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|---|---|---|
class XCLIPTextModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,886 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XCLIPVisionModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,887 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XGLMForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,888 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XGLMModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,889 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XGLMPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,890 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,891 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,892 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMForQuestionAnsweringSimple(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,893 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,894 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,895 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,896 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,897 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMWithLMHeadModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,898 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,899 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,900 |
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|
class XLMRobertaForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,901 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,902 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,903 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,904 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,905 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,906 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaXLForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,907 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaXLForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,908 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaXLForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,909 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaXLForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,910 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaXLForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,911 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaXLForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,912 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLMRobertaXLModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,913 |
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|
class XLMRobertaXLPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,914 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLNetForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,915 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLNetForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,916 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py
|
class XLNetForQuestionAnsweringSimple(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,917 |
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|
class XLNetForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,918 |
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|
class XLNetForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,919 |
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|
class XLNetLMHeadModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,920 |
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|
class XLNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,921 |
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|
class XLNetPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,922 |
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|
class XmodForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,923 |
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|
class XmodForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,924 |
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|
class XmodForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,925 |
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|
class XmodForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,926 |
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|
class XmodForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,927 |
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|
class XmodForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,928 |
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|
class XmodModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,929 |
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|
class XmodPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,930 |
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|
class YolosForObjectDetection(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,931 |
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|
class YolosModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,932 |
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|
class YolosPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,933 |
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|
class YosoForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,934 |
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|
class YosoForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,935 |
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|
class YosoForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,936 |
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|
class YosoForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,937 |
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|
class YosoForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,938 |
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|
class YosoModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,939 |
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|
class YosoPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,940 |
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|
class ZambaForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,941 |
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|
class ZambaForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,942 |
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|
class ZambaModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,943 |
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|
class ZambaPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,944 |
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|
class ZoeDepthForDepthEstimation(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,945 |
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|
class ZoeDepthPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,946 |
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|
class Adafactor(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,947 |
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|
class AdamW(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,948 |
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|
class Conv1D(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,949 |
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|
class Trainer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,950 |
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|
class Seq2SeqTrainer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
| 1,951 |
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|
class TFGPT2Tokenizer(metaclass=DummyObject):
_backends = ["keras_nlp"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["keras_nlp"])
| 1,952 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_keras_nlp_objects.py
|
class MusicgenMelodyFeatureExtractor(metaclass=DummyObject):
_backends = ["torchaudio"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchaudio"])
| 1,953 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_torchaudio_objects.py
|
class MusicgenMelodyProcessor(metaclass=DummyObject):
_backends = ["torchaudio"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchaudio"])
| 1,954 |
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|
class cached_property(property):
"""
Descriptor that mimics @property but caches output in member variable.
From tensorflow_datasets
Built-in in functools from Python 3.8.
"""
def __get__(self, obj, objtype=None):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
attr = "__cached_" + self.fget.__name__
cached = getattr(obj, attr, None)
if cached is None:
cached = self.fget(obj)
setattr(obj, attr, cached)
return cached
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|
class ModelOutput(OrderedDict):
"""
Base class for all model outputs as dataclass. Has a `__getitem__` that allows indexing by integer or slice (like a
tuple) or strings (like a dictionary) that will ignore the `None` attributes. Otherwise behaves like a regular
python dictionary.
<Tip warning={true}>
You can't unpack a `ModelOutput` directly. Use the [`~utils.ModelOutput.to_tuple`] method to convert it to a tuple
before.
</Tip>
"""
def __init_subclass__(cls) -> None:
"""Register subclasses as pytree nodes.
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|
This is necessary to synchronize gradients when using `torch.nn.parallel.DistributedDataParallel` with
`static_graph=True` with modules that output `ModelOutput` subclasses.
"""
if is_torch_available():
if version.parse(get_torch_version()) >= version.parse("2.2"):
_torch_pytree.register_pytree_node(
cls,
_model_output_flatten,
partial(_model_output_unflatten, output_type=cls),
serialized_type_name=f"{cls.__module__}.{cls.__name__}",
)
else:
_torch_pytree._register_pytree_node(
cls,
_model_output_flatten,
partial(_model_output_unflatten, output_type=cls),
)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
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|
# Subclasses of ModelOutput must use the @dataclass decorator
# This check is done in __init__ because the @dataclass decorator operates after __init_subclass__
# issubclass() would return True for issubclass(ModelOutput, ModelOutput) when False is needed
# Just need to check that the current class is not ModelOutput
is_modeloutput_subclass = self.__class__ != ModelOutput
if is_modeloutput_subclass and not is_dataclass(self):
raise TypeError(
f"{self.__module__}.{self.__class__.__name__} is not a dataclasss."
" This is a subclass of ModelOutput and so must use the @dataclass decorator."
)
def __post_init__(self):
"""Check the ModelOutput dataclass.
Only occurs if @dataclass decorator has been used.
"""
class_fields = fields(self)
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|
# Safety and consistency checks
if not len(class_fields):
raise ValueError(f"{self.__class__.__name__} has no fields.")
if not all(field.default is None for field in class_fields[1:]):
raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")
first_field = getattr(self, class_fields[0].name)
other_fields_are_none = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and not is_tensor(first_field):
if isinstance(first_field, dict):
iterator = first_field.items()
first_field_iterator = True
else:
try:
iterator = iter(first_field)
first_field_iterator = True
except TypeError:
first_field_iterator = False
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|
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(iterator):
if (
not isinstance(element, (list, tuple))
or not len(element) == 2
or not isinstance(element[0], str)
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
self[class_fields[0].name] = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f"Cannot set key/value for {element}. It needs to be a tuple (key, value)."
)
break
| 1,956 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
setattr(self, element[0], element[1])
if element[1] is not None:
self[element[0]] = element[1]
elif first_field is not None:
self[class_fields[0].name] = first_field
else:
for field in class_fields:
v = getattr(self, field.name)
if v is not None:
self[field.name] = v
| 1,956 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
def __delitem__(self, *args, **kwargs):
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def setdefault(self, *args, **kwargs):
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def pop(self, *args, **kwargs):
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def update(self, *args, **kwargs):
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__(self, k):
if isinstance(k, str):
inner_dict = dict(self.items())
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__(self, name, value):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(name, value)
super().__setattr__(name, value)
| 1,956 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
def __setitem__(self, key, value):
# Will raise a KeyException if needed
super().__setitem__(key, value)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(key, value)
def __reduce__(self):
if not is_dataclass(self):
return super().__reduce__()
callable, _args, *remaining = super().__reduce__()
args = tuple(getattr(self, field.name) for field in fields(self))
return callable, args, *remaining
def to_tuple(self) -> Tuple[Any]:
"""
Convert self to a tuple containing all the attributes/keys that are not `None`.
"""
return tuple(self[k] for k in self.keys())
| 1,956 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
class ExplicitEnum(str, Enum):
"""
Enum with more explicit error message for missing values.
"""
@classmethod
def _missing_(cls, value):
raise ValueError(
f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}"
)
| 1,957 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
class PaddingStrategy(ExplicitEnum):
"""
Possible values for the `padding` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for tab-completion in an
IDE.
"""
LONGEST = "longest"
MAX_LENGTH = "max_length"
DO_NOT_PAD = "do_not_pad"
| 1,958 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
class TensorType(ExplicitEnum):
"""
Possible values for the `return_tensors` argument in [`PreTrainedTokenizerBase.__call__`]. Useful for
tab-completion in an IDE.
"""
PYTORCH = "pt"
TENSORFLOW = "tf"
NUMPY = "np"
JAX = "jax"
MLX = "mlx"
| 1,959 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
class ContextManagers:
"""
Wrapper for `contextlib.ExitStack` which enters a collection of context managers. Adaptation of `ContextManagers`
in the `fastcore` library.
"""
def __init__(self, context_managers: List[ContextManager]):
self.context_managers = context_managers
self.stack = ExitStack()
def __enter__(self):
for context_manager in self.context_managers:
self.stack.enter_context(context_manager)
def __exit__(self, *args, **kwargs):
self.stack.__exit__(*args, **kwargs)
| 1,960 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
class LossKwargs(TypedDict, total=False):
"""
Keyword arguments to be passed to the loss function
Attributes:
num_items_in_batch (`int`, *optional*):
Number of items in the batch. It is recommended to pass it when
you are doing gradient accumulation.
"""
num_items_in_batch: Optional[int]
| 1,961 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py
|
class ImageProcessingMixin(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,962 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class BaseImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,963 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class ImageFeatureExtractionMixin(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,964 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class AriaImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,965 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class BeitFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,966 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class BeitImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,967 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class BitImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,968 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class BlipImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,969 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class BridgeTowerImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,970 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class ChameleonImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,971 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class ChineseCLIPFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,972 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class ChineseCLIPImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,973 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class CLIPFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,974 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class CLIPImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,975 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class ConditionalDetrFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,976 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class ConditionalDetrImageProcessor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,977 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
class ConvNextFeatureExtractor(metaclass=DummyObject):
_backends = ["vision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["vision"])
| 1,978 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_vision_objects.py
|
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