
+                    )
                 elif collectionType is CollectionType.CALIBRATION:
                     assocsByTimespan = self.calibAssociations[data["collection"]]
                     for d in data["validity_ranges"]:
                         if "timespan" in d:
-                            assocsByTimespan[d["timespan"]] = d["dataset_ids"]
+                            assocsByTimespan[d["timespan"]] = [
+                                x if not isinstance(x, int) else cast(DatasetId, _refIntId2UUID[x])
+                                for x in d["dataset_ids"]
+                            ]
                         else:
                             # TODO: this is for backward compatibility, should
                             # be removed at some point.
-                            assocsByTimespan[Timespan(begin=d["begin"], end=d["end"])] = d["dataset_ids"]
+                            assocsByTimespan[Timespan(begin=d["begin"], end=d["end"])] = [
+                                x if not isinstance(x, int) else cast(DatasetId, _refIntId2UUID[x])
+                                for x in d["dataset_ids"]
+                            ]
                 else:
                     raise ValueError(f"Unexpected calibration type for association: {collectionType.name}.")
             else:
@@ -362,7 +375,14 @@ def __init__(self, stream: IO, registry: Registry):
                 FileDataset(
                     d.get("path"),
                     [
-                        DatasetRef(datasetType, dataId, run=data["run"], id=refid)
+                        DatasetRef(
+                            datasetType,
+                            dataId,
+                            run=data["run"],
+                            id=refid
+                            if not isinstance(refid, int)
+                            else cast(DatasetId, _refIntId2UUID[refid]),
+                        )
                         for dataId, refid in zip(
                             ensure_iterable(d["data_id"]), ensure_iterable(d["dataset_id"])
                         )