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class TFSegformerForSemanticSegmentation(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 55,415 | 55,584 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,400 |
class TFSegformerModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 55,587 | 55,738 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,401 |
class TFSegformerPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 55,741 | 55,902 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,402 |
class TFSpeech2TextForConditionalGeneration(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 55,905 | 56,077 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,403 |
class TFSpeech2TextModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 56,080 | 56,233 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,404 |
class TFSpeech2TextPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 56,236 | 56,399 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,405 |
class TFSwiftFormerForImageClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 56,402 | 56,572 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,406 |
class TFSwiftFormerModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 56,575 | 56,728 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,407 |
class TFSwiftFormerPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 56,731 | 56,894 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,408 |
class TFSwinForImageClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 56,897 | 57,060 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,409 |
class TFSwinForMaskedImageModeling(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 57,063 | 57,226 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,410 |
class TFSwinModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 57,229 | 57,375 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,411 |
class TFSwinPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 57,378 | 57,534 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,412 |
class TFT5EncoderModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 57,537 | 57,688 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,413 |
class TFT5ForConditionalGeneration(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 57,691 | 57,854 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,414 |
class TFT5Model(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 57,857 | 58,001 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,415 |
class TFT5PreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 58,004 | 58,158 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,416 |
class TFTapasForMaskedLM(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 58,161 | 58,314 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,417 |
class TFTapasForQuestionAnswering(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 58,317 | 58,479 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,418 |
class TFTapasForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 58,482 | 58,649 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,419 |
class TFTapasModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 58,652 | 58,799 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,420 |
class TFTapasPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 58,802 | 58,959 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,421 |
class TFVisionEncoderDecoderModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 58,962 | 59,124 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,422 |
class TFVisionTextDualEncoderModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 59,127 | 59,290 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,423 |
class TFViTForImageClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 59,293 | 59,455 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,424 |
class TFViTModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 59,458 | 59,603 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,425 |
class TFViTPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 59,606 | 59,761 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,426 |
class TFViTMAEForPreTraining(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 59,764 | 59,921 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,427 |
class TFViTMAEModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 59,924 | 60,072 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,428 |
class TFViTMAEPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 60,075 | 60,233 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,429 |
class TFWav2Vec2ForCTC(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 60,236 | 60,387 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,430 |
class TFWav2Vec2ForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 60,390 | 60,560 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,431 |
class TFWav2Vec2Model(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 60,563 | 60,713 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,432 |
class TFWav2Vec2PreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 60,716 | 60,876 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,433 |
class TFWhisperForConditionalGeneration(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 60,879 | 61,047 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,434 |
class TFWhisperModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 61,050 | 61,199 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,435 |
class TFWhisperPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 61,202 | 61,361 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,436 |
class TFXGLMForCausalLM(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 61,364 | 61,516 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,437 |
class TFXGLMModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 61,519 | 61,665 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,438 |
class TFXGLMPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 61,668 | 61,824 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,439 |
class TFXLMForMultipleChoice(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 61,827 | 61,984 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,440 |
class TFXLMForQuestionAnsweringSimple(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 61,987 | 62,153 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,441 |
class TFXLMForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 62,156 | 62,321 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,442 |
class TFXLMForTokenClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 62,324 | 62,486 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,443 |
class TFXLMMainLayer(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 62,489 | 62,638 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,444 |
class TFXLMModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 62,641 | 62,786 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,445 |
class TFXLMPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 62,789 | 62,944 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,446 |
class TFXLMWithLMHeadModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 62,947 | 63,102 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,447 |
class TFXLMRobertaForCausalLM(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 63,105 | 63,263 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,448 |
class TFXLMRobertaForMaskedLM(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 63,266 | 63,424 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,449 |
class TFXLMRobertaForMultipleChoice(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 63,427 | 63,591 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,450 |
class TFXLMRobertaForQuestionAnswering(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 63,594 | 63,761 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,451 |
class TFXLMRobertaForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 63,764 | 63,936 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,452 |
class TFXLMRobertaForTokenClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 63,939 | 64,108 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,453 |
class TFXLMRobertaModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 64,111 | 64,263 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,454 |
class TFXLMRobertaPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 64,266 | 64,428 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,455 |
class TFXLNetForMultipleChoice(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 64,431 | 64,590 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,456 |
class TFXLNetForQuestionAnsweringSimple(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 64,593 | 64,761 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,457 |
class TFXLNetForSequenceClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 64,764 | 64,931 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,458 |
class TFXLNetForTokenClassification(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 64,934 | 65,098 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,459 |
class TFXLNetLMHeadModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 65,101 | 65,254 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,460 |
class TFXLNetMainLayer(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 65,257 | 65,408 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,461 |
class TFXLNetModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 65,411 | 65,558 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,462 |
class TFXLNetPreTrainedModel(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 65,561 | 65,718 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,463 |
class AdamWeightDecay(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 65,721 | 65,871 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,464 |
class GradientAccumulator(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 65,874 | 66,028 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,465 |
class WarmUp(metaclass=DummyObject):
_backends = ["tf"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tf"])
|
class_definition
| 66,031 | 66,172 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tf_objects.py
| null | 2,466 |
class TypeHintParsingException(Exception):
"""Exception raised for errors in parsing type hints to generate JSON schemas"""
pass
|
class_definition
| 2,256 | 2,393 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/chat_template_utils.py
| null | 2,467 |
class DocstringParsingException(Exception):
"""Exception raised for errors in parsing docstrings to generate JSON schemas"""
pass
|
class_definition
| 2,396 | 2,534 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/chat_template_utils.py
| null | 2,468 |
class AssistantTracker(Extension):
# This extension is used to track the indices of assistant-generated tokens in the rendered chat
tags = {"generation"}
def __init__(self, environment: ImmutableSandboxedEnvironment):
# The class is only initiated by jinja.
super().__init__(environment)
environment.extend(activate_tracker=self.activate_tracker)
self._rendered_blocks = None
self._generation_indices = None
def parse(self, parser: jinja2.parser.Parser) -> jinja2.nodes.CallBlock:
lineno = next(parser.stream).lineno
body = parser.parse_statements(["name:endgeneration"], drop_needle=True)
return jinja2.nodes.CallBlock(self.call_method("_generation_support"), [], [], body).set_lineno(lineno)
@jinja2.pass_eval_context
def _generation_support(self, context: jinja2.nodes.EvalContext, caller: jinja2.runtime.Macro) -> str:
rv = caller()
if self.is_active():
# Only track generation indices if the tracker is active
start_index = len("".join(self._rendered_blocks))
end_index = start_index + len(rv)
self._generation_indices.append((start_index, end_index))
return rv
def is_active(self) -> bool:
return self._rendered_blocks or self._generation_indices
@contextmanager
def activate_tracker(self, rendered_blocks: List[int], generation_indices: List[int]):
try:
if self.is_active():
raise ValueError("AssistantTracker should not be reused before closed")
self._rendered_blocks = rendered_blocks
self._generation_indices = generation_indices
yield
finally:
self._rendered_blocks = None
self._generation_indices = None
|
class_definition
| 14,166 | 16,107 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/chat_template_utils.py
| null | 2,469 |
class PushToHubMixin:
"""
A Mixin containing the functionality to push a model or tokenizer to the hub.
"""
def _create_repo(
self,
repo_id: str,
private: Optional[bool] = None,
token: Optional[Union[bool, str]] = None,
repo_url: Optional[str] = None,
organization: Optional[str] = None,
) -> str:
"""
Create the repo if needed, cleans up repo_id with deprecated kwargs `repo_url` and `organization`, retrieves
the token.
"""
if repo_url is not None:
warnings.warn(
"The `repo_url` argument is deprecated and will be removed in v5 of Transformers. Use `repo_id` "
"instead."
)
if repo_id is not None:
raise ValueError(
"`repo_id` and `repo_url` are both specified. Please set only the argument `repo_id`."
)
repo_id = repo_url.replace(f"{HUGGINGFACE_CO_RESOLVE_ENDPOINT}/", "")
if organization is not None:
warnings.warn(
"The `organization` argument is deprecated and will be removed in v5 of Transformers. Set your "
"organization directly in the `repo_id` passed instead (`repo_id={organization}/{model_id}`)."
)
if not repo_id.startswith(organization):
if "/" in repo_id:
repo_id = repo_id.split("/")[-1]
repo_id = f"{organization}/{repo_id}"
url = create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True)
return url.repo_id
def _get_files_timestamps(self, working_dir: Union[str, os.PathLike]):
"""
Returns the list of files with their last modification timestamp.
"""
return {f: os.path.getmtime(os.path.join(working_dir, f)) for f in os.listdir(working_dir)}
def _upload_modified_files(
self,
working_dir: Union[str, os.PathLike],
repo_id: str,
files_timestamps: Dict[str, float],
commit_message: Optional[str] = None,
token: Optional[Union[bool, str]] = None,
create_pr: bool = False,
revision: str = None,
commit_description: str = None,
):
"""
Uploads all modified files in `working_dir` to `repo_id`, based on `files_timestamps`.
"""
if commit_message is None:
if "Model" in self.__class__.__name__:
commit_message = "Upload model"
elif "Config" in self.__class__.__name__:
commit_message = "Upload config"
elif "Tokenizer" in self.__class__.__name__:
commit_message = "Upload tokenizer"
elif "FeatureExtractor" in self.__class__.__name__:
commit_message = "Upload feature extractor"
elif "Processor" in self.__class__.__name__:
commit_message = "Upload processor"
else:
commit_message = f"Upload {self.__class__.__name__}"
modified_files = [
f
for f in os.listdir(working_dir)
if f not in files_timestamps or os.path.getmtime(os.path.join(working_dir, f)) > files_timestamps[f]
]
# filter for actual files + folders at the root level
modified_files = [
f
for f in modified_files
if os.path.isfile(os.path.join(working_dir, f)) or os.path.isdir(os.path.join(working_dir, f))
]
operations = []
# upload standalone files
for file in modified_files:
if os.path.isdir(os.path.join(working_dir, file)):
# go over individual files of folder
for f in os.listdir(os.path.join(working_dir, file)):
operations.append(
CommitOperationAdd(
path_or_fileobj=os.path.join(working_dir, file, f), path_in_repo=os.path.join(file, f)
)
)
else:
operations.append(
CommitOperationAdd(path_or_fileobj=os.path.join(working_dir, file), path_in_repo=file)
)
if revision is not None and not revision.startswith("refs/pr"):
try:
create_branch(repo_id=repo_id, branch=revision, token=token, exist_ok=True)
except HfHubHTTPError as e:
if e.response.status_code == 403 and create_pr:
# If we are creating a PR on a repo we don't have access to, we can't create the branch.
# so let's assume the branch already exists. If it's not the case, an error will be raised when
# calling `create_commit` below.
pass
else:
raise
logger.info(f"Uploading the following files to {repo_id}: {','.join(modified_files)}")
return create_commit(
repo_id=repo_id,
operations=operations,
commit_message=commit_message,
commit_description=commit_description,
token=token,
create_pr=create_pr,
revision=revision,
)
def push_to_hub(
self,
repo_id: str,
use_temp_dir: Optional[bool] = None,
commit_message: Optional[str] = None,
private: Optional[bool] = None,
token: Optional[Union[bool, str]] = None,
max_shard_size: Optional[Union[int, str]] = "5GB",
create_pr: bool = False,
safe_serialization: bool = True,
revision: str = None,
commit_description: str = None,
tags: Optional[List[str]] = None,
**deprecated_kwargs,
) -> str:
"""
Upload the {object_files} to the 🤗 Model Hub.
Parameters:
repo_id (`str`):
The name of the repository you want to push your {object} to. It should contain your organization name
when pushing to a given organization.
use_temp_dir (`bool`, *optional*):
Whether or not to use a temporary directory to store the files saved before they are pushed to the Hub.
Will default to `True` if there is no directory named like `repo_id`, `False` otherwise.
commit_message (`str`, *optional*):
Message to commit while pushing. Will default to `"Upload {object}"`.
private (`bool`, *optional*):
Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.
token (`bool` or `str`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
when running `huggingface-cli login` (stored in `~/.huggingface`). Will default to `True` if `repo_url`
is not specified.
max_shard_size (`int` or `str`, *optional*, defaults to `"5GB"`):
Only applicable for models. The maximum size for a checkpoint before being sharded. Checkpoints shard
will then be each of size lower than this size. If expressed as a string, needs to be digits followed
by a unit (like `"5MB"`). We default it to `"5GB"` so that users can easily load models on free-tier
Google Colab instances without any CPU OOM issues.
create_pr (`bool`, *optional*, defaults to `False`):
Whether or not to create a PR with the uploaded files or directly commit.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether or not to convert the model weights in safetensors format for safer serialization.
revision (`str`, *optional*):
Branch to push the uploaded files to.
commit_description (`str`, *optional*):
The description of the commit that will be created
tags (`List[str]`, *optional*):
List of tags to push on the Hub.
Examples:
```python
from transformers import {object_class}
{object} = {object_class}.from_pretrained("google-bert/bert-base-cased")
# Push the {object} to your namespace with the name "my-finetuned-bert".
{object}.push_to_hub("my-finetuned-bert")
# Push the {object} to an organization with the name "my-finetuned-bert".
{object}.push_to_hub("huggingface/my-finetuned-bert")
```
"""
use_auth_token = deprecated_kwargs.pop("use_auth_token", None)
ignore_metadata_errors = deprecated_kwargs.pop("ignore_metadata_errors", False)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
token = use_auth_token
repo_path_or_name = deprecated_kwargs.pop("repo_path_or_name", None)
if repo_path_or_name is not None:
# Should use `repo_id` instead of `repo_path_or_name`. When using `repo_path_or_name`, we try to infer
# repo_id from the folder path, if it exists.
warnings.warn(
"The `repo_path_or_name` argument is deprecated and will be removed in v5 of Transformers. Use "
"`repo_id` instead.",
FutureWarning,
)
if repo_id is not None:
raise ValueError(
"`repo_id` and `repo_path_or_name` are both specified. Please set only the argument `repo_id`."
)
if os.path.isdir(repo_path_or_name):
# repo_path: infer repo_id from the path
repo_id = repo_id.split(os.path.sep)[-1]
working_dir = repo_id
else:
# repo_name: use it as repo_id
repo_id = repo_path_or_name
working_dir = repo_id.split("/")[-1]
else:
# Repo_id is passed correctly: infer working_dir from it
working_dir = repo_id.split("/")[-1]
# Deprecation warning will be sent after for repo_url and organization
repo_url = deprecated_kwargs.pop("repo_url", None)
organization = deprecated_kwargs.pop("organization", None)
repo_id = self._create_repo(
repo_id, private=private, token=token, repo_url=repo_url, organization=organization
)
# Create a new empty model card and eventually tag it
model_card = create_and_tag_model_card(
repo_id, tags, token=token, ignore_metadata_errors=ignore_metadata_errors
)
if use_temp_dir is None:
use_temp_dir = not os.path.isdir(working_dir)
with working_or_temp_dir(working_dir=working_dir, use_temp_dir=use_temp_dir) as work_dir:
files_timestamps = self._get_files_timestamps(work_dir)
# Save all files.
self.save_pretrained(work_dir, max_shard_size=max_shard_size, safe_serialization=safe_serialization)
# Update model card if needed:
model_card.save(os.path.join(work_dir, "README.md"))
return self._upload_modified_files(
work_dir,
repo_id,
files_timestamps,
commit_message=commit_message,
token=token,
create_pr=create_pr,
revision=revision,
commit_description=commit_description,
)
|
class_definition
| 30,571 | 42,477 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/hub.py
| null | 2,470 |
class PushInProgress:
"""
Internal class to keep track of a push in progress (which might contain multiple `Future` jobs).
"""
def __init__(self, jobs: Optional[futures.Future] = None) -> None:
self.jobs = [] if jobs is None else jobs
def is_done(self):
return all(job.done() for job in self.jobs)
def wait_until_done(self):
futures.wait(self.jobs)
def cancel(self) -> None:
self.jobs = [
job
for job in self.jobs
# Cancel the job if it wasn't started yet and remove cancelled/done jobs from the list
if not (job.cancel() or job.done())
]
|
class_definition
| 55,171 | 55,829 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/hub.py
| null | 2,471 |
class ASTFeatureExtractor(metaclass=DummyObject):
_backends = ["speech"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["speech"])
|
class_definition
| 129 | 291 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_speech_objects.py
| null | 2,472 |
class Speech2TextFeatureExtractor(metaclass=DummyObject):
_backends = ["speech"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["speech"])
|
class_definition
| 294 | 464 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_speech_objects.py
| null | 2,473 |
class BaseImageProcessorFast(metaclass=DummyObject):
_backends = ["torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchvision"])
|
class_definition
| 129 | 304 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_torchvision_objects.py
| null | 2,474 |
class DeformableDetrImageProcessorFast(metaclass=DummyObject):
_backends = ["torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchvision"])
|
class_definition
| 307 | 492 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_torchvision_objects.py
| null | 2,475 |
class DetrImageProcessorFast(metaclass=DummyObject):
_backends = ["torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchvision"])
|
class_definition
| 495 | 670 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_torchvision_objects.py
| null | 2,476 |
class PixtralImageProcessorFast(metaclass=DummyObject):
_backends = ["torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchvision"])
|
class_definition
| 673 | 851 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_torchvision_objects.py
| null | 2,477 |
class RTDetrImageProcessorFast(metaclass=DummyObject):
_backends = ["torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchvision"])
|
class_definition
| 854 | 1,031 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_torchvision_objects.py
| null | 2,478 |
class ViTImageProcessorFast(metaclass=DummyObject):
_backends = ["torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchvision"])
|
class_definition
| 1,034 | 1,208 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_torchvision_objects.py
| null | 2,479 |
class BackboneType(enum.Enum):
TIMM = "timm"
TRANSFORMERS = "transformers"
|
class_definition
| 856 | 938 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/backbone_utils.py
| null | 2,480 |
class BackboneMixin:
backbone_type: Optional[BackboneType] = None
def _init_timm_backbone(self, config) -> None:
"""
Initialize the backbone model from timm The backbone must already be loaded to self._backbone
"""
if getattr(self, "_backbone", None) is None:
raise ValueError("self._backbone must be set before calling _init_timm_backbone")
# These will diagree with the defaults for the transformers models e.g. for resnet50
# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
self.stage_names = [stage["module"] for stage in self._backbone.feature_info.info]
self.num_features = [stage["num_chs"] for stage in self._backbone.feature_info.info]
# In some timm versions, out_indices reflects the input type of out_indices on the `create_model` call,
# in later versions >= 1, it is always a tuple
out_indices = list(self._backbone.feature_info.out_indices)
out_features = self._backbone.feature_info.module_name()
# We verify the out indices and out features are valid
verify_out_features_out_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
)
self._out_features, self._out_indices = out_features, out_indices
def _init_transformers_backbone(self, config) -> None:
stage_names = getattr(config, "stage_names")
out_features = getattr(config, "out_features", None)
out_indices = getattr(config, "out_indices", None)
self.stage_names = stage_names
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=stage_names
)
# Number of channels for each stage. This is set in the transformer backbone model init
self.num_features = None
def _init_backbone(self, config) -> None:
"""
Method to initialize the backbone. This method is called by the constructor of the base class after the
pretrained model weights have been loaded.
"""
self.config = config
self.use_timm_backbone = getattr(config, "use_timm_backbone", False)
self.backbone_type = BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS
if self.backbone_type == BackboneType.TIMM:
self._init_timm_backbone(config)
elif self.backbone_type == BackboneType.TRANSFORMERS:
self._init_transformers_backbone(config)
else:
raise ValueError(f"backbone_type {self.backbone_type} not supported.")
@property
def out_features(self):
return self._out_features
@out_features.setter
def out_features(self, out_features: List[str]):
"""
Set the out_features attribute. This will also update the out_indices attribute to match the new out_features.
"""
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=None, stage_names=self.stage_names
)
@property
def out_indices(self):
return self._out_indices
@out_indices.setter
def out_indices(self, out_indices: Union[Tuple[int], List[int]]):
"""
Set the out_indices attribute. This will also update the out_features attribute to match the new out_indices.
"""
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=None, out_indices=out_indices, stage_names=self.stage_names
)
@property
def out_feature_channels(self):
# the current backbones will output the number of channels for each stage
# even if that stage is not in the out_features list.
return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names)}
@property
def channels(self):
return [self.out_feature_channels[name] for name in self.out_features]
def forward_with_filtered_kwargs(self, *args, **kwargs):
signature = dict(inspect.signature(self.forward).parameters)
filtered_kwargs = {k: v for k, v in kwargs.items() if k in signature}
return self(*args, **filtered_kwargs)
def forward(
self,
pixel_values,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
raise NotImplementedError("This method should be implemented by the derived class.")
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig` to
include the `out_features` and `out_indices` attributes.
"""
output = super().to_dict()
output["out_features"] = output.pop("_out_features")
output["out_indices"] = output.pop("_out_indices")
return output
|
class_definition
| 6,912 | 12,057 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/backbone_utils.py
| null | 2,481 |
class BackboneConfigMixin:
"""
A Mixin to support handling the `out_features` and `out_indices` attributes for the backbone configurations.
"""
@property
def out_features(self):
return self._out_features
@out_features.setter
def out_features(self, out_features: List[str]):
"""
Set the out_features attribute. This will also update the out_indices attribute to match the new out_features.
"""
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=None, stage_names=self.stage_names
)
@property
def out_indices(self):
return self._out_indices
@out_indices.setter
def out_indices(self, out_indices: Union[Tuple[int], List[int]]):
"""
Set the out_indices attribute. This will also update the out_features attribute to match the new out_indices.
"""
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=None, out_indices=out_indices, stage_names=self.stage_names
)
def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig` to
include the `out_features` and `out_indices` attributes.
"""
output = super().to_dict()
output["out_features"] = output.pop("_out_features")
output["out_indices"] = output.pop("_out_indices")
return output
|
class_definition
| 12,060 | 13,608 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/backbone_utils.py
| null | 2,482 |
class AlbertTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 129 | 299 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,483 |
class BartTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 302 | 470 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,484 |
class BarthezTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 473 | 644 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,485 |
class BertTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 647 | 815 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,486 |
class BigBirdTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 818 | 989 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,487 |
class BlenderbotTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 992 | 1,166 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,488 |
class BlenderbotSmallTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 1,169 | 1,348 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,489 |
class BloomTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 1,351 | 1,520 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,490 |
class CamembertTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 1,523 | 1,696 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,491 |
class CLIPTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 1,699 | 1,867 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,492 |
class CodeLlamaTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 1,870 | 2,043 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,493 |
class CodeGenTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 2,046 | 2,217 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,494 |
class CohereTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 2,220 | 2,390 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,495 |
class ConvBertTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 2,393 | 2,565 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,496 |
class CpmTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 2,568 | 2,735 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,497 |
class DebertaTokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 2,738 | 2,909 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,498 |
class DebertaV2TokenizerFast(metaclass=DummyObject):
_backends = ["tokenizers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["tokenizers"])
|
class_definition
| 2,912 | 3,085 | 0 |
/Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/utils/dummy_tokenizers_objects.py
| null | 2,499 |
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