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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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from transformers.models.auto.configuration_auto import AutoConfig |
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import copy |
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logger = logging.get_logger(__name__) |
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class SAMCaptionerConfig(PretrainedConfig): |
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model_type = "sam-captioner" |
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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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sam_config = kwargs.pop("sam", None) |
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sam_model_type = sam_config.pop("model_type", None) |
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captioner_config = kwargs.pop("captioner", None) |
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captioner_model_type = captioner_config.pop("model_type", None) |
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self.use_vcot = kwargs.pop("use_vcot", False) |
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self.dtype = kwargs.pop("dtype", "float32") |
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self.sam = AutoConfig.for_model(sam_model_type, **sam_config) |
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self.captioner = AutoConfig.for_model(captioner_model_type, **captioner_config) |
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@classmethod |
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def from_sam_captioner_configs( |
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cls, sam_config: PretrainedConfig, captioner_config: PretrainedConfig, dtype, use_vcot, **kwargs |
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) -> PretrainedConfig: |
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return cls( |
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sam=sam_config.to_dict(), captioner=captioner_config.to_dict(), dtype=dtype, use_vcot=use_vcot, **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["sam"] = self.sam.to_dict() |
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output["captioner"] = self.captioner.to_dict() |
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output["model_type"] = self.__class__.model_type |
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return output |
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