from transformers import PretrainedConfig


class ResnetConfig(PretrainedConfig):
    model_type = "custom_resnet50d"

    def __init__(
        self,
        block_type="bottleneck",
        layers: list[int] = [3, 4, 6, 3],
        num_classes: int = 1000,
        input_channels: int = 3,
        cardinality: int = 1,
        base_width: int = 64,
        stem_width: int = 64,
        stem_type: str = "",
        avg_down: bool = False,
        **kwargs,
    ):
        if block_type not in ["basic", "bottleneck"]:
            raise ValueError(
                f"block_type should be either 'basic' or 'bottleneck', but is {block_type}"
            )
        if stem_type not in ["", "deep", "deep-tiered"]:
            raise ValueError(
                f"stem_type should be either '', 'deep' or 'deep-tiered', but is {stem_type}"
            )

        self.block_type = block_type
        self.layers = layers
        self.num_classes = num_classes
        self.input_channels = input_channels
        self.cardinality = cardinality
        self.base_width = base_width
        self.stem_width = stem_width
        self.stem_type = stem_type
        self.avg_down = avg_down
        super().__init__(**kwargs)


__all__ = [
    "ResnetConfig",
]

if __name__ == "__main__":
    resnet50d_config = ResnetConfig(
        block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True
    )
    print(resnet50d_config)
    resnet50d_config.save_pretrained("resnet50d_config")