[WIP] Fix Pyright static type checking by replacing if-else imports with try-except (#16578)

* rebase and isort

* modify cookiecutter init

* fix cookiecutter auto imports

* fix clean_frameworks_in_init

* fix add_model_to_main_init

* blackify

* replace unnecessary f-strings

* update yolos imports

* fix roberta import bug

* fix yolos missing dependency

* fix add_model_like and cookiecutter bug

* fix repository consistency error

* modify cookiecutter, fix add_new_model_like

* remove stale line
Co-authored-by: Dom Miketa <dmiketa@exscientia.co.uk>

src/transformers/models/herbert/__init__.py

@@ -18,21 +18,31 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tokenizers_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
 
 
 _import_structure = {
     "tokenization_herbert": ["HerbertTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_herbert_fast"] = ["HerbertTokenizerFast"]
 
 
 if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_herbert_fast import HerbertTokenizerFast
 
 else:

src/transformers/models/hubert/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tf_available, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {
@@ -25,7 +25,12 @@ _import_structure = {
     "configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_hubert"] = [
         "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "HubertForCTC",
@@ -35,7 +40,12 @@ if is_torch_available():
     ]
 
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_hubert"] = [
         "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFHubertForCTC",
@@ -47,7 +57,12 @@ if TYPE_CHECKING:
     from ..wav2vec2.feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
     from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_hubert import (
             HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             HubertForCTC,
@@ -56,7 +71,12 @@ if TYPE_CHECKING:
             HubertPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_hubert import (
             TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFHubertForCTC,

src/transformers/models/ibert/__init__.py

@@ -18,14 +18,19 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
 
 
 _import_structure = {
     "configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_ibert"] = [
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
@@ -40,7 +45,12 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_ibert import (
             IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             IBertForMaskedLM,

src/transformers/models/imagegpt/__init__.py

@@ -18,17 +18,27 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_torch_available, is_vision_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
 
 
 _import_structure = {
     "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_imagegpt"] = ["ImageGPTFeatureExtractor"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_imagegpt"] = [
         "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "ImageGPTForCausalImageModeling",
@@ -42,10 +52,20 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_imagegpt import (
             IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
             ImageGPTForCausalImageModeling,

src/transformers/models/layoutlm/__init__.py

@@ -18,7 +18,13 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
 from .tokenization_layoutlm import LayoutLMTokenizer
 
@@ -28,10 +34,20 @@ _import_structure = {
     "tokenization_layoutlm": ["LayoutLMTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_layoutlm_fast"] = ["LayoutLMTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_layoutlm"] = [
         "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LayoutLMForMaskedLM",
@@ -41,7 +57,12 @@ if is_torch_available():
         "LayoutLMPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_layoutlm"] = [
         "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFLayoutLMForMaskedLM",
@@ -57,10 +78,20 @@ if TYPE_CHECKING:
     from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
     from .tokenization_layoutlm import LayoutLMTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_layoutlm import (
             LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             LayoutLMForMaskedLM,
@@ -69,7 +100,12 @@ if TYPE_CHECKING:
             LayoutLMModel,
             LayoutLMPreTrainedModel,
         )
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_layoutlm import (
             TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFLayoutLMForMaskedLM,

src/transformers/models/layoutlmv2/__init__.py

@@ -18,7 +18,13 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tokenizers_available,
+    is_torch_available,
+    is_vision_available,
+)
 
 
 _import_structure = {
@@ -26,14 +32,29 @@ _import_structure = {
     "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_layoutlmv2_fast"] = ["LayoutLMv2TokenizerFast"]
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_layoutlmv2"] = ["LayoutLMv2FeatureExtractor"]
     _import_structure["processing_layoutlmv2"] = ["LayoutLMv2Processor"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_layoutlmv2"] = [
         "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LayoutLMv2ForQuestionAnswering",
@@ -48,14 +69,29 @@ if TYPE_CHECKING:
     from .configuration_layoutlmv2 import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMv2Config
     from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_layoutlmv2_fast import LayoutLMv2TokenizerFast
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_layoutlmv2 import LayoutLMv2FeatureExtractor
         from .processing_layoutlmv2 import LayoutLMv2Processor
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_layoutlmv2 import (
             LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
             LayoutLMv2ForQuestionAnswering,

src/transformers/models/layoutxlm/__init__.py

@@ -19,6 +19,7 @@
 from typing import TYPE_CHECKING
 
 from ...utils import (
+    OptionalDependencyNotAvailable,
     _LazyModule,
     is_sentencepiece_available,
     is_tokenizers_available,
@@ -29,23 +30,53 @@ from ...utils import (
 
 _import_structure = {}
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_layoutxlm"] = ["LayoutXLMTokenizer"]
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_layoutxlm_fast"] = ["LayoutXLMTokenizerFast"]
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["processing_layoutxlm"] = ["LayoutXLMProcessor"]
 
 if TYPE_CHECKING:
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_layoutxlm import LayoutXLMTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .processing_layoutlmv2 import LayoutXLMProcessor
 
 else:

src/transformers/models/led/__init__.py

@@ -17,7 +17,13 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -25,10 +31,20 @@ _import_structure = {
     "tokenization_led": ["LEDTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_led_fast"] = ["LEDTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_led"] = [
         "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LEDForConditionalGeneration",
@@ -39,7 +55,12 @@ if is_torch_available():
     ]
 
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_led"] = ["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]
 
 
@@ -47,10 +68,20 @@ if TYPE_CHECKING:
     from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
     from .tokenization_led import LEDTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_led_fast import LEDTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_led import (
             LED_PRETRAINED_MODEL_ARCHIVE_LIST,
             LEDForConditionalGeneration,
@@ -60,7 +91,12 @@ if TYPE_CHECKING:
             LEDPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel
 
 else:

src/transformers/models/longformer/__init__.py

@@ -18,7 +18,13 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -30,10 +36,20 @@ _import_structure = {
     "tokenization_longformer": ["LongformerTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_longformer_fast"] = ["LongformerTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_longformer"] = [
         "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LongformerForMaskedLM",
@@ -46,7 +62,12 @@ if is_torch_available():
         "LongformerSelfAttention",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_longformer"] = [
         "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFLongformerForMaskedLM",
@@ -68,10 +89,20 @@ if TYPE_CHECKING:
     )
     from .tokenization_longformer import LongformerTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_longformer_fast import LongformerTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_longformer import (
             LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             LongformerForMaskedLM,
@@ -84,7 +115,12 @@ if TYPE_CHECKING:
             LongformerSelfAttention,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_longformer import (
             TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFLongformerForMaskedLM,

src/transformers/models/luke/__init__.py

@@ -18,7 +18,7 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
 
 
 _import_structure = {
@@ -26,7 +26,12 @@ _import_structure = {
     "tokenization_luke": ["LukeTokenizer"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_luke"] = [
         "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LukeForEntityClassification",
@@ -42,7 +47,12 @@ if TYPE_CHECKING:
     from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
     from .tokenization_luke import LukeTokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_luke import (
             LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
             LukeForEntityClassification,

src/transformers/models/lxmert/__init__.py

@@ -18,7 +18,13 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -26,10 +32,20 @@ _import_structure = {
     "tokenization_lxmert": ["LxmertTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_lxmert_fast"] = ["LxmertTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_lxmert"] = [
         "LxmertEncoder",
         "LxmertForPreTraining",
@@ -40,7 +56,12 @@ if is_torch_available():
         "LxmertXLayer",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_lxmert"] = [
         "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFLxmertForPreTraining",
@@ -55,10 +76,20 @@ if TYPE_CHECKING:
     from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
     from .tokenization_lxmert import LxmertTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_lxmert_fast import LxmertTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_lxmert import (
             LxmertEncoder,
             LxmertForPreTraining,
@@ -69,7 +100,12 @@ if TYPE_CHECKING:
             LxmertXLayer,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_lxmert import (
             TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFLxmertForPreTraining,

src/transformers/models/m2m_100/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
 
 
 _import_structure = {
@@ -26,7 +26,12 @@ _import_structure = {
 }
 
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_m2m_100"] = [
         "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
         "M2M100ForConditionalGeneration",
@@ -39,7 +44,12 @@ if TYPE_CHECKING:
     from .configuration_m2m_100 import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, M2M100Config, M2M100OnnxConfig
     from .tokenization_m2m_100 import M2M100Tokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_m2m_100 import (
             M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
             M2M100ForConditionalGeneration,

src/transformers/models/marian/__init__.py

@@ -18,6 +18,7 @@
 from typing import TYPE_CHECKING
 
 from ...utils import (
+    OptionalDependencyNotAvailable,
     _LazyModule,
     is_flax_available,
     is_sentencepiece_available,
@@ -31,10 +32,20 @@ _import_structure = {
     "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
 }
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_marian"] = ["MarianTokenizer"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_marian"] = [
         "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MarianForCausalLM",
@@ -43,19 +54,39 @@ if is_torch_available():
         "MarianPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_marian"] = ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_marian"] = ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]
 
 if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
 
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_marian import MarianTokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_marian import (
             MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
             MarianForCausalLM,
@@ -64,10 +95,20 @@ if TYPE_CHECKING:
             MarianPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
 
 else:

src/transformers/models/maskformer/__init__.py

@@ -17,18 +17,28 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_torch_available, is_vision_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
 
 
 _import_structure = {
     "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_maskformer"] = ["MaskFormerFeatureExtractor"]
 
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_maskformer"] = [
         "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MaskFormerForInstanceSegmentation",
@@ -39,9 +49,19 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_maskformer import MaskFormerFeatureExtractor
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_maskformer import (
             MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             MaskFormerForInstanceSegmentation,

src/transformers/models/mbart/__init__.py

@@ -18,6 +18,7 @@
 from typing import TYPE_CHECKING
 
 from ...utils import (
+    OptionalDependencyNotAvailable,
     _LazyModule,
     is_flax_available,
     is_sentencepiece_available,
@@ -31,13 +32,28 @@ _import_structure = {
     "configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"],
 }
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_mbart"] = ["MBartTokenizer"]
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_mbart_fast"] = ["MBartTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_mbart"] = [
         "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MBartForCausalLM",
@@ -48,14 +64,24 @@ if is_torch_available():
         "MBartPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_mbart"] = [
         "TFMBartForConditionalGeneration",
         "TFMBartModel",
         "TFMBartPreTrainedModel",
     ]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_mbart"] = [
         "FlaxMBartForConditionalGeneration",
         "FlaxMBartForQuestionAnswering",
@@ -68,13 +94,28 @@ if is_flax_available():
 if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
 
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_mbart import MBartTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_mbart_fast import MBartTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_mbart import (
             MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
             MBartForCausalLM,
@@ -85,10 +126,20 @@ if TYPE_CHECKING:
             MBartPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_mbart import (
             FlaxMBartForConditionalGeneration,
             FlaxMBartForQuestionAnswering,

src/transformers/models/mbart50/__init__.py

@@ -17,23 +17,43 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_sentencepiece_available, is_tokenizers_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
 
 
 _import_structure = {}
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_mbart50"] = ["MBart50Tokenizer"]
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_mbart50_fast"] = ["MBart50TokenizerFast"]
 
 
 if TYPE_CHECKING:
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_mbart50 import MBart50Tokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_mbart50_fast import MBart50TokenizerFast
 
 else:

src/transformers/models/megatron_bert/__init__.py

@@ -17,14 +17,19 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
 
 
 _import_structure = {
     "configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_megatron_bert"] = [
         "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MegatronBertForCausalLM",
@@ -42,7 +47,12 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_megatron_bert import (
             MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             MegatronBertForCausalLM,

src/transformers/models/mluke/__init__.py

@@ -18,17 +18,27 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_sentencepiece_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
 
 
 _import_structure = {}
 
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_mluke"] = ["MLukeTokenizer"]
 
 if TYPE_CHECKING:
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_mluke import MLukeTokenizer
 
 

src/transformers/models/mmbt/__init__.py

@@ -18,21 +18,31 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
 
 
 _import_structure = {
     "configuration_mmbt": ["MMBTConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_mmbt"] = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]
 
 
 if TYPE_CHECKING:
     from .configuration_mmbt import MMBTConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
 
 else:

src/transformers/models/mobilebert/__init__.py

@@ -18,7 +18,13 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -30,10 +36,20 @@ _import_structure = {
     "tokenization_mobilebert": ["MobileBertTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_mobilebert_fast"] = ["MobileBertTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_mobilebert"] = [
         "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MobileBertForMaskedLM",
@@ -49,7 +65,12 @@ if is_torch_available():
         "load_tf_weights_in_mobilebert",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_mobilebert"] = [
         "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFMobileBertForMaskedLM",
@@ -73,10 +94,20 @@ if TYPE_CHECKING:
     )
     from .tokenization_mobilebert import MobileBertTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_mobilebert_fast import MobileBertTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_mobilebert import (
             MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             MobileBertForMaskedLM,
@@ -92,7 +123,12 @@ if TYPE_CHECKING:
             load_tf_weights_in_mobilebert,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_mobilebert import (
             TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFMobileBertForMaskedLM,