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import os
from typing import *
from transformers.configuration_utils import PretrainedConfig
from transformers.models.clip.configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
class BiomedCLIPTextProjectionConfig(PretrainedConfig):
def __init__(
self,
hidden_size=768,
intermediate_size=640,
projection_dim=512,
num_hidden_layers=2,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
cls._set_token_in_kwargs(kwargs)
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
# get the vision config dict if we are loading from CLIPConfig
if config_dict.get("model_type") == "clip":
config_dict = config_dict["text_projection_config"]
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
)
return cls.from_dict(config_dict, **kwargs)
class BiomedCLIPConfig(CLIPConfig):
def __init__(
self, text_config=None, text_projection_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs
):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
super().__init__(text_config, vision_config, projection_dim, logit_scale_init_value, **kwargs)
text_projection_config_dict = kwargs.pop("text_projection_config_dict", None)
if text_projection_config is None:
if text_projection_config_dict is not None:
text_projection_config = {}
_text_projection_config_dict = BiomedCLIPTextProjectionConfig(**text_projection_config_dict)
text_projection_config.update(_text_projection_config_dict)
else:
text_projection_config = BiomedCLIPTextProjectionConfig(**text_projection_config)
self.text_projection_config = text_projection_config
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