optimum/intel/utils/dummy_neural_compressor_objects.py (92 lines of code) (raw):

# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .import_utils import DummyObject, requires_backends class INCModel(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCModelForCausalLM(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCModelForMaskedLM(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCModelForMultipleChoice(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCModelForQuestionAnswering(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCModelForSeq2SeqLM(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCModelForSequenceClassification(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCModelForTokenClassification(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCModelForVision2Seq(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCQuantizer(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCSeq2SeqTrainer(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCTrainer(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"]) class INCConfig(metaclass=DummyObject): _backends = ["neural_compressor"] def __init__(self, *args, **kwargs): requires_backends(self, ["neural_compressor"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["neural_compressor"])