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from transformers.models.clip.configuration_clip import CLIPVisionConfig
class AestheticsPredictorConfig(CLIPVisionConfig):
model_type = "aesthetics_predictor"
def __init__(
self,
hidden_size: int = 768,
intermediate_size: int = 3072,
projection_dim: int = 512,
num_hidden_layers: int = 12,
num_attention_heads: int = 12,
num_channels: int = 3,
image_size: int = 224,
patch_size: int = 32,
hidden_act: str = "quick_gelu",
layer_norm_eps: float = 0.00001,
attention_dropout: float = 0,
initializer_range: float = 0.02,
initializer_factor: float = 1,
**kwargs,
):
super().__init__(
hidden_size,
intermediate_size,
projection_dim,
num_hidden_layers,
num_attention_heads,
num_channels,
image_size,
patch_size,
hidden_act,
layer_norm_eps,
attention_dropout,
initializer_range,
initializer_factor,
**kwargs,
)
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