from transformers import PretrainedConfig
from typing import List


class CetaceanClassifierConfig(PretrainedConfig):
    model_type = "cetaceanet"

    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` must be 'basic' or bottleneck', got {block_type}.")
        # if stem_type not in ["", "deep", "deep-tiered"]:
        #     raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {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)