text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
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 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
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 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
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 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XLNetForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XLNetForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XLNetLMHeadModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XLNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XLNetPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XmodForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XmodForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,924 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XmodForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XmodForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XmodForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XmodForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XmodModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class XmodPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YolosForObjectDetection(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YolosModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YolosPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YosoForMaskedLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,934 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YosoForMultipleChoice(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,935 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YosoForQuestionAnswering(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,936 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YosoForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,937 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YosoForTokenClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,938 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YosoModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,939 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class YosoPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,940 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class ZambaForCausalLM(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,941 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class ZambaForSequenceClassification(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,942 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class ZambaModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class ZambaPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class ZoeDepthForDepthEstimation(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class ZoeDepthPreTrainedModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,946 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class Adafactor(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,947 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class AdamW(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class Conv1D(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,949 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class Trainer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
class Seq2SeqTrainer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"]) | 1,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_pt_objects.py |
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 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_torchaudio_objects.py |
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... | 1,955 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py |
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={tru... | 1,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py |
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"):
_t... | 1,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py |
# 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 cl... | 1,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py |
# 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.")
... | 1,956 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/generic.py |
# 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))
... | 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(s... | 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):
... | 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().__redu... | 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... | 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_i... | 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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