"""MyResNet 모델 설정 클래스.

ResNet (He et al., 2015) 논문을 바탕으로 구현한 커스텀 모델의 설정입니다.
허깅페이스 PretrainedConfig를 상속받아 save_pretrained / from_pretrained 호환됩니다.
"""
from typing import List, Optional

from transformers import PretrainedConfig


class MyResNetConfig(PretrainedConfig):
    """
    MyResNet 모델의 하이퍼파라미터를 저장하는 Config 클래스.

    Args:
        num_channels (int): 입력 이미지 채널 수 (RGB=3, grayscale=1).
        num_labels (int): 분류할 클래스 개수.
        block_type (str): 'basic' (ResNet-18/34) 또는 'bottleneck' (ResNet-50/101/152).
        layers (List[int]): 각 stage(conv2_x ~ conv5_x)에 들어갈 블록 개수.
            - ResNet-18:  [2, 2, 2, 2]
            - ResNet-34:  [3, 4, 6, 3]
            - ResNet-50:  [3, 4, 6, 3] (bottleneck)
            - ResNet-101: [3, 4, 23, 3] (bottleneck)
            - ResNet-152: [3, 8, 36, 3] (bottleneck)
        hidden_sizes (List[int]): 각 stage의 기본 채널 수. 기본값 [64, 128, 256, 512].
        image_size (int): 입력 이미지 크기 (정사각형 기준).

    Example:
        >>> from configuration_myresnet import MyResNetConfig
        >>> config = MyResNetConfig(num_labels=10, layers=[2, 2, 2, 2])
    """

    model_type = "myresnet"

    def __init__(
        self,
        num_channels: int = 3,
        num_labels: int = 1000,
        block_type: str = "basic",
        layers: Optional[List[int]] = None,
        hidden_sizes: Optional[List[int]] = None,
        image_size: int = 224,
        **kwargs,
    ):
        super().__init__(num_labels=num_labels, **kwargs)
        self.num_channels = num_channels
        self.block_type = block_type
        self.layers = layers if layers is not None else [3, 4, 6, 3]
        self.hidden_sizes = hidden_sizes if hidden_sizes is not None else [64, 128, 256, 512]
        self.image_size = image_size

        # 검증
        if block_type not in ("basic", "bottleneck"):
            raise ValueError(f"block_type must be 'basic' or 'bottleneck', got {block_type}")
        if len(self.layers) != 4:
            raise ValueError(f"layers must have length 4, got {len(self.layers)}")
        if len(self.hidden_sizes) != 4:
            raise ValueError(f"hidden_sizes must have length 4, got {len(self.hidden_sizes)}")