Better dummies (#15148)

* Better dummies

* See if this fixes the issue

* Fix quality

* Style

* Add doc for DummyObject

src/transformers/file_utils.py

@@ -831,6 +831,18 @@ def requires_backends(obj, backends):
         raise ImportError("".join([BACKENDS_MAPPING[backend][1].format(name) for backend in backends]))
 
 
+class DummyObject(type):
+    """
+    Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by
+    `requires_backend` each time a user tries to access any method of that class.
+    """
+
+    def __getattr__(cls, key):
+        if key.startswith("_"):
+            return super().__getattr__(cls, key)
+        requires_backends(cls, cls._backends)
+
+
 def add_start_docstrings(*docstr):
     def docstring_decorator(fn):
         fn.__doc__ = "".join(docstr) + (fn.__doc__ if fn.__doc__ is not None else "")

src/transformers/utils/dummy_pytorch_quantization_and_torch_objects.py

@@ -1,120 +1,78 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
 QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
-class QDQBertForMaskedLM:
+class QDQBertForMaskedLM(metaclass=DummyObject):
-    def __init__(self, *args, **kwargs):
+    _backends = ["pytorch_quantization", "torch"]
-        requires_backends(self, ["pytorch_quantization", "torch"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
-
-    def forward(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-
-class QDQBertForMultipleChoice:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
-
-    def forward(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-
-class QDQBertForNextSentencePrediction:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
-
-    def forward(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-
-class QDQBertForQuestionAnswering:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
-
-    def forward(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-
-class QDQBertForSequenceClassification:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
-
-    def forward(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-
-class QDQBertForTokenClassification:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
-
-    def forward(self, *args, **kwargs):
-        requires_backends(self, ["pytorch_quantization", "torch"])
-
-
-class QDQBertLayer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["pytorch_quantization", "torch"])
 
 
-class QDQBertLMHeadModel:
+class QDQBertForMultipleChoice(metaclass=DummyObject):
+    _backends = ["pytorch_quantization", "torch"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["pytorch_quantization", "torch"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
 
-    def forward(self, *args, **kwargs):
+class QDQBertForNextSentencePrediction(metaclass=DummyObject):
-        requires_backends(self, ["pytorch_quantization", "torch"])
+    _backends = ["pytorch_quantization", "torch"]
 
-
-class QDQBertModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["pytorch_quantization", "torch"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
 
-    def forward(self, *args, **kwargs):
+class QDQBertForQuestionAnswering(metaclass=DummyObject):
-        requires_backends(self, ["pytorch_quantization", "torch"])
+    _backends = ["pytorch_quantization", "torch"]
 
-
-class QDQBertPreTrainedModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["pytorch_quantization", "torch"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["pytorch_quantization", "torch"])
 
-    def forward(self, *args, **kwargs):
+class QDQBertForSequenceClassification(metaclass=DummyObject):
+    _backends = ["pytorch_quantization", "torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["pytorch_quantization", "torch"])
+
+
+class QDQBertForTokenClassification(metaclass=DummyObject):
+    _backends = ["pytorch_quantization", "torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["pytorch_quantization", "torch"])
+
+
+class QDQBertLayer(metaclass=DummyObject):
+    _backends = ["pytorch_quantization", "torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["pytorch_quantization", "torch"])
+
+
+class QDQBertLMHeadModel(metaclass=DummyObject):
+    _backends = ["pytorch_quantization", "torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["pytorch_quantization", "torch"])
+
+
+class QDQBertModel(metaclass=DummyObject):
+    _backends = ["pytorch_quantization", "torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["pytorch_quantization", "torch"])
+
+
+class QDQBertPreTrainedModel(metaclass=DummyObject):
+    _backends = ["pytorch_quantization", "torch"]
+
+    def __init__(self, *args, **kwargs):
         requires_backends(self, ["pytorch_quantization", "torch"])
 
 

src/transformers/utils/dummy_scatter_objects.py

@@ -1,69 +1,45 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
 TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
-class TapasForMaskedLM:
+class TapasForMaskedLM(metaclass=DummyObject):
+    _backends = ["scatter"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["scatter"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["scatter"])
 
-    def forward(self, *args, **kwargs):
+class TapasForQuestionAnswering(metaclass=DummyObject):
-        requires_backends(self, ["scatter"])
+    _backends = ["scatter"]
 
-
-class TapasForQuestionAnswering:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["scatter"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["scatter"])
 
-    def forward(self, *args, **kwargs):
+class TapasForSequenceClassification(metaclass=DummyObject):
-        requires_backends(self, ["scatter"])
+    _backends = ["scatter"]
 
-
-class TapasForSequenceClassification:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["scatter"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["scatter"])
 
-    def forward(self, *args, **kwargs):
+class TapasModel(metaclass=DummyObject):
-        requires_backends(self, ["scatter"])
+    _backends = ["scatter"]
 
-
-class TapasModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["scatter"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["scatter"])
 
-    def forward(self, *args, **kwargs):
+class TapasPreTrainedModel(metaclass=DummyObject):
-        requires_backends(self, ["scatter"])
+    _backends = ["scatter"]
 
-
-class TapasPreTrainedModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["scatter"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["scatter"])
-
-    def forward(self, *args, **kwargs):
-        requires_backends(self, ["scatter"])
-
 
 def load_tf_weights_in_tapas(*args, **kwargs):
     requires_backends(load_tf_weights_in_tapas, ["scatter"])

src/transformers/utils/dummy_sentencepiece_and_speech_objects.py

@@ -1,11 +1,10 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
-class Speech2TextProcessor:
+class Speech2TextProcessor(metaclass=DummyObject):
+    _backends = ["sentencepiece", "speech"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece", "speech"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece", "speech"])

src/transformers/utils/dummy_sentencepiece_and_tokenizers_objects.py

@@ -1,5 +1,6 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
 SLOW_TO_FAST_CONVERTERS = None

src/transformers/utils/dummy_sentencepiece_objects.py

@@ -1,200 +1,157 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
-class AlbertTokenizer:
+class AlbertTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class BarthezTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class BarthezTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class BartphoTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class BartphoTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class BertGenerationTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class BertGenerationTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class BigBirdTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class BigBirdTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class CamembertTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class CamembertTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class DebertaV2Tokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class DebertaV2Tokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class LayoutXLMTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class LayoutXLMTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class M2M100Tokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class M2M100Tokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class MarianTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class MarianTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class MBart50Tokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class MBart50Tokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class MBartTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class MBartTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class MLukeTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class MLukeTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class MT5Tokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class MT5Tokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class PegasusTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class PegasusTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class ReformerTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class ReformerTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class RemBertTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class RemBertTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class Speech2TextTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class Speech2TextTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class T5Tokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class T5Tokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class XLMProphetNetTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class XLMProphetNetTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class XLMRobertaTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class XLMRobertaTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])
 
+class XLNetTokenizer(metaclass=DummyObject):
+    _backends = ["sentencepiece"]
 
-class XLNetTokenizer:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["sentencepiece"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["sentencepiece"])

src/transformers/utils/dummy_speech_objects.py

@@ -1,7 +1,10 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
-class Speech2TextFeatureExtractor:
+class Speech2TextFeatureExtractor(metaclass=DummyObject):
+    _backends = ["speech"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["speech"])

src/transformers/utils/dummy_timm_and_vision_objects.py

@@ -1,53 +1,34 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
 DETR_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
-class DetrForObjectDetection:
+class DetrForObjectDetection(metaclass=DummyObject):
+    _backends = ["timm", "vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["timm", "vision"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["timm", "vision"])
 
-    def forward(self, *args, **kwargs):
+class DetrForSegmentation(metaclass=DummyObject):
-        requires_backends(self, ["timm", "vision"])
+    _backends = ["timm", "vision"]
 
-
-class DetrForSegmentation:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["timm", "vision"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["timm", "vision"])
 
-    def forward(self, *args, **kwargs):
+class DetrModel(metaclass=DummyObject):
-        requires_backends(self, ["timm", "vision"])
+    _backends = ["timm", "vision"]
 
-
-class DetrModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["timm", "vision"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["timm", "vision"])
 
-    def forward(self, *args, **kwargs):
+class DetrPreTrainedModel(metaclass=DummyObject):
-        requires_backends(self, ["timm", "vision"])
+    _backends = ["timm", "vision"]
 
-
-class DetrPreTrainedModel:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["timm", "vision"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["timm", "vision"])
-
-    def forward(self, *args, **kwargs):
-        requires_backends(self, ["timm", "vision"])

src/transformers/utils/dummy_tokenizers_objects.py

@@ -1,398 +1,311 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
-class AlbertTokenizerFast:
+class AlbertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class BartTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class BartTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class BarthezTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class BarthezTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class BertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class BertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class BigBirdTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class BigBirdTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class BlenderbotTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class BlenderbotTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class BlenderbotSmallTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class BlenderbotSmallTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class CamembertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class CamembertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class CLIPTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class CLIPTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class ConvBertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class ConvBertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class DebertaTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class DebertaTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class DistilBertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class DistilBertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class DPRContextEncoderTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class DPRContextEncoderTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class DPRQuestionEncoderTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class DPRQuestionEncoderTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class DPRReaderTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class DPRReaderTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class ElectraTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class ElectraTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class FNetTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class FNetTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class FunnelTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class FunnelTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class GPT2TokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class GPT2TokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class HerbertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class HerbertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class LayoutLMTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class LayoutLMTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class LayoutLMv2TokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class LayoutLMv2TokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class LayoutXLMTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class LayoutXLMTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class LEDTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class LEDTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class LongformerTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class LongformerTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class LxmertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class LxmertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class MBartTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class MBartTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class MBart50TokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class MBart50TokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class MobileBertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class MobileBertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class MPNetTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class MPNetTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class MT5TokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class MT5TokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class OpenAIGPTTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class OpenAIGPTTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class PegasusTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class PegasusTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class ReformerTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class ReformerTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class RemBertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class RemBertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class RetriBertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class RetriBertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class RobertaTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class RobertaTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class RoFormerTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class RoFormerTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class SplinterTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class SplinterTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class SqueezeBertTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class SqueezeBertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class T5TokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class T5TokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class XLMRobertaTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class XLMRobertaTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class XLNetTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class XLNetTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])
 
+class PreTrainedTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
 
-class PreTrainedTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["tokenizers"])

src/transformers/utils/dummy_vision_objects.py

@@ -1,79 +1,94 @@
 # This file is autogenerated by the command `make fix-copies`, do not edit.
-from ..file_utils import requires_backends
+# flake8: noqa
+from ..file_utils import DummyObject, requires_backends
 
 
-class ImageFeatureExtractionMixin:
+class ImageFeatureExtractionMixin(metaclass=DummyObject):
+    _backends = ["vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class BeitFeatureExtractor:
+class BeitFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class CLIPFeatureExtractor:
+class CLIPFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class CLIPProcessor:
+class CLIPProcessor(metaclass=DummyObject):
-    def __init__(self, *args, **kwargs):
+    _backends = ["vision"]
-        requires_backends(self, ["vision"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["vision"])
-
-
-class DeiTFeatureExtractor:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class DetrFeatureExtractor:
+class DeiTFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class ImageGPTFeatureExtractor:
+class DetrFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class LayoutLMv2FeatureExtractor:
+class ImageGPTFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class LayoutLMv2Processor:
+class LayoutLMv2FeatureExtractor(metaclass=DummyObject):
-    def __init__(self, *args, **kwargs):
+    _backends = ["vision"]
-        requires_backends(self, ["vision"])
 
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["vision"])
-
-
-class LayoutXLMProcessor:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["vision"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["vision"])
-
-
-class PerceiverFeatureExtractor:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class SegformerFeatureExtractor:
+class LayoutLMv2Processor(metaclass=DummyObject):
+    _backends = ["vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])
 
 
-class ViTFeatureExtractor:
+class LayoutXLMProcessor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
+class PerceiverFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
+class SegformerFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["vision"])
+
+
+class ViTFeatureExtractor(metaclass=DummyObject):
+    _backends = ["vision"]
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["vision"])

utils/check_dummies.py

@@ -33,46 +33,15 @@ DUMMY_CONSTANT = """
 {0} = None
 """
 
-DUMMY_PRETRAINED_CLASS = """
-class {0}:
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, {1})
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, {1})
-"""
-
-PT_DUMMY_PRETRAINED_CLASS = (
-    DUMMY_PRETRAINED_CLASS
-    + """
-    def forward(self, *args, **kwargs):
-        requires_backends(self, {1})
-"""
-)
-
-TF_DUMMY_PRETRAINED_CLASS = (
-    DUMMY_PRETRAINED_CLASS
-    + """
-    def call(self, *args, **kwargs):
-        requires_backends(self, {1})
-"""
-)
-
-FLAX_DUMMY_PRETRAINED_CLASS = (
-    DUMMY_PRETRAINED_CLASS
-    + """
-    def __call__(self, *args, **kwargs):
-        requires_backends(self, {1})
-"""
-)
-
 DUMMY_CLASS = """
-class {0}:
+class {0}(metaclass=DummyObject):
+    _backends = {1}
+
     def __init__(self, *args, **kwargs):
         requires_backends(self, {1})
 """
 
+
 DUMMY_FUNCTION = """
 def {0}(*args, **kwargs):
     requires_backends({0}, {1})
@@ -126,45 +95,12 @@ def read_init():
 
 def create_dummy_object(name, backend_name):
     """Create the code for the dummy object corresponding to `name`."""
-    _models = [
-        "ForCausalLM",
-        "ForConditionalGeneration",
-        "ForMaskedLM",
-        "ForMultipleChoice",
-        "ForNextSentencePrediction",
-        "ForObjectDetection",
-        "ForQuestionAnswering",
-        "ForSegmentation",
-        "ForSequenceClassification",
-        "ForTokenClassification",
-        "Model",
-    ]
-    _pretrained = ["Config", "Tokenizer", "Processor"]
     if name.isupper():
         return DUMMY_CONSTANT.format(name)
     elif name.islower():
         return DUMMY_FUNCTION.format(name, backend_name)
     else:
-        is_model = False
+        return DUMMY_CLASS.format(name, backend_name)
-        for part in _models:
-            if part in name:
-                is_model = True
-                break
-        if is_model:
-            if name.startswith("TF"):
-                return TF_DUMMY_PRETRAINED_CLASS.format(name, backend_name)
-            if name.startswith("Flax"):
-                return FLAX_DUMMY_PRETRAINED_CLASS.format(name, backend_name)
-            return PT_DUMMY_PRETRAINED_CLASS.format(name, backend_name)
-        is_pretrained = False
-        for part in _pretrained:
-            if part in name:
-                is_pretrained = True
-                break
-        if is_pretrained:
-            return DUMMY_PRETRAINED_CLASS.format(name, backend_name)
-        else:
-            return DUMMY_CLASS.format(name, backend_name)
 
 
 def create_dummy_files():
@@ -176,7 +112,8 @@ def create_dummy_files():
     for backend, objects in backend_specific_objects.items():
         backend_name = "[" + ", ".join(f'"{b}"' for b in backend.split("_and_")) + "]"
         dummy_file = "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
-        dummy_file += "from ..file_utils import requires_backends\n\n"
+        dummy_file += "# flake8: noqa\n"
+        dummy_file += "from ..file_utils import DummyObject, requires_backends\n\n"
         dummy_file += "\n".join([create_dummy_object(o, backend_name) for o in objects])
         dummy_files[backend] = dummy_file
 

utils/check_repo.py

@@ -522,6 +522,7 @@ UNDOCUMENTED_OBJECTS = [
     "BasicTokenizer",  # Internal, should never have been in the main init.
     "CharacterTokenizer",  # Internal, should never have been in the main init.
     "DPRPretrainedReader",  # Like an Encoder.
+    "DummyObject",  # Just picked by mistake sometimes.
     "MecabTokenizer",  # Internal, should never have been in the main init.
     "ModelCard",  # Internal type.
     "SqueezeBertModule",  # Internal building block (should have been called SqueezeBertLayer)