[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/mpnet/__init__.py

@@ -18,7 +18,14 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -26,10 +33,20 @@ _import_structure = {
     "tokenization_mpnet": ["MPNetTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_mpnet_fast"] = ["MPNetTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_mpnet"] = [
         "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MPNetForMaskedLM",
@@ -42,7 +59,12 @@ if is_torch_available():
         "MPNetPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_mpnet"] = [
         "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFMPNetEmbeddings",
@@ -61,10 +83,20 @@ if TYPE_CHECKING:
     from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
     from .tokenization_mpnet import MPNetTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_mpnet_fast import MPNetTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_mpnet import (
             MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
             MPNetForMaskedLM,
@@ -77,7 +109,12 @@ if TYPE_CHECKING:
             MPNetPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_mpnet import (
             TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFMPNetEmbeddings,

src/transformers/models/mt5/__init__.py

@@ -19,6 +19,7 @@
 from typing import TYPE_CHECKING
 
 from ...utils import (
+    OptionalDependencyNotAvailable,
     _LazyModule,
     is_flax_available,
     is_sentencepiece_available,
@@ -46,26 +47,56 @@ _import_structure = {
     "configuration_mt5": ["MT5Config"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_mt5"] = ["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5Model"]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_mt5"] = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_mt5"] = ["FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
 
 
 if TYPE_CHECKING:
     from .configuration_mt5 import MT5Config
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_mt5 import TFMT5EncoderModel, TFMT5ForConditionalGeneration, TFMT5Model
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_mt5 import FlaxMT5ForConditionalGeneration, FlaxMT5Model
 
 else:

src/transformers/models/nystromformer/__init__.py

@@ -18,14 +18,19 @@
 from typing import TYPE_CHECKING
 
 # rely on isort to merge the imports
-from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
 
 
 _import_structure = {
     "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_nystromformer"] = [
         "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NystromformerForMaskedLM",
@@ -42,7 +47,12 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_nystromformer import (
             NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             NystromformerForMaskedLM,

src/transformers/models/openai/__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_openai": ["OpenAIGPTTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_openai_fast"] = ["OpenAIGPTTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_openai"] = [
         "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "OpenAIGPTDoubleHeadsModel",
@@ -40,7 +56,12 @@ if is_torch_available():
         "load_tf_weights_in_openai_gpt",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_openai"] = [
         "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFOpenAIGPTDoubleHeadsModel",
@@ -56,10 +77,20 @@ if TYPE_CHECKING:
     from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
     from .tokenization_openai import OpenAIGPTTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_openai_fast import OpenAIGPTTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_openai import (
             OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
             OpenAIGPTDoubleHeadsModel,
@@ -70,7 +101,12 @@ if TYPE_CHECKING:
             load_tf_weights_in_openai_gpt,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_openai import (
             TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFOpenAIGPTDoubleHeadsModel,

src/transformers/models/pegasus/__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_pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig"],
 }
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_pegasus"] = ["PegasusTokenizer"]
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_pegasus_fast"] = ["PegasusTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_pegasus"] = [
         "PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
         "PegasusForCausalLM",
@@ -46,14 +62,24 @@ if is_torch_available():
         "PegasusPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_pegasus"] = [
         "TFPegasusForConditionalGeneration",
         "TFPegasusModel",
         "TFPegasusPreTrainedModel",
     ]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_pegasus"] = [
         "FlaxPegasusForConditionalGeneration",
         "FlaxPegasusModel",
@@ -64,13 +90,28 @@ if is_flax_available():
 if TYPE_CHECKING:
     from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig
 
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_pegasus import PegasusTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_pegasus_fast import PegasusTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_pegasus import (
             PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
             PegasusForCausalLM,
@@ -79,10 +120,20 @@ if TYPE_CHECKING:
             PegasusPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_pegasus import (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,

src/transformers/models/perceiver/__init__.py

@@ -17,7 +17,13 @@
 # limitations under the License.
 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 = {
@@ -25,10 +31,20 @@ _import_structure = {
     "tokenization_perceiver": ["PerceiverTokenizer"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_perceiver"] = ["PerceiverFeatureExtractor"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_perceiver"] = [
         "PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "PerceiverForImageClassificationConvProcessing",
@@ -48,10 +64,20 @@ if TYPE_CHECKING:
     from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig
     from .tokenization_perceiver import PerceiverTokenizer
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_perceiver import PerceiverFeatureExtractor
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_perceiver import (
             PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
             PerceiverForImageClassificationConvProcessing,

src/transformers/models/plbart/__init__.py

@@ -17,17 +17,33 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_sentencepiece_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
     "configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"],
 }
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_plbart"] = ["PLBartTokenizer"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_plbart"] = [
         "PLBART_PRETRAINED_MODEL_ARCHIVE_LIST",
         "PLBartForCausalLM",
@@ -41,10 +57,20 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
 
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_plbart import PLBartTokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_plbart import (
             PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
             PLBartForCausalLM,

src/transformers/models/poolformer/__init__.py

@@ -18,17 +18,27 @@
 from typing import TYPE_CHECKING
 
 # rely on isort to merge the imports
-from ...utils import _LazyModule, is_torch_available, is_vision_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
 
 
 _import_structure = {
     "configuration_poolformer": ["POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PoolFormerConfig"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_poolformer"] = ["PoolFormerFeatureExtractor"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_poolformer"] = [
         "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "PoolFormerForImageClassification",
@@ -40,10 +50,20 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_poolformer import POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_poolformer import PoolFormerFeatureExtractor
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_poolformer import (
             POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             PoolFormerForImageClassification,

src/transformers/models/prophetnet/__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_prophetnet": ["ProphetNetTokenizer"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_prophetnet"] = [
         "PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "ProphetNetDecoder",
@@ -42,7 +47,12 @@ if TYPE_CHECKING:
     from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
     from .tokenization_prophetnet import ProphetNetTokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_prophetnet import (
             PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
             ProphetNetDecoder,

src/transformers/models/qdqbert/__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_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_qdqbert"] = [
         "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "QDQBertForMaskedLM",
@@ -44,7 +49,12 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_qdqbert import (
             QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             QDQBertForMaskedLM,

src/transformers/models/rag/__init__.py

@@ -18,7 +18,7 @@
 
 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 = {
@@ -27,7 +27,12 @@ _import_structure = {
     "tokenization_rag": ["RagTokenizer"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_rag"] = [
         "RagModel",
         "RagPreTrainedModel",
@@ -35,7 +40,12 @@ if is_torch_available():
         "RagTokenForGeneration",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_rag"] = [
         "TFRagModel",
         "TFRagPreTrainedModel",
@@ -49,10 +59,20 @@ if TYPE_CHECKING:
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_rag import (
             TFRagModel,
             TFRagPreTrainedModel,

src/transformers/models/realm/__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 = {
@@ -25,10 +25,20 @@ _import_structure = {
     "tokenization_realm": ["RealmTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_realm_fast"] = ["RealmTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_realm"] = [
         "REALM_PRETRAINED_MODEL_ARCHIVE_LIST",
         "RealmEmbedder",
@@ -46,10 +56,20 @@ if TYPE_CHECKING:
     from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
     from .tokenization_realm import RealmTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_realm import RealmTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_realm import (
             REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
             RealmEmbedder,

src/transformers/models/reformer/__init__.py

@@ -18,20 +18,41 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_sentencepiece_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
     "configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"],
 }
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_reformer"] = ["ReformerTokenizer"]
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_reformer_fast"] = ["ReformerTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_reformer"] = [
         "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "ReformerAttention",
@@ -48,13 +69,28 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
 
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_reformer import ReformerTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_reformer_fast import ReformerTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_reformer import (
             REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             ReformerAttention,

src/transformers/models/regnet/__init__.py

@@ -19,13 +19,19 @@ from typing import TYPE_CHECKING
 
 # rely on isort to merge the imports
 from ...file_utils import _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable
 
 
 _import_structure = {
     "configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_regnet"] = [
         "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "RegNetForImageClassification",
@@ -37,7 +43,12 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_regnet import (
             REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
             RegNetForImageClassification,

src/transformers/models/rembert/__init__.py

@@ -19,6 +19,7 @@
 from typing import TYPE_CHECKING
 
 from ...utils import (
+    OptionalDependencyNotAvailable,
     _LazyModule,
     is_sentencepiece_available,
     is_tf_available,
@@ -31,13 +32,28 @@ _import_structure = {
     "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig"],
 }
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_rembert"] = ["RemBertTokenizer"]
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_rembert_fast"] = ["RemBertTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_rembert"] = [
         "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "RemBertForCausalLM",
@@ -53,7 +69,12 @@ if is_torch_available():
     ]
 
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_rembert"] = [
         "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFRemBertForCausalLM",
@@ -71,13 +92,28 @@ if is_tf_available():
 if TYPE_CHECKING:
     from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig
 
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_rembert import RemBertTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_rembert_fast import RemBertTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_rembert import (
             REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             RemBertForCausalLM,
@@ -92,7 +128,12 @@ if TYPE_CHECKING:
             load_tf_weights_in_rembert,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_rembert import (
             TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFRemBertForCausalLM,

src/transformers/models/resnet/__init__.py

@@ -18,14 +18,19 @@
 from typing import TYPE_CHECKING
 
 # rely on isort to merge the imports
-from ...utils import _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
 
 
 _import_structure = {
     "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_resnet"] = [
         "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "ResNetForImageClassification",
@@ -37,7 +42,12 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_resnet import (
             RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
             ResNetForImageClassification,

src/transformers/models/retribert/__init__.py

@@ -18,7 +18,7 @@
 
 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 = {
@@ -26,10 +26,20 @@ _import_structure = {
     "tokenization_retribert": ["RetriBertTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_retribert_fast"] = ["RetriBertTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_retribert"] = [
         "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "RetriBertModel",
@@ -41,10 +51,20 @@ if TYPE_CHECKING:
     from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
     from .tokenization_retribert import RetriBertTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_retribert_fast import RetriBertTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_retribert import (
             RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             RetriBertModel,

src/transformers/models/roberta/__init__.py

@@ -18,7 +18,14 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -26,10 +33,20 @@ _import_structure = {
     "tokenization_roberta": ["RobertaTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_roberta_fast"] = ["RobertaTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_roberta"] = [
         "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "RobertaForCausalLM",
@@ -42,7 +59,12 @@ if is_torch_available():
         "RobertaPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_roberta"] = [
         "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFRobertaForCausalLM",
@@ -56,7 +78,12 @@ if is_tf_available():
         "TFRobertaPreTrainedModel",
     ]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_roberta"] = [
         "FlaxRobertaForCausalLM",
         "FlaxRobertaForMaskedLM",
@@ -73,10 +100,20 @@ if TYPE_CHECKING:
     from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
     from .tokenization_roberta import RobertaTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_roberta_fast import RobertaTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_roberta import (
             ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
             RobertaForCausalLM,
@@ -89,7 +126,12 @@ if TYPE_CHECKING:
             RobertaPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_roberta import (
             TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFRobertaForCausalLM,
@@ -103,7 +145,12 @@ if TYPE_CHECKING:
             TFRobertaPreTrainedModel,
         )
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_roberta import (
             FlaxRobertaForCausalLM,
             FlaxRobertaForMaskedLM,

src/transformers/models/roformer/__init__.py

@@ -17,7 +17,14 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
@@ -25,10 +32,20 @@ _import_structure = {
     "tokenization_roformer": ["RoFormerTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_roformer_fast"] = ["RoFormerTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_roformer"] = [
         "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "RoFormerForCausalLM",
@@ -44,7 +61,12 @@ if is_torch_available():
     ]
 
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_roformer"] = [
         "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFRoFormerForCausalLM",
@@ -59,7 +81,12 @@ if is_tf_available():
     ]
 
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_roformer"] = [
         "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FlaxRoFormerForMaskedLM",
@@ -76,10 +103,20 @@ if TYPE_CHECKING:
     from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
     from .tokenization_roformer import RoFormerTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_roformer_fast import RoFormerTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_roformer import (
             ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             RoFormerForCausalLM,
@@ -94,7 +131,12 @@ if TYPE_CHECKING:
             load_tf_weights_in_roformer,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_roformer import (
             TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFRoFormerForCausalLM,
@@ -108,7 +150,12 @@ if TYPE_CHECKING:
             TFRoFormerPreTrainedModel,
         )
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_roformer import (
             FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             FlaxRoFormerForMaskedLM,

src/transformers/models/segformer/__init__.py

@@ -17,17 +17,27 @@
 # 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_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_segformer"] = ["SegformerFeatureExtractor"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_segformer"] = [
         "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "SegformerDecodeHead",
@@ -42,10 +52,20 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_segformer import SegformerFeatureExtractor
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_segformer import (
             SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             SegformerDecodeHead,