add repo_id info

src/datasets/arrow_dataset.py

@@ -221,6 +221,10 @@ def task_templates(self):
     def version(self):
         return self._info.version
 
+    @property
+    def repo_id(self) -> str:
+        return self._info.repo_id
+
 
 class TensorflowDatasetMixin:
     _TF_DATASET_REFS = set()

src/datasets/builder.py

@@ -390,6 +390,8 @@ def __init__(
         # update info with user specified infos
         if features is not None:
             self.info.features = features
+        if repo_id is not None:
+            self.info.repo_id = repo_id
 
         # Prepare data dirs:
         # cache_dir can be a remote bucket on GCS or S3 (when using BeamBasedBuilder for distributed data processing)
@@ -417,7 +419,7 @@ def __init__(
                     if len(os.listdir(self._cache_dir)) > 0:
                         if os.path.exists(path_join(self._cache_dir, config.DATASET_INFO_FILENAME)):
                             logger.info("Overwrite dataset info from restored data version if exists.")
-                            self.info = DatasetInfo.from_directory(self._cache_dir)
+                            self.info.update(DatasetInfo.from_directory(self._cache_dir))
                     else:  # dir exists but no data, remove the empty dir as data aren't available anymore
                         logger.warning(
                             f"Old caching folder {self._cache_dir} for dataset {self.dataset_name} exists but no data were found. Removing it. "
@@ -882,7 +884,7 @@ def download_and_prepare(
                 logger.info(f"Found cached dataset {self.dataset_name} ({self._output_dir})")
                 # We need to update the info in case some splits were added in the meantime
                 # for example when calling load_dataset from multiple workers.
-                self.info = self._load_info()
+                self.info.update(self._load_info())
                 self.download_post_processing_resources(dl_manager)
                 return
 

src/datasets/info.py

@@ -153,6 +153,7 @@ class DatasetInfo:
     dataset_name: Optional[str] = None  # for packaged builders, to be different from builder_name
     config_name: Optional[str] = None
     version: Optional[Union[str, Version]] = None
+    repo_id: Optional[str] = None
     # Set later by `download_and_prepare`
     splits: Optional[dict] = None
     download_checksums: Optional[dict] = None
@@ -282,6 +283,12 @@ def from_merge(cls, dataset_infos: List["DatasetInfo"]):
         supervised_keys = None
         task_templates = None
 
+        repo_ids = {dset_info.repo_id for dset_info in dataset_infos}
+        if len(repo_ids) == 1 and next(iter(repo_ids)) is not None:
+            repo_id = next(iter(repo_ids))
+        else:
+            repo_id = None
+
         # Find common task templates across all dataset infos
         all_task_templates = [info.task_templates for info in dataset_infos if info.task_templates is not None]
         if len(all_task_templates) > 1:
@@ -299,6 +306,7 @@ def from_merge(cls, dataset_infos: List["DatasetInfo"]):
             features=features,
             supervised_keys=supervised_keys,
             task_templates=task_templates,
+            repo_id=repo_id,
         )
 
     @classmethod