Improve exception type.

ImportError isn't really appropriate when there's no import involved.

src/transformers/commands/serving.py

@@ -107,7 +107,7 @@ class ServeCommand(BaseTransformersCLICommand):
         self._host = host
         self._port = port
         if not _serve_dependancies_installed:
-            raise ImportError(
+            raise RuntimeError(
                 "Using serve command requires FastAPI and unicorn. "
                 "Please install transformers with [serving]: pip install transformers[serving]."
                 "Or install FastAPI and unicorn separatly."

src/transformers/commands/train.py

@@ -8,7 +8,7 @@ from transformers.commands import BaseTransformersCLICommand
 
 
 if not is_tf_available() and not is_torch_available():
-    raise ImportError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")
+    raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")
 
 # TF training parameters
 USE_XLA = False

src/transformers/data/processors/squad.py

@@ -324,7 +324,7 @@ def squad_convert_examples_to_features(
     del new_features
     if return_dataset == "pt":
         if not is_torch_available():
-            raise ImportError("Pytorch must be installed to return a pytorch dataset.")
+            raise RuntimeError("PyTorch must be installed to return a PyTorch dataset.")
 
         # Convert to Tensors and build dataset
         all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
@@ -354,7 +354,7 @@ def squad_convert_examples_to_features(
         return features, dataset
     elif return_dataset == "tf":
         if not is_tf_available():
-            raise ImportError("TensorFlow must be installed to return a TensorFlow dataset.")
+            raise RuntimeError("TensorFlow must be installed to return a TensorFlow dataset.")
 
         def gen():
             for ex in features:

src/transformers/data/processors/utils.py

@@ -294,7 +294,7 @@ class SingleSentenceClassificationProcessor(DataProcessor):
             return features
         elif return_tensors == "tf":
             if not is_tf_available():
-                raise ImportError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
+                raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
             import tensorflow as tf
 
             def gen():
@@ -309,7 +309,7 @@ class SingleSentenceClassificationProcessor(DataProcessor):
             return dataset
         elif return_tensors == "pt":
             if not is_torch_available():
-                raise ImportError("return_tensors set to 'pt' but PyTorch can't be imported")
+                raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
             import torch
             from torch.utils.data import TensorDataset
 

src/transformers/pipelines.py

@@ -68,7 +68,7 @@ def get_framework(model=None):
         # Try to guess which framework to use from the model classname
         framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
     elif not is_tf_available() and not is_torch_available():
-        raise ImportError(
+        raise RuntimeError(
             "At least one of TensorFlow 2.0 or PyTorch should be installed. "
             "To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ "
             "To install PyTorch, read the instructions at https://pytorch.org/."