Add configuration code for trust_remote_code

configuration_condensatenet.py

@@ -0,0 +1,42 @@
+"""CondensateNet Configuration"""
+
+from transformers import PretrainedConfig
+from typing import Tuple
+
+
+class CondensateNetConfig(PretrainedConfig):
+    """
+    Configuration for CondensateNet.
+    
+    Stores all hyperparameters needed to reconstruct the model architecture.
+    
+    Args:
+        encoder_variant: EfficientNetV2 variant to use (default: "rw_s")
+        pyramid_channels: Channel dimensions for FPN levels
+        pyramid_dim: Dimension of FPN feature maps
+        use_spatial_attention: Whether to use sparse spatial attention
+        spatial_kernel_size: Kernel size for spatial attention
+        dropout_rate: Dropout rate for regularization
+        num_classes: Number of output classes (1 for binary segmentation)
+    """
+    model_type = "condensatenet"
+    
+    def __init__(
+        self,
+        encoder_variant: str = "rw_s",
+        pyramid_channels: Tuple[int, ...] = (24, 48, 64, 160),
+        pyramid_dim: int = 32,
+        use_spatial_attention: bool = True,
+        spatial_kernel_size: int = 11,
+        dropout_rate: float = 0.15,
+        num_classes: int = 1,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.encoder_variant = encoder_variant
+        self.pyramid_channels = list(pyramid_channels)
+        self.pyramid_dim = pyramid_dim
+        self.use_spatial_attention = use_spatial_attention
+        self.spatial_kernel_size = spatial_kernel_size
+        self.dropout_rate = dropout_rate
+        self.num_classes = num_classes