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/file_utils.py

@@ -51,7 +51,7 @@ from .utils import logging
 
 
 # The package importlib_metadata is in a different place, depending on the python version.
-if version.parse(sys.version) < version.parse("3.8"):
+if sys.version_info < (3, 8):
     import importlib_metadata
 else:
     import importlib.metadata as importlib_metadata

src/transformers/models/albert/__init__.py

@@ -16,17 +16,66 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available
-from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
+from typing import TYPE_CHECKING
 
+from ...file_utils import (
+    _BaseLazyModule,
+    is_sentencepiece_available,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig"],
+}
 
 if is_sentencepiece_available():
-    from .tokenization_albert import AlbertTokenizer
+    _import_structure["tokenization_albert"] = ["AlbertTokenizer"]
 
 if is_tokenizers_available():
-    from .tokenization_albert_fast import AlbertTokenizerFast
+    _import_structure["tokenization_albert_fast"] = ["AlbertTokenizerFast"]
 
 if is_torch_available():
+    _import_structure["modeling_albert"] = [
+        "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "AlbertForMaskedLM",
+        "AlbertForMultipleChoice",
+        "AlbertForPreTraining",
+        "AlbertForQuestionAnswering",
+        "AlbertForSequenceClassification",
+        "AlbertForTokenClassification",
+        "AlbertModel",
+        "AlbertPreTrainedModel",
+        "load_tf_weights_in_albert",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_albert"] = [
+        "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFAlbertForMaskedLM",
+        "TFAlbertForMultipleChoice",
+        "TFAlbertForPreTraining",
+        "TFAlbertForQuestionAnswering",
+        "TFAlbertForSequenceClassification",
+        "TFAlbertForTokenClassification",
+        "TFAlbertMainLayer",
+        "TFAlbertModel",
+        "TFAlbertPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
+
+    if is_sentencepiece_available():
+        from .tokenization_albert import AlbertTokenizer
+
+    if is_tokenizers_available():
+        from .tokenization_albert_fast import AlbertTokenizerFast
+
+    if is_torch_available():
         from .modeling_albert import (
             ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             AlbertForMaskedLM,
@@ -40,7 +89,7 @@ if is_torch_available():
             load_tf_weights_in_albert,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_albert import (
             TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFAlbertForMaskedLM,
@@ -53,3 +102,21 @@ if is_tf_available():
             TFAlbertModel,
             TFAlbertPreTrainedModel,
         )
+
+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)

src/transformers/models/auto/__init__.py

@@ -16,12 +16,78 @@
 # 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_torch_available
-from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
-from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_flax_available, is_tf_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
+    "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
+}
 
 if is_torch_available():
+    _import_structure["modeling_auto"] = [
+        "MODEL_FOR_CAUSAL_LM_MAPPING",
+        "MODEL_FOR_MASKED_LM_MAPPING",
+        "MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
+        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
+        "MODEL_FOR_PRETRAINING_MAPPING",
+        "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
+        "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
+        "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
+        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
+        "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
+        "MODEL_MAPPING",
+        "MODEL_WITH_LM_HEAD_MAPPING",
+        "AutoModel",
+        "AutoModelForCausalLM",
+        "AutoModelForMaskedLM",
+        "AutoModelForMultipleChoice",
+        "AutoModelForNextSentencePrediction",
+        "AutoModelForPreTraining",
+        "AutoModelForQuestionAnswering",
+        "AutoModelForSeq2SeqLM",
+        "AutoModelForSequenceClassification",
+        "AutoModelForTableQuestionAnswering",
+        "AutoModelForTokenClassification",
+        "AutoModelWithLMHead",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_auto"] = [
+        "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
+        "TF_MODEL_FOR_MASKED_LM_MAPPING",
+        "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
+        "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
+        "TF_MODEL_FOR_PRETRAINING_MAPPING",
+        "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
+        "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
+        "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
+        "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
+        "TF_MODEL_MAPPING",
+        "TF_MODEL_WITH_LM_HEAD_MAPPING",
+        "TFAutoModel",
+        "TFAutoModelForCausalLM",
+        "TFAutoModelForMaskedLM",
+        "TFAutoModelForMultipleChoice",
+        "TFAutoModelForPreTraining",
+        "TFAutoModelForQuestionAnswering",
+        "TFAutoModelForSeq2SeqLM",
+        "TFAutoModelForSequenceClassification",
+        "TFAutoModelForTokenClassification",
+        "TFAutoModelWithLMHead",
+    ]
+
+if is_flax_available():
+    _import_structure["modeling_flax_auto"] = ["FLAX_MODEL_MAPPING", "FlaxAutoModel"]
+
+
+if TYPE_CHECKING:
+    from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
+    from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
+
+    if is_torch_available():
         from .modeling_auto import (
             MODEL_FOR_CAUSAL_LM_MAPPING,
             MODEL_FOR_MASKED_LM_MAPPING,
@@ -49,7 +115,7 @@ if is_torch_available():
             AutoModelWithLMHead,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_auto import (
             TF_MODEL_FOR_CAUSAL_LM_MAPPING,
             TF_MODEL_FOR_MASKED_LM_MAPPING,
@@ -74,5 +140,23 @@ if is_tf_available():
             TFAutoModelWithLMHead,
         )
 
-if is_flax_available():
+    if is_flax_available():
         from .modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel
+
+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)

src/transformers/models/bart/__init__.py

@@ -15,15 +15,42 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from ...file_utils import is_tf_available, is_tokenizers_available, is_torch_available
-from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig
-from .tokenization_bart import BartTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig"],
+    "tokenization_bart": ["BartTokenizer"],
+}
 
 if is_tokenizers_available():
-    from .tokenization_bart_fast import BartTokenizerFast
+    _import_structure["tokenization_bart_fast"] = ["BartTokenizerFast"]
 
 if is_torch_available():
+    _import_structure["modeling_bart"] = [
+        "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "BartForConditionalGeneration",
+        "BartForQuestionAnswering",
+        "BartForSequenceClassification",
+        "BartModel",
+        "BartPretrainedModel",
+        "PretrainedBartModel",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_bart"] = ["TFBartForConditionalGeneration", "TFBartModel", "TFBartPretrainedModel"]
+
+
+if TYPE_CHECKING:
+    from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig
+    from .tokenization_bart import BartTokenizer
+
+    if is_tokenizers_available():
+        from .tokenization_bart_fast import BartTokenizerFast
+
+    if is_torch_available():
         from .modeling_bart import (
             BART_PRETRAINED_MODEL_ARCHIVE_LIST,
             BartForConditionalGeneration,
@@ -34,5 +61,23 @@ if is_torch_available():
             PretrainedBartModel,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_bart import TFBartForConditionalGeneration, TFBartModel, TFBartPretrainedModel
+
+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)

src/transformers/models/barthez/__init__.py

@@ -16,11 +16,42 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_sentencepiece_available, is_tokenizers_available
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_sentencepiece_available, is_tokenizers_available
+
+
+_import_structure = {}
 
 if is_sentencepiece_available():
-    from .tokenization_barthez import BarthezTokenizer
+    _import_structure["tokenization_barthez"] = ["BarthezTokenizer"]
 
 if is_tokenizers_available():
+    _import_structure["tokenization_barthez_fast"] = ["BarthezTokenizerFast"]
+
+
+if TYPE_CHECKING:
+
+    if is_sentencepiece_available():
+        from .tokenization_barthez import BarthezTokenizer
+
+    if is_tokenizers_available():
         from .tokenization_barthez_fast import BarthezTokenizerFast
+
+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)

src/transformers/models/bert/__init__.py

@@ -16,15 +16,71 @@
 # 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_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
-from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
+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_bert": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertConfig"],
+    "tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
+}
 
 if is_tokenizers_available():
-    from .tokenization_bert_fast import BertTokenizerFast
+    _import_structure["tokenization_bert_fast"] = ["BertTokenizerFast"]
 
 if is_torch_available():
+    _import_structure["modeling_bert"] = [
+        "BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "BertForMaskedLM",
+        "BertForMultipleChoice",
+        "BertForNextSentencePrediction",
+        "BertForPreTraining",
+        "BertForQuestionAnswering",
+        "BertForSequenceClassification",
+        "BertForTokenClassification",
+        "BertLayer",
+        "BertLMHeadModel",
+        "BertModel",
+        "BertPreTrainedModel",
+        "load_tf_weights_in_bert",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_bert"] = [
+        "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFBertEmbeddings",
+        "TFBertForMaskedLM",
+        "TFBertForMultipleChoice",
+        "TFBertForNextSentencePrediction",
+        "TFBertForPreTraining",
+        "TFBertForQuestionAnswering",
+        "TFBertForSequenceClassification",
+        "TFBertForTokenClassification",
+        "TFBertLMHeadModel",
+        "TFBertMainLayer",
+        "TFBertModel",
+        "TFBertPreTrainedModel",
+    ]
+
+if is_flax_available():
+    _import_structure["modeling_flax_bert"] = ["FlaxBertForMaskedLM", "FlaxBertModel"]
+
+
+if TYPE_CHECKING:
+    from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
+    from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
+
+    if is_tokenizers_available():
+        from .tokenization_bert_fast import BertTokenizerFast
+
+    if is_torch_available():
         from .modeling_bert import (
             BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             BertForMaskedLM,
@@ -41,7 +97,7 @@ if is_torch_available():
             load_tf_weights_in_bert,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_bert import (
             TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFBertEmbeddings,
@@ -58,5 +114,23 @@ if is_tf_available():
             TFBertPreTrainedModel,
         )
 
-if is_flax_available():
+    if is_flax_available():
         from .modeling_flax_bert import FlaxBertForMaskedLM, FlaxBertModel
+
+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)

src/transformers/models/bert_generation/__init__.py

@@ -16,16 +16,53 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_sentencepiece_available, is_torch_available
-from .configuration_bert_generation import BertGenerationConfig
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_sentencepiece_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_bert_generation": ["BertGenerationConfig"],
+}
 
 if is_sentencepiece_available():
-    from .tokenization_bert_generation import BertGenerationTokenizer
+    _import_structure["tokenization_bert_generation"] = ["BertGenerationTokenizer"]
 
 if is_torch_available():
+    _import_structure["modeling_bert_generation"] = [
+        "BertGenerationDecoder",
+        "BertGenerationEncoder",
+        "load_tf_weights_in_bert_generation",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_bert_generation import BertGenerationConfig
+
+    if is_sentencepiece_available():
+        from .tokenization_bert_generation import BertGenerationTokenizer
+
+    if is_torch_available():
         from .modeling_bert_generation import (
             BertGenerationDecoder,
             BertGenerationEncoder,
             load_tf_weights_in_bert_generation,
         )
+
+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)

src/transformers/models/bert_japanese/__init__.py

@@ -16,4 +16,33 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
+from typing import TYPE_CHECKING
+
+from ...file_utils import _BaseLazyModule
+
+
+_import_structure = {
+    "tokenization_bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"],
+}
+
+
+if TYPE_CHECKING:
+    from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
+
+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)

src/transformers/models/bertweet/__init__.py

@@ -16,4 +16,33 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .tokenization_bertweet import BertweetTokenizer
+from typing import TYPE_CHECKING
+
+from ...file_utils import _BaseLazyModule
+
+
+_import_structure = {
+    "tokenization_bertweet": ["BertweetTokenizer"],
+}
+
+
+if TYPE_CHECKING:
+    from .tokenization_bertweet import BertweetTokenizer
+
+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)

src/transformers/models/blenderbot/__init__.py

@@ -16,12 +16,34 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_tf_available, is_torch_available
-from .configuration_blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig
-from .tokenization_blenderbot import BlenderbotTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_tf_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_blenderbot": ["BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotConfig"],
+    "tokenization_blenderbot": ["BlenderbotTokenizer"],
+}
 
 if is_torch_available():
+    _import_structure["modeling_blenderbot"] = [
+        "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "BlenderbotForConditionalGeneration",
+        "BlenderbotModel",
+        "BlenderbotPreTrainedModel",
+    ]
+
+
+if is_tf_available():
+    _import_structure["modeling_tf_blenderbot"] = ["TFBlenderbotForConditionalGeneration"]
+
+
+if TYPE_CHECKING:
+    from .configuration_blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig
+    from .tokenization_blenderbot import BlenderbotTokenizer
+
+    if is_torch_available():
         from .modeling_blenderbot import (
             BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
             BlenderbotForConditionalGeneration,
@@ -29,6 +51,23 @@ if is_torch_available():
             BlenderbotPreTrainedModel,
         )
 
-
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_blenderbot import TFBlenderbotForConditionalGeneration
+
+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)

src/transformers/models/blenderbot_small/__init__.py

@@ -15,15 +15,51 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from ...file_utils import is_torch_available
-from .configuration_blenderbot_small import BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotSmallConfig
-from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_blenderbot_small": ["BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotSmallConfig"],
+    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
+}
 
 if is_torch_available():
+    _import_structure["modeling_blenderbot_small"] = [
+        "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "BlenderbotSmallForConditionalGeneration",
+        "BlenderbotSmallModel",
+        "BlenderbotSmallPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_blenderbot_small import BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotSmallConfig
+    from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
+
+    if is_torch_available():
         from .modeling_blenderbot_small import (
             BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
             BlenderbotSmallForConditionalGeneration,
             BlenderbotSmallModel,
             BlenderbotSmallPreTrainedModel,
         )
+
+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)

src/transformers/models/camembert/__init__.py

@@ -16,17 +16,61 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available
-from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
+from typing import TYPE_CHECKING
 
+from ...file_utils import (
+    _BaseLazyModule,
+    is_sentencepiece_available,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"],
+}
 
 if is_sentencepiece_available():
-    from .tokenization_camembert import CamembertTokenizer
+    _import_structure["tokenization_camembert"] = ["CamembertTokenizer"]
 
 if is_tokenizers_available():
-    from .tokenization_camembert_fast import CamembertTokenizerFast
+    _import_structure["tokenization_camembert_fast"] = ["CamembertTokenizerFast"]
 
 if is_torch_available():
+    _import_structure["modeling_camembert"] = [
+        "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "CamembertForCausalLM",
+        "CamembertForMaskedLM",
+        "CamembertForMultipleChoice",
+        "CamembertForQuestionAnswering",
+        "CamembertForSequenceClassification",
+        "CamembertForTokenClassification",
+        "CamembertModel",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_camembert"] = [
+        "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFCamembertForMaskedLM",
+        "TFCamembertForMultipleChoice",
+        "TFCamembertForQuestionAnswering",
+        "TFCamembertForSequenceClassification",
+        "TFCamembertForTokenClassification",
+        "TFCamembertModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
+
+    if is_sentencepiece_available():
+        from .tokenization_camembert import CamembertTokenizer
+
+    if is_tokenizers_available():
+        from .tokenization_camembert_fast import CamembertTokenizerFast
+
+    if is_torch_available():
         from .modeling_camembert import (
             CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             CamembertForCausalLM,
@@ -38,7 +82,7 @@ if is_torch_available():
             CamembertModel,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_camembert import (
             TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFCamembertForMaskedLM,
@@ -48,3 +92,21 @@ if is_tf_available():
             TFCamembertForTokenClassification,
             TFCamembertModel,
         )
+
+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)

src/transformers/models/ctrl/__init__.py

@@ -16,12 +16,40 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_tf_available, is_torch_available
-from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
-from .tokenization_ctrl import CTRLTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_tf_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
+    "tokenization_ctrl": ["CTRLTokenizer"],
+}
 
 if is_torch_available():
+    _import_structure["modeling_ctrl"] = [
+        "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "CTRLForSequenceClassification",
+        "CTRLLMHeadModel",
+        "CTRLModel",
+        "CTRLPreTrainedModel",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_ctrl"] = [
+        "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFCTRLForSequenceClassification",
+        "TFCTRLLMHeadModel",
+        "TFCTRLModel",
+        "TFCTRLPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
+    from .tokenization_ctrl import CTRLTokenizer
+
+    if is_torch_available():
         from .modeling_ctrl import (
             CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
             CTRLForSequenceClassification,
@@ -30,7 +58,7 @@ if is_torch_available():
             CTRLPreTrainedModel,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_ctrl import (
             TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFCTRLForSequenceClassification,
@@ -38,3 +66,21 @@ if is_tf_available():
             TFCTRLModel,
             TFCTRLPreTrainedModel,
         )
+
+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)

src/transformers/models/deberta/__init__.py

@@ -16,15 +16,51 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_torch_available
-from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig
-from .tokenization_deberta import DebertaTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig"],
+    "tokenization_deberta": ["DebertaTokenizer"],
+}
 
 if is_torch_available():
+    _import_structure["modeling_deberta"] = [
+        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "DebertaForSequenceClassification",
+        "DebertaModel",
+        "DebertaPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig
+    from .tokenization_deberta import DebertaTokenizer
+
+    if is_torch_available():
         from .modeling_deberta import (
             DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
             DebertaForSequenceClassification,
             DebertaModel,
             DebertaPreTrainedModel,
         )
+
+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)

src/transformers/models/distilbert/__init__.py

@@ -16,15 +16,53 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_tf_available, is_tokenizers_available, is_torch_available
-from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
-from .tokenization_distilbert import DistilBertTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_distilbert": ["DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DistilBertConfig"],
+    "tokenization_distilbert": ["DistilBertTokenizer"],
+}
 
 if is_tokenizers_available():
-    from .tokenization_distilbert_fast import DistilBertTokenizerFast
+    _import_structure["tokenization_distilbert_fast"] = ["DistilBertTokenizerFast"]
 
 if is_torch_available():
+    _import_structure["modeling_distilbert"] = [
+        "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "DistilBertForMaskedLM",
+        "DistilBertForMultipleChoice",
+        "DistilBertForQuestionAnswering",
+        "DistilBertForSequenceClassification",
+        "DistilBertForTokenClassification",
+        "DistilBertModel",
+        "DistilBertPreTrainedModel",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_distilbert"] = [
+        "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFDistilBertForMaskedLM",
+        "TFDistilBertForMultipleChoice",
+        "TFDistilBertForQuestionAnswering",
+        "TFDistilBertForSequenceClassification",
+        "TFDistilBertForTokenClassification",
+        "TFDistilBertMainLayer",
+        "TFDistilBertModel",
+        "TFDistilBertPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
+    from .tokenization_distilbert import DistilBertTokenizer
+
+    if is_tokenizers_available():
+        from .tokenization_distilbert_fast import DistilBertTokenizerFast
+
+    if is_torch_available():
         from .modeling_distilbert import (
             DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             DistilBertForMaskedLM,
@@ -36,7 +74,7 @@ if is_torch_available():
             DistilBertPreTrainedModel,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_distilbert import (
             TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFDistilBertForMaskedLM,
@@ -48,3 +86,21 @@ if is_tf_available():
             TFDistilBertModel,
             TFDistilBertPreTrainedModel,
         )
+
+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)

src/transformers/models/dpr/__init__.py

@@ -16,24 +16,73 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_tf_available, is_tokenizers_available, is_torch_available
-from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
-from .tokenization_dpr import (
+from typing import TYPE_CHECKING
+
+from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
+    "tokenization_dpr": [
+        "DPRContextEncoderTokenizer",
+        "DPRQuestionEncoderTokenizer",
+        "DPRReaderOutput",
+        "DPRReaderTokenizer",
+    ],
+}
+
+
+if is_tokenizers_available():
+    _import_structure["tokenization_dpr_fast"] = [
+        "DPRContextEncoderTokenizerFast",
+        "DPRQuestionEncoderTokenizerFast",
+        "DPRReaderTokenizerFast",
+    ]
+
+if is_torch_available():
+    _import_structure["modeling_dpr"] = [
+        "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "DPRContextEncoder",
+        "DPRPretrainedContextEncoder",
+        "DPRPretrainedQuestionEncoder",
+        "DPRPretrainedReader",
+        "DPRQuestionEncoder",
+        "DPRReader",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_dpr"] = [
+        "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFDPRContextEncoder",
+        "TFDPRPretrainedContextEncoder",
+        "TFDPRPretrainedQuestionEncoder",
+        "TFDPRPretrainedReader",
+        "TFDPRQuestionEncoder",
+        "TFDPRReader",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
+    from .tokenization_dpr import (
         DPRContextEncoderTokenizer,
         DPRQuestionEncoderTokenizer,
         DPRReaderOutput,
         DPRReaderTokenizer,
-)
-
+    )
 
-if is_tokenizers_available():
+    if is_tokenizers_available():
         from .tokenization_dpr_fast import (
             DPRContextEncoderTokenizerFast,
             DPRQuestionEncoderTokenizerFast,
             DPRReaderTokenizerFast,
         )
 
-if is_torch_available():
+    if is_torch_available():
         from .modeling_dpr import (
             DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
             DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
@@ -46,7 +95,7 @@ if is_torch_available():
             DPRReader,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_dpr import (
             TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
             TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
@@ -58,3 +107,21 @@ if is_tf_available():
             TFDPRQuestionEncoder,
             TFDPRReader,
         )
+
+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)

src/transformers/models/electra/__init__.py

@@ -16,15 +16,55 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_tf_available, is_tokenizers_available, is_torch_available
-from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig
-from .tokenization_electra import ElectraTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig"],
+    "tokenization_electra": ["ElectraTokenizer"],
+}
 
 if is_tokenizers_available():
-    from .tokenization_electra_fast import ElectraTokenizerFast
+    _import_structure["tokenization_electra_fast"] = ["ElectraTokenizerFast"]
 
 if is_torch_available():
+    _import_structure["modeling_electra"] = [
+        "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "ElectraForMaskedLM",
+        "ElectraForMultipleChoice",
+        "ElectraForPreTraining",
+        "ElectraForQuestionAnswering",
+        "ElectraForSequenceClassification",
+        "ElectraForTokenClassification",
+        "ElectraModel",
+        "ElectraPreTrainedModel",
+        "load_tf_weights_in_electra",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_electra"] = [
+        "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFElectraForMaskedLM",
+        "TFElectraForMultipleChoice",
+        "TFElectraForPreTraining",
+        "TFElectraForQuestionAnswering",
+        "TFElectraForSequenceClassification",
+        "TFElectraForTokenClassification",
+        "TFElectraModel",
+        "TFElectraPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig
+    from .tokenization_electra import ElectraTokenizer
+
+    if is_tokenizers_available():
+        from .tokenization_electra_fast import ElectraTokenizerFast
+
+    if is_torch_available():
         from .modeling_electra import (
             ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
             ElectraForMaskedLM,
@@ -38,7 +78,7 @@ if is_torch_available():
             load_tf_weights_in_electra,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_electra import (
             TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFElectraForMaskedLM,
@@ -50,3 +90,21 @@ if is_tf_available():
             TFElectraModel,
             TFElectraPreTrainedModel,
         )
+
+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)

src/transformers/models/encoder_decoder/__init__.py

@@ -16,9 +16,39 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_torch_available
-from .configuration_encoder_decoder import EncoderDecoderConfig
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_encoder_decoder": ["EncoderDecoderConfig"],
+}
 
 if is_torch_available():
+    _import_structure["modeling_encoder_decoder"] = ["EncoderDecoderModel"]
+
+
+if TYPE_CHECKING:
+    from .configuration_encoder_decoder import EncoderDecoderConfig
+
+    if is_torch_available():
         from .modeling_encoder_decoder import EncoderDecoderModel
+
+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)

src/transformers/models/flaubert/__init__.py

@@ -16,12 +16,45 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_tf_available, is_torch_available
-from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
-from .tokenization_flaubert import FlaubertTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_tf_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig"],
+    "tokenization_flaubert": ["FlaubertTokenizer"],
+}
 
 if is_torch_available():
+    _import_structure["modeling_flaubert"] = [
+        "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "FlaubertForMultipleChoice",
+        "FlaubertForQuestionAnswering",
+        "FlaubertForQuestionAnsweringSimple",
+        "FlaubertForSequenceClassification",
+        "FlaubertForTokenClassification",
+        "FlaubertModel",
+        "FlaubertWithLMHeadModel",
+    ]
+
+if is_tf_available():
+    _import_structure["modeling_tf_flaubert"] = [
+        "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFFlaubertForMultipleChoice",
+        "TFFlaubertForQuestionAnsweringSimple",
+        "TFFlaubertForSequenceClassification",
+        "TFFlaubertForTokenClassification",
+        "TFFlaubertModel",
+        "TFFlaubertWithLMHeadModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
+    from .tokenization_flaubert import FlaubertTokenizer
+
+    if is_torch_available():
         from .modeling_flaubert import (
             FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             FlaubertForMultipleChoice,
@@ -33,7 +66,7 @@ if is_torch_available():
             FlaubertWithLMHeadModel,
         )
 
-if is_tf_available():
+    if is_tf_available():
         from .modeling_tf_flaubert import (
             TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFFlaubertForMultipleChoice,
@@ -43,3 +76,21 @@ if is_tf_available():
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
         )
+
+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)

src/transformers/models/fsmt/__init__.py

@@ -16,10 +16,41 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...file_utils import is_torch_available
-from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
-from .tokenization_fsmt import FSMTTokenizer
+from typing import TYPE_CHECKING
 
+from ...file_utils import _BaseLazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
+    "tokenization_fsmt": ["FSMTTokenizer"],
+}
 
 if is_torch_available():
+    _import_structure["modeling_fsmt"] = ["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]
+
+
+if TYPE_CHECKING:
+    from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
+    from .tokenization_fsmt import FSMTTokenizer
+
+    if is_torch_available():
         from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel
+
+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)