Allow soft dependencies in the namespace with ImportErrors at use (#7537)

* PoC on RAG

* Format class name/obj name

* Better name in message

* PoC on one TF model

* Add PyTorch and TF dummy objects + script

* Treat scikit-learn

* Bad copy pastes

* Typo

src/transformers/data/__init__.py

@@ -2,7 +2,7 @@
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.
 
-from .metrics import is_sklearn_available
+from .metrics import glue_compute_metrics, xnli_compute_metrics
 from .processors import (
     DataProcessor,
     InputExample,
@@ -21,7 +21,3 @@ from .processors import (
     xnli_processors,
     xnli_tasks_num_labels,
 )
-
-
-if is_sklearn_available():
-    from .metrics import glue_compute_metrics, xnli_compute_metrics

src/transformers/data/metrics/__init__.py

@@ -14,77 +14,75 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-try:
+from ...file_utils import is_sklearn_available, requires_sklearn
+
+
+if is_sklearn_available():
     from sklearn.metrics import f1_score, matthews_corrcoef
 
     from scipy.stats import pearsonr, spearmanr
 
-    _has_sklearn = True
-except (AttributeError, ImportError):
-    _has_sklearn = False
+
+def simple_accuracy(preds, labels):
+    requires_sklearn(simple_accuracy)
+    return (preds == labels).mean()
 
 
-def is_sklearn_available():
-    return _has_sklearn
+def acc_and_f1(preds, labels):
+    requires_sklearn(acc_and_f1)
+    acc = simple_accuracy(preds, labels)
+    f1 = f1_score(y_true=labels, y_pred=preds)
+    return {
+        "acc": acc,
+        "f1": f1,
+        "acc_and_f1": (acc + f1) / 2,
+    }
 
 
-if _has_sklearn:
+def pearson_and_spearman(preds, labels):
+    requires_sklearn(pearson_and_spearman)
+    pearson_corr = pearsonr(preds, labels)[0]
+    spearman_corr = spearmanr(preds, labels)[0]
+    return {
+        "pearson": pearson_corr,
+        "spearmanr": spearman_corr,
+        "corr": (pearson_corr + spearman_corr) / 2,
+    }
 
-    def simple_accuracy(preds, labels):
-        return (preds == labels).mean()
 
-    def acc_and_f1(preds, labels):
-        acc = simple_accuracy(preds, labels)
-        f1 = f1_score(y_true=labels, y_pred=preds)
-        return {
-            "acc": acc,
-            "f1": f1,
-            "acc_and_f1": (acc + f1) / 2,
-        }
+def glue_compute_metrics(task_name, preds, labels):
+    requires_sklearn(glue_compute_metrics)
+    assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
+    if task_name == "cola":
+        return {"mcc": matthews_corrcoef(labels, preds)}
+    elif task_name == "sst-2":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "mrpc":
+        return acc_and_f1(preds, labels)
+    elif task_name == "sts-b":
+        return pearson_and_spearman(preds, labels)
+    elif task_name == "qqp":
+        return acc_and_f1(preds, labels)
+    elif task_name == "mnli":
+        return {"mnli/acc": simple_accuracy(preds, labels)}
+    elif task_name == "mnli-mm":
+        return {"mnli-mm/acc": simple_accuracy(preds, labels)}
+    elif task_name == "qnli":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "rte":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "wnli":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "hans":
+        return {"acc": simple_accuracy(preds, labels)}
+    else:
+        raise KeyError(task_name)
 
-    def pearson_and_spearman(preds, labels):
-        pearson_corr = pearsonr(preds, labels)[0]
-        spearman_corr = spearmanr(preds, labels)[0]
-        return {
-            "pearson": pearson_corr,
-            "spearmanr": spearman_corr,
-            "corr": (pearson_corr + spearman_corr) / 2,
-        }
 
-    def glue_compute_metrics(task_name, preds, labels):
-        assert len(preds) == len(
-            labels
-        ), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
-        if task_name == "cola":
-            return {"mcc": matthews_corrcoef(labels, preds)}
-        elif task_name == "sst-2":
-            return {"acc": simple_accuracy(preds, labels)}
-        elif task_name == "mrpc":
-            return acc_and_f1(preds, labels)
-        elif task_name == "sts-b":
-            return pearson_and_spearman(preds, labels)
-        elif task_name == "qqp":
-            return acc_and_f1(preds, labels)
-        elif task_name == "mnli":
-            return {"mnli/acc": simple_accuracy(preds, labels)}
-        elif task_name == "mnli-mm":
-            return {"mnli-mm/acc": simple_accuracy(preds, labels)}
-        elif task_name == "qnli":
-            return {"acc": simple_accuracy(preds, labels)}
-        elif task_name == "rte":
-            return {"acc": simple_accuracy(preds, labels)}
-        elif task_name == "wnli":
-            return {"acc": simple_accuracy(preds, labels)}
-        elif task_name == "hans":
-            return {"acc": simple_accuracy(preds, labels)}
-        else:
-            raise KeyError(task_name)
-
-    def xnli_compute_metrics(task_name, preds, labels):
-        assert len(preds) == len(
-            labels
-        ), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
-        if task_name == "xnli":
-            return {"acc": simple_accuracy(preds, labels)}
-        else:
-            raise KeyError(task_name)
+def xnli_compute_metrics(task_name, preds, labels):
+    requires_sklearn(xnli_compute_metrics)
+    assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}"
+    if task_name == "xnli":
+        return {"acc": simple_accuracy(preds, labels)}
+    else:
+        raise KeyError(task_name)