Delete config.py

config.py

@@ -1,68 +0,0 @@
-from dataclasses import dataclass
-from typing import Tuple
-
-from transformers import PretrainedConfig
-
-
-@dataclass
-class sCTConfig(PretrainedConfig):  # noqa: N801
-    model_type = "sCT"
-
-    def __init__(self, **kwargs):  # type: ignore
-        self.alphabet_size = kwargs.get("alphabet_size", 7)
-        self.pad_token_id = kwargs.get("pad_token_id", 5)
-        self.mask_token_id = kwargs.get("mask_token_id", 6)
-        self.cell_len = kwargs.get("cell_len", 19968)
-
-        self.num_downsamples = kwargs.get("num_downsamples", 8)
-        self.attention_heads = kwargs.get("attention_heads", 16)
-        self.key_size = kwargs.get("key_size", None)
-        self.token_embed_dim = kwargs.get("token_embed_dim", 16)
-
-        self.embed_dim = kwargs.get("embed_dim", 1024)
-        self.ffn_embed_dim = kwargs.get("ffn_embed_dim", 2048)
-        self.num_layers = kwargs.get("num_layers", 4)
-        self.layer_norm_eps = kwargs.get("layer_norm_eps", 1e-5)
-        self.interpolation_method = kwargs.get("interpolation_method", "nearest")
-
-        # bad hack to satisfy cellnt_celltype_annotation.py:312
-        self.max_positions: int = kwargs.get("max_positions", 20480)
-        self.num_cells: int = kwargs.get("num_cells", 50)
-        self.num_hidden_layers_head: int = kwargs.get("num_hidden_layers_head", 1)
-
-        self.use_skip_connection: bool = kwargs.get("use_skip_connection", True)
-
-        # logging
-        self.use_gradient_checkpointing: bool = False
-
-        # return
-        self.embeddings_layers_to_save: Tuple[int, ...] = kwargs.get(
-            "embeddings_layers_to_save", ()
-        )
-        self.attention_maps_to_save: list[tuple[int, int]] = kwargs.get(
-            "attention_maps_to_save", []
-        )
-
-        # Spatial info configuration
-        self.use_spatial_information: bool = kwargs.get(
-            "use_spatial_information", False
-        )
-        self.num_scales: int = kwargs.get("num_scales", 10)
-        self.sigma_min: float = kwargs.get("sigma_min", 1.0)
-        self.sigma_max: float = kwargs.get("sigma_max", 10.0)
-
-        super().__init__(**kwargs)
-
-        def __post_init__(self) -> None:  # type: ignore # noqa: N807
-            """
-            Checks that the given values are compatible.
-            """
-            if self.key_size is None:
-                if not self.embed_dim % self.attention_heads == 0:
-                    raise ValueError(
-                        f"When no key size is provided, the embedding dimension"
-                        f"should be divisible by the number of heads, however "
-                        f"provided embedding dimension is {self.embed_dim} and "
-                        f"the number of heads is {self.attention_heads}."
-                    )
-                self.key_size = self.embed_dim // self.attention_heads