Fast imports part 3 (#9474)

* New intermediate inits

* Update template

* Avoid importing torch/tf/flax in tokenization unless necessary

* Styling

* Shutup flake8

* Better python version check

src/transformers/models/roberta/__init__.py

@@ -16,38 +16,103 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
-from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
-from .tokenization_roberta import RobertaTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import (
+    _BaseLazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig"],
+    "tokenization_roberta": ["RobertaTokenizer"],
+}
 
 if is_tokenizers_available():
-    from .tokenization_roberta_fast import RobertaTokenizerFast
+    _import_structure["tokenization_roberta_fast"] = ["RobertaTokenizerFast"]
 
 if is_torch_available():
-    from .modeling_roberta import (
-        ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
-        RobertaForCausalLM,
-        RobertaForMaskedLM,
-        RobertaForMultipleChoice,
-        RobertaForQuestionAnswering,
-        RobertaForSequenceClassification,
-        RobertaForTokenClassification,
-        RobertaModel,
-    )
+    _import_structure["modeling_roberta"] = [
+        "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "RobertaForCausalLM",
+        "RobertaForMaskedLM",
+        "RobertaForMultipleChoice",
+        "RobertaForQuestionAnswering",
+        "RobertaForSequenceClassification",
+        "RobertaForTokenClassification",
+        "RobertaModel",
+    ]
 
 if is_tf_available():
-    from .modeling_tf_roberta import (
-        TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
-        TFRobertaForMaskedLM,
-        TFRobertaForMultipleChoice,
-        TFRobertaForQuestionAnswering,
-        TFRobertaForSequenceClassification,
-        TFRobertaForTokenClassification,
-        TFRobertaMainLayer,
-        TFRobertaModel,
-        TFRobertaPreTrainedModel,
-    )
+    _import_structure["modeling_tf_roberta"] = [
+        "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFRobertaForMaskedLM",
+        "TFRobertaForMultipleChoice",
+        "TFRobertaForQuestionAnswering",
+        "TFRobertaForSequenceClassification",
+        "TFRobertaForTokenClassification",
+        "TFRobertaMainLayer",
+        "TFRobertaModel",
+        "TFRobertaPreTrainedModel",
+    ]
 
 if is_flax_available():
-    from .modeling_flax_roberta import FlaxRobertaModel
+    _import_structure["modeling_flax_roberta"] = ["FlaxRobertaModel"]
+
+
+if TYPE_CHECKING:
+    from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
+    from .tokenization_roberta import RobertaTokenizer
+
+    if is_tokenizers_available():
+        from .tokenization_roberta_fast import RobertaTokenizerFast
+
+    if is_torch_available():
+        from .modeling_roberta import (
+            ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            RobertaForCausalLM,
+            RobertaForMaskedLM,
+            RobertaForMultipleChoice,
+            RobertaForQuestionAnswering,
+            RobertaForSequenceClassification,
+            RobertaForTokenClassification,
+            RobertaModel,
+        )
+
+    if is_tf_available():
+        from .modeling_tf_roberta import (
+            TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFRobertaForMaskedLM,
+            TFRobertaForMultipleChoice,
+            TFRobertaForQuestionAnswering,
+            TFRobertaForSequenceClassification,
+            TFRobertaForTokenClassification,
+            TFRobertaMainLayer,
+            TFRobertaModel,
+            TFRobertaPreTrainedModel,
+        )
+
+    if is_flax_available():
+        from .modeling_flax_roberta import FlaxRobertaModel
+
+else:
+    import importlib
+    import os
+    import sys
+
+    class _LazyModule(_BaseLazyModule):
+        """
+        Module class that surfaces all objects but only performs associated imports when the objects are requested.
+        """
+
+        __file__ = globals()["__file__"]
+        __path__ = [os.path.dirname(__file__)]
+
+        def _get_module(self, module_name: str):
+            return importlib.import_module("." + module_name, self.__name__)
+
+    sys.modules[__name__] = _LazyModule(__name__, _import_structure)