"""PatchSVAE configuration for HuggingFace AutoModel."""

from transformers import PretrainedConfig


class PatchSVAEConfig(PretrainedConfig):
    """Configuration for PatchSVAE — Fresnel geometric compression lens.

    Args:
        matrix_v: Number of rows per patch matrix (vocabulary size)
        D: Embedding dimension (number of singular values per patch)
        patch_size: Spatial patch size in pixels
        hidden: MLP hidden width for encoder/decoder
        depth: Number of residual blocks in encoder and decoder
        n_cross_layers: Number of spectral cross-attention layers
        max_alpha: Maximum coordination strength per spectral mode
        alpha_init: Initial alpha logit (sigmoid(alpha_init) * max_alpha)
        target_cv: Soft hand CV target for training
        image_size: Expected input image size (H=W)
    """
    model_type = "patchsvae"

    def __init__(
        self,
        matrix_v=256,
        D=16,
        patch_size=16,
        hidden=768,
        depth=4,
        n_cross_layers=2,
        max_alpha=0.2,
        alpha_init=-2.0,
        target_cv=0.125,
        image_size=128,
        **kwargs,
    ):
        self.matrix_v = matrix_v
        self.D = D
        self.patch_size = patch_size
        self.hidden = hidden
        self.depth = depth
        self.n_cross_layers = n_cross_layers
        self.max_alpha = max_alpha
        self.alpha_init = alpha_init
        self.target_cv = target_cv
        self.image_size = image_size

        # Derived properties
        self.n_patches = (image_size // patch_size) ** 2
        self.patch_dim = 3 * patch_size * patch_size
        self.mat_dim = matrix_v * D
        self.latent_channels = D
        self.latent_size = image_size // patch_size

        super().__init__(**kwargs)