src/transformers/models/oneformer/configuration_oneformer.py/OneFormerConfig/__init__
class OneFormerConfig:    def __init__(
        self,
        backbone_config: Optional[Dict] = None,
        ignore_value: int = 255,
        num_queries: int = 150,
        no_object_weight: int = 0.1,
        class_weight: float = 2.0,
        mask_weight: float = 5.0,
        dice_weight: float = 5.0,
        contrastive_weight: float = 0.5,
        contrastive_temperature: float = 0.07,
        train_num_points: int = 12544,
        oversample_ratio: float = 3.0,
        importance_sample_ratio: float = 0.75,
        init_std: float = 0.02,
        init_xavier_std: float = 1.0,
        layer_norm_eps: float = 1e-05,
        is_training: bool = False,
        use_auxiliary_loss: bool = True,
        output_auxiliary_logits: bool = True,
        strides: Optional[list] = [4, 8, 16, 32],
        task_seq_len: int = 77,
        text_encoder_width: int = 256,
        text_encoder_context_length: int = 77,
        text_encoder_num_layers: int = 6,
        text_encoder_vocab_size: int = 49408,
        text_encoder_proj_layers: int = 2,
        text_encoder_n_ctx: int = 16,
        conv_dim: int = 256,
        mask_dim: int = 256,
        hidden_dim: int = 256,
        encoder_feedforward_dim: int = 1024,
        norm: str = "GN",
        encoder_layers: int = 6,
        decoder_layers: int = 10,
        use_task_norm: bool = True,
        num_attention_heads: int = 8,
        dropout: float = 0.1,
        dim_feedforward: int = 2048,
        pre_norm: bool = False,
        enforce_input_proj: bool = False,
        query_dec_layers: int = 2,
        common_stride: int = 4,
        **kwargs,
    ):
        if backbone_config is None:
            logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone.")
            backbone_config = CONFIG_MAPPING["swin"](
                image_size=224,
                in_channels=3,
                patch_size=4,
                embed_dim=96,
                depths=[2, 2, 6, 2],
                num_heads=[3, 6, 12, 24],
                window_size=7,
                drop_path_rate=0.3,
                use_absolute_embeddings=False,
                out_features=["stage1", "stage2", "stage3", "stage4"],
            )
        elif isinstance(backbone_config, dict):
            backbone_model_type = backbone_config.get("model_type")
            config_class = CONFIG_MAPPING[backbone_model_type]
            backbone_config = config_class.from_dict(backbone_config)

        self.backbone_config = backbone_config

        self.ignore_value = ignore_value
        self.num_queries = num_queries
        self.no_object_weight = no_object_weight
        self.class_weight = class_weight
        self.mask_weight = mask_weight
        self.dice_weight = dice_weight
        self.contrastive_weight = contrastive_weight
        self.contrastive_temperature = contrastive_temperature
        self.train_num_points = train_num_points
        self.oversample_ratio = oversample_ratio
        self.importance_sample_ratio = importance_sample_ratio
        self.init_std = init_std
        self.init_xavier_std = init_xavier_std
        self.layer_norm_eps = layer_norm_eps
        self.is_training = is_training
        self.use_auxiliary_loss = use_auxiliary_loss
        self.output_auxiliary_logits = output_auxiliary_logits
        self.strides = strides
        self.task_seq_len = task_seq_len
        self.text_encoder_width = text_encoder_width
        self.text_encoder_context_length = text_encoder_context_length
        self.text_encoder_num_layers = text_encoder_num_layers
        self.text_encoder_vocab_size = text_encoder_vocab_size
        self.text_encoder_proj_layers = text_encoder_proj_layers
        self.text_encoder_n_ctx = text_encoder_n_ctx
        self.conv_dim = conv_dim
        self.mask_dim = mask_dim
        self.hidden_dim = hidden_dim
        self.encoder_feedforward_dim = encoder_feedforward_dim
        self.norm = norm
        self.encoder_layers = encoder_layers
        self.decoder_layers = decoder_layers
        self.use_task_norm = use_task_norm
        self.num_attention_heads = num_attention_heads
        self.dropout = dropout
        self.dim_feedforward = dim_feedforward
        self.pre_norm = pre_norm
        self.enforce_input_proj = enforce_input_proj
        self.query_dec_layers = query_dec_layers
        self.common_stride = common_stride
        self.num_hidden_layers = decoder_layers

        super().__init__(**kwargs)