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import copy |
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from transformers import GPT2Config, ViTConfig |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class ViTGPT2Config(PretrainedConfig): |
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model_type = "vit-gpt2" |
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is_composition = True |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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if "vit_config" not in kwargs: |
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raise ValueError("`vit_config` can not be `None`.") |
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if "gpt2_config" not in kwargs: |
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raise ValueError("`gpt2_config` can not be `None`.") |
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vit_config = kwargs.pop("vit_config") |
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gpt2_config = kwargs.pop("gpt2_config") |
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self.vit_config = ViTConfig(**vit_config) |
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self.gpt2_config = GPT2Config(**gpt2_config) |
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@classmethod |
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def from_vit_gpt2_configs( |
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cls, vit_config: PretrainedConfig, gpt2_config: PretrainedConfig, **kwargs |
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): |
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return cls( |
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vit_config=vit_config.to_dict(), |
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gpt2_config=gpt2_config.to_dict(), |
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**kwargs |
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) |
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def to_dict(self): |
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output = copy.deepcopy(self.__dict__) |
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output["vit_config"] = self.vit_config.to_dict() |
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output["gpt2_config"] = self.gpt2_config.to_dict() |
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output["model_type"] = self.__class__.model_type |
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return output |
