[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/ctrl/__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 = {
@@ -26,7 +26,12 @@ _import_structure = {
     "tokenization_ctrl": ["CTRLTokenizer"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_ctrl"] = [
         "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CTRLForSequenceClassification",
@@ -35,7 +40,12 @@ if is_torch_available():
         "CTRLPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_ctrl"] = [
         "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFCTRLForSequenceClassification",
@@ -49,7 +59,12 @@ if TYPE_CHECKING:
     from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
     from .tokenization_ctrl import CTRLTokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_ctrl import (
             CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
             CTRLForSequenceClassification,
@@ -58,7 +73,12 @@ if TYPE_CHECKING:
             CTRLPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_ctrl import (
             TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFCTRLForSequenceClassification,

src/transformers/models/data2vec/__init__.py

@@ -18,9 +18,7 @@
 
 from typing import TYPE_CHECKING
 
-from transformers.utils.import_utils import is_tf_available
-
-from ...utils import _LazyModule, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {
@@ -40,7 +38,12 @@ _import_structure = {
     ],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_data2vec_audio"] = [
         "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Data2VecAudioForAudioFrameClassification",
@@ -90,7 +93,12 @@ if TYPE_CHECKING:
         Data2VecVisionOnnxConfig,
     )
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_data2vec_audio import (
             DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
             Data2VecAudioForAudioFrameClassification,

src/transformers/models/deberta/__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_deberta": ["DebertaTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_deberta_fast"] = ["DebertaTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_deberta"] = [
         "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DebertaForMaskedLM",
@@ -40,7 +56,12 @@ if is_torch_available():
         "DebertaPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_deberta"] = [
         "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDebertaForMaskedLM",
@@ -56,10 +77,20 @@ if TYPE_CHECKING:
     from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig
     from .tokenization_deberta import DebertaTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_deberta_fast import DebertaTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_deberta import (
             DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
             DebertaForMaskedLM,
@@ -70,7 +101,12 @@ if TYPE_CHECKING:
             DebertaPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_deberta import (
             TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFDebertaForMaskedLM,

src/transformers/models/deberta_v2/__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_deberta_v2": ["DebertaV2Tokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_deberta_v2_fast"] = ["DebertaV2TokenizerFast"]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_deberta_v2"] = [
         "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDebertaV2ForMaskedLM",
@@ -40,7 +56,12 @@ if is_tf_available():
         "TFDebertaV2PreTrainedModel",
     ]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_deberta_v2"] = [
         "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DebertaV2ForMaskedLM",
@@ -56,10 +77,20 @@ if TYPE_CHECKING:
     from .configuration_deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config
     from .tokenization_deberta_v2 import DebertaV2Tokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_deberta_v2_fast import DebertaV2TokenizerFast
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_deberta_v2 import (
             TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFDebertaV2ForMaskedLM,
@@ -70,7 +101,12 @@ if TYPE_CHECKING:
             TFDebertaV2PreTrainedModel,
         )
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_deberta_v2 import (
             DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
             DebertaV2ForMaskedLM,

src/transformers/models/decision_transformer/__init__.py

@@ -18,7 +18,7 @@
 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 = {
@@ -28,7 +28,12 @@ _import_structure = {
     ],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_decision_transformer"] = [
         "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DecisionTransformerGPT2Model",
@@ -44,7 +49,12 @@ if TYPE_CHECKING:
         DecisionTransformerConfig,
     )
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_decision_transformer import (
             DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
             DecisionTransformerGPT2Model,

src/transformers/models/deit/__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_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_deit"] = ["DeiTFeatureExtractor"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_deit"] = [
         "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DeiTForImageClassification",
@@ -41,10 +51,20 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_deit import DeiTFeatureExtractor
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_deit import (
             DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
             DeiTForImageClassification,

src/transformers/models/detr/__init__.py

@@ -18,17 +18,27 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_timm_available, is_vision_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_timm_available, is_vision_available
 
 
 _import_structure = {
     "configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_detr"] = ["DetrFeatureExtractor"]
 
-if is_timm_available():
+try:
+    if not is_timm_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_detr"] = [
         "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DetrForObjectDetection",
@@ -41,10 +51,20 @@ if is_timm_available():
 if TYPE_CHECKING:
     from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_detr import DetrFeatureExtractor
 
-    if is_timm_available():
+    try:
+        if not is_timm_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_detr import (
             DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
             DetrForObjectDetection,

src/transformers/models/distilbert/__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 = {
@@ -30,10 +37,20 @@ _import_structure = {
     "tokenization_distilbert": ["DistilBertTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_distilbert_fast"] = ["DistilBertTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_distilbert"] = [
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
@@ -45,7 +62,12 @@ if is_torch_available():
         "DistilBertPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_distilbert"] = [
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
@@ -58,7 +80,12 @@ if is_tf_available():
         "TFDistilBertPreTrainedModel",
     ]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_distilbert"] = [
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
@@ -78,10 +105,20 @@ if TYPE_CHECKING:
     )
     from .tokenization_distilbert import DistilBertTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_distilbert_fast import DistilBertTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_distilbert import (
             DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             DistilBertForMaskedLM,
@@ -93,7 +130,12 @@ if TYPE_CHECKING:
             DistilBertPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_distilbert import (
             TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFDistilBertForMaskedLM,
@@ -106,7 +148,12 @@ if TYPE_CHECKING:
             TFDistilBertPreTrainedModel,
         )
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_distilbert import (
             FlaxDistilBertForMaskedLM,
             FlaxDistilBertForMultipleChoice,

src/transformers/models/dpr/__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 = {
@@ -32,14 +38,24 @@ _import_structure = {
 }
 
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_dpr_fast"] = [
         "DPRContextEncoderTokenizerFast",
         "DPRQuestionEncoderTokenizerFast",
         "DPRReaderTokenizerFast",
     ]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_dpr"] = [
         "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -53,7 +69,12 @@ if is_torch_available():
         "DPRReader",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_dpr"] = [
         "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -76,14 +97,24 @@ if TYPE_CHECKING:
         DPRReaderTokenizer,
     )
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_dpr_fast import (
             DPRContextEncoderTokenizerFast,
             DPRQuestionEncoderTokenizerFast,
             DPRReaderTokenizerFast,
         )
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_dpr import (
             DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
             DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
@@ -97,7 +128,12 @@ if TYPE_CHECKING:
             DPRReader,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_dpr import (
             TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
             TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,

src/transformers/models/dpt/__init__.py

@@ -18,16 +18,27 @@
 from typing import TYPE_CHECKING
 
 from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
+from ...utils import OptionalDependencyNotAvailable
 
 
 _import_structure = {
     "configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_dpt"] = ["DPTFeatureExtractor"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_dpt"] = [
         "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DPTForDepthEstimation",
@@ -40,10 +51,20 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_dpt import DPTFeatureExtractor
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_dpt import (
             DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
             DPTForDepthEstimation,

src/transformers/models/electra/__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_electra": ["ElectraTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_electra_fast"] = ["ElectraTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_electra"] = [
         "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "ElectraForCausalLM",
@@ -44,7 +61,12 @@ if is_torch_available():
         "load_tf_weights_in_electra",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_electra"] = [
         "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFElectraForMaskedLM",
@@ -57,7 +79,12 @@ if is_tf_available():
         "TFElectraPreTrainedModel",
     ]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_electra"] = [
         "FlaxElectraForCausalLM",
         "FlaxElectraForMaskedLM",
@@ -75,10 +102,20 @@ if TYPE_CHECKING:
     from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
     from .tokenization_electra import ElectraTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_electra_fast import ElectraTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_electra import (
             ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
             ElectraForCausalLM,
@@ -93,7 +130,12 @@ if TYPE_CHECKING:
             load_tf_weights_in_electra,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_electra import (
             TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFElectraForMaskedLM,
@@ -106,7 +148,12 @@ if TYPE_CHECKING:
             TFElectraPreTrainedModel,
         )
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_electra import (
             FlaxElectraForCausalLM,
             FlaxElectraForMaskedLM,

src/transformers/models/encoder_decoder/__init__.py

@@ -18,32 +18,68 @@
 
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
     "configuration_encoder_decoder": ["EncoderDecoderConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_encoder_decoder"] = ["EncoderDecoderModel"]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_encoder_decoder"] = ["TFEncoderDecoderModel"]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_encoder_decoder"] = ["FlaxEncoderDecoderModel"]
 
 if TYPE_CHECKING:
     from .configuration_encoder_decoder import EncoderDecoderConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_encoder_decoder import EncoderDecoderModel
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
 
 else:

src/transformers/models/flaubert/__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 = {
@@ -26,7 +26,12 @@ _import_structure = {
     "tokenization_flaubert": ["FlaubertTokenizer"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flaubert"] = [
         "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FlaubertForMultipleChoice",
@@ -38,7 +43,12 @@ if is_torch_available():
         "FlaubertWithLMHeadModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_flaubert"] = [
         "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFlaubertForMultipleChoice",
@@ -55,7 +65,12 @@ if TYPE_CHECKING:
     from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
     from .tokenization_flaubert import FlaubertTokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flaubert import (
             FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             FlaubertForMultipleChoice,
@@ -67,7 +82,12 @@ if TYPE_CHECKING:
             FlaubertWithLMHeadModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_flaubert import (
             TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFFlaubertForMultipleChoice,

src/transformers/models/fnet/__init__.py

@@ -17,22 +17,41 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from transformers import is_sentencepiece_available
-
-from ...utils import _LazyModule, is_tokenizers_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_sentencepiece_available,
+    is_tokenizers_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
     "configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"],
 }
 
-if is_sentencepiece_available():
+try:
+    if not is_sentencepiece_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_fnet"] = ["FNetTokenizer"]
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_fnet_fast"] = ["FNetTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_fnet"] = [
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
@@ -51,13 +70,28 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
 
-    if is_sentencepiece_available():
+    try:
+        if not is_sentencepiece_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_fnet import FNetTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_fnet_fast import FNetTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_fnet import (
             FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
             FNetForMaskedLM,

src/transformers/models/fsmt/__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_fsmt": ["FSMTTokenizer"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_fsmt"] = ["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]
 
 
@@ -34,7 +39,12 @@ if TYPE_CHECKING:
     from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
     from .tokenization_fsmt import FSMTTokenizer
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel
 
 else:

src/transformers/models/funnel/__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 = {
@@ -27,10 +33,20 @@ _import_structure = {
     "tokenization_funnel": ["FunnelTokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_funnel_fast"] = ["FunnelTokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_funnel"] = [
         "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FunnelBaseModel",
@@ -45,7 +61,12 @@ if is_torch_available():
         "load_tf_weights_in_funnel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_funnel"] = [
         "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFunnelBaseModel",
@@ -64,10 +85,20 @@ if TYPE_CHECKING:
     from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
     from .tokenization_funnel import FunnelTokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_funnel_fast import FunnelTokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_funnel import (
             FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
             FunnelBaseModel,
@@ -82,7 +113,12 @@ if TYPE_CHECKING:
             load_tf_weights_in_funnel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_funnel import (
             TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFFunnelBaseModel,

src/transformers/models/glpn/__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_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"],
 }
 
-if is_vision_available():
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["feature_extraction_glpn"] = ["GLPNFeatureExtractor"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_glpn"] = [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
@@ -41,10 +51,20 @@ if is_torch_available():
 if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig
 
-    if is_vision_available():
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .feature_extraction_glpn import GLPNFeatureExtractor
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_glpn import (
             GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
             GLPNForDepthEstimation,

src/transformers/models/gpt2/__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_gpt2": ["GPT2Tokenizer"],
 }
 
-if is_tokenizers_available():
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"]
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_gpt2"] = [
         "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPT2DoubleHeadsModel",
@@ -41,7 +58,12 @@ if is_torch_available():
         "load_tf_weights_in_gpt2",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_gpt2"] = [
         "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFGPT2DoubleHeadsModel",
@@ -52,17 +74,32 @@ if is_tf_available():
         "TFGPT2PreTrainedModel",
     ]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_gpt2"] = ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]
 
 if TYPE_CHECKING:
     from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig
     from .tokenization_gpt2 import GPT2Tokenizer
 
-    if is_tokenizers_available():
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .tokenization_gpt2_fast import GPT2TokenizerFast
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_gpt2 import (
             GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPT2DoubleHeadsModel,
@@ -74,7 +111,12 @@ if TYPE_CHECKING:
             load_tf_weights_in_gpt2,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_gpt2 import (
             TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFGPT2DoubleHeadsModel,
@@ -85,7 +127,12 @@ if TYPE_CHECKING:
             TFGPT2PreTrainedModel,
         )
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel
 
 else:

src/transformers/models/gpt_neo/__init__.py

@@ -17,14 +17,19 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_flax_available, is_torch_available
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
 
 
 _import_structure = {
     "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_gpt_neo"] = [
         "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTNeoForCausalLM",
@@ -34,7 +39,12 @@ if is_torch_available():
         "load_tf_weights_in_gpt_neo",
     ]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_gpt_neo"] = [
         "FlaxGPTNeoForCausalLM",
         "FlaxGPTNeoModel",
@@ -45,7 +55,12 @@ if is_flax_available():
 if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_gpt_neo import (
             GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTNeoForCausalLM,
@@ -55,7 +70,12 @@ if TYPE_CHECKING:
             load_tf_weights_in_gpt_neo,
         )
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
 
 

src/transformers/models/gptj/__init__.py

@@ -17,14 +17,25 @@
 # limitations under the License.
 from typing import TYPE_CHECKING
 
-from ...utils import _LazyModule, is_flax_available, is_tf_available, is_torch_available
+from ...utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_flax_available,
+    is_tf_available,
+    is_torch_available,
+)
 
 
 _import_structure = {
     "configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"],
 }
 
-if is_torch_available():
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_gptj"] = [
         "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GPTJForCausalLM",
@@ -34,7 +45,12 @@ if is_torch_available():
         "GPTJPreTrainedModel",
     ]
 
-if is_tf_available():
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_tf_gptj"] = [
         "TFGPTJForCausalLM",
         "TFGPTJForQuestionAnswering",
@@ -43,7 +59,12 @@ if is_tf_available():
         "TFGPTJPreTrainedModel",
     ]
 
-if is_flax_available():
+try:
+    if not is_flax_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
     _import_structure["modeling_flax_gptj"] = [
         "FlaxGPTJForCausalLM",
         "FlaxGPTJModel",
@@ -54,7 +75,12 @@ if is_flax_available():
 if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
 
-    if is_torch_available():
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_gptj import (
             GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
             GPTJForCausalLM,
@@ -64,7 +90,12 @@ if TYPE_CHECKING:
             GPTJPreTrainedModel,
         )
 
-    if is_tf_available():
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_tf_gptj import (
             TFGPTJForCausalLM,
             TFGPTJForQuestionAnswering,
@@ -73,7 +104,12 @@ if TYPE_CHECKING:
             TFGPTJPreTrainedModel,
         )
 
-    if is_flax_available():
+    try:
+        if not is_flax_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
         from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
 
 else: