Merge pull request #2288 from aaugustin/better-handle-optional-imports

Improve handling of optional imports

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/metrics/__init__.py

@@ -14,18 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-
-
-logger = logging.getLogger(__name__)
-
 try:
     from scipy.stats import pearsonr, spearmanr
     from sklearn.metrics import matthews_corrcoef, f1_score
 
     _has_sklearn = True
 except (AttributeError, ImportError) as e:
-    logger.warning("To use data.metrics please install scikit-learn. See https://scikit-learn.org/stable/index.html")
     _has_sklearn = 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/modeling_tf_pytorch_utils.py

@@ -76,12 +76,12 @@ def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_i
     try:
         import tensorflow as tf  # noqa: F401
         import torch  # noqa: F401
-    except ImportError as e:
+    except ImportError:
         logger.error(
             "Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see "
             "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
         )
-        raise e
+        raise
 
     pt_path = os.path.abspath(pytorch_checkpoint_path)
     logger.info("Loading PyTorch weights from {}".format(pt_path))
@@ -111,12 +111,12 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
         import torch  # noqa: F401
         import tensorflow as tf  # noqa: F401
         from tensorflow.python.keras import backend as K
-    except ImportError as e:
+    except ImportError:
         logger.error(
             "Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see "
             "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
         )
-        raise e
+        raise
 
     if tf_inputs is None:
         tf_inputs = tf_model.dummy_inputs
@@ -209,12 +209,12 @@ def load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path, tf_inputs
     try:
         import tensorflow as tf  # noqa: F401
         import torch  # noqa: F401
-    except ImportError as e:
+    except ImportError:
         logger.error(
             "Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
             "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
         )
-        raise e
+        raise
 
     import transformers
 
@@ -251,12 +251,12 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
     try:
         import tensorflow as tf  # noqa: F401
         import torch  # noqa: F401
-    except ImportError as e:
+    except ImportError:
         logger.error(
             "Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
             "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
         )
-        raise e
+        raise
 
     new_pt_params_dict = {}
     current_pt_params_dict = dict(pt_model.named_parameters())

src/transformers/modeling_utils.py

@@ -454,12 +454,12 @@ class PreTrainedModel(nn.Module):
                     from transformers import load_tf2_checkpoint_in_pytorch_model
 
                     model = load_tf2_checkpoint_in_pytorch_model(model, resolved_archive_file, allow_missing_keys=True)
-                except ImportError as e:
+                except ImportError:
                     logger.error(
                         "Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed. Please see "
                         "https://pytorch.org/ and https://www.tensorflow.org/install/ for installation instructions."
                     )
-                    raise e
+                    raise
         else:
             # Convert old format to new format if needed from a PyTorch state_dict
             old_keys = []

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/."

src/transformers/tokenization_albert.py

@@ -100,6 +100,7 @@ class AlbertTokenizer(PreTrainedTokenizer):
                 "You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece"
                 "pip install sentencepiece"
             )
+            raise
 
         self.do_lower_case = do_lower_case
         self.remove_space = remove_space
@@ -127,6 +128,7 @@ class AlbertTokenizer(PreTrainedTokenizer):
                 "You need to install SentencePiece to use AlbertTokenizer: https://github.com/google/sentencepiece"
                 "pip install sentencepiece"
             )
+            raise
         self.sp_model = spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file)
 

src/transformers/tokenization_t5.py

@@ -107,6 +107,7 @@ class T5Tokenizer(PreTrainedTokenizer):
                 "https://github.com/google/sentencepiece"
                 "pip install sentencepiece"
             )
+            raise
 
         self.vocab_file = vocab_file
         self._extra_ids = extra_ids
@@ -132,6 +133,7 @@ class T5Tokenizer(PreTrainedTokenizer):
                 "You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
                 "pip install sentencepiece"
             )
+            raise
         self.sp_model = spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file)
 

src/transformers/tokenization_transfo_xl.py

@@ -26,14 +26,12 @@ from collections import Counter, OrderedDict
 
 import numpy as np
 
-from .file_utils import cached_path
+from .file_utils import cached_path, is_torch_available
 from .tokenization_utils import PreTrainedTokenizer
 
 
-try:
+if is_torch_available():
     import torch
-except ImportError:
-    pass
 
 
 logger = logging.getLogger(__name__)

src/transformers/tokenization_xlm.py

@@ -646,7 +646,7 @@ class XLMTokenizer(PreTrainedTokenizer):
                 self.ja_word_tokenizer = Mykytea.Mykytea(
                     "-model %s/local/share/kytea/model.bin" % os.path.expanduser("~")
                 )
-            except (AttributeError, ImportError) as e:
+            except (AttributeError, ImportError):
                 logger.error(
                     "Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper (https://github.com/chezou/Mykytea-python) with the following steps"
                 )
@@ -655,7 +655,7 @@ class XLMTokenizer(PreTrainedTokenizer):
                 logger.error("3. ./configure --prefix=$HOME/local")
                 logger.error("4. make && make install")
                 logger.error("5. pip install kytea")
-                raise e
+                raise
         return list(self.ja_word_tokenizer.getWS(text))
 
     @property
@@ -760,12 +760,12 @@ class XLMTokenizer(PreTrainedTokenizer):
                     from pythainlp.tokenize import word_tokenize as th_word_tokenize
                 else:
                     th_word_tokenize = sys.modules["pythainlp"].word_tokenize
-            except (AttributeError, ImportError) as e:
+            except (AttributeError, ImportError):
                 logger.error(
                     "Make sure you install PyThaiNLP (https://github.com/PyThaiNLP/pythainlp) with the following steps"
                 )
                 logger.error("1. pip install pythainlp")
-                raise e
+                raise
             text = th_word_tokenize(text)
         elif lang == "zh":
             try:
@@ -773,10 +773,10 @@ class XLMTokenizer(PreTrainedTokenizer):
                     import jieba
                 else:
                     jieba = sys.modules["jieba"]
-            except (AttributeError, ImportError) as e:
+            except (AttributeError, ImportError):
                 logger.error("Make sure you install Jieba (https://github.com/fxsjy/jieba) with the following steps")
                 logger.error("1. pip install jieba")
-                raise e
+                raise
             text = " ".join(jieba.cut(text))
             text = self.moses_pipeline(text, lang=lang)
             text = text.split()

src/transformers/tokenization_xlnet.py

@@ -100,6 +100,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
                 "You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
                 "pip install sentencepiece"
             )
+            raise
 
         self.do_lower_case = do_lower_case
         self.remove_space = remove_space
@@ -127,6 +128,7 @@ class XLNetTokenizer(PreTrainedTokenizer):
                 "You need to install SentencePiece to use XLNetTokenizer: https://github.com/google/sentencepiece"
                 "pip install sentencepiece"
             )
+            raise
         self.sp_model = spm.SentencePieceProcessor()
         self.sp_model.Load(self.vocab_file)