"""Configuration for Cactus-Compute/gemma-4-e2b-it-hybrid.

`Gemma4E2BItHybridConfig` is the stock Gemma-4 text config plus the hyperparameters of
the handoff probe that scores every generation with ``confidence = 1 - p_wrong``.
Everything the base model needs is inherited unchanged, so the base weights load
with identical keys.
"""

from transformers.models.gemma4.configuration_gemma4 import Gemma4TextConfig


class Gemma4E2BItHybridConfig(Gemma4TextConfig):
    """Gemma-4 text config extended with handoff-probe hyperparameters.

    Extra fields (all serialized to ``config.json``):

    - ``probe_layer`` (`int`, defaults to 28): zero-indexed decoder layer whose
      output feeds the probe. Must be ``< num_hidden_layers``.
    - ``probe_feature_size`` (`int`, defaults to 1536): width of the captured
      hidden states; must equal ``hidden_size``.
    - ``probe_max_tokens`` (`int`, defaults to 1024): the probe scores at most
      the first ``probe_max_tokens`` generated-token rows.
    - ``probe_proj_dim`` (`int`, defaults to 32): width of the probe's attention
      projection (fixed by the released checkpoint).
    """

    model_type = "gemma-4-e2b-it-hybrid"

    probe_layer: int = 28
    probe_feature_size: int = 1536
    probe_max_tokens: int = 1024
    probe_proj_dim: int = 32

    def __post_init__(self, **kwargs):
        super().__post_init__(**kwargs)
        if not 0 <= self.probe_layer < self.num_hidden_layers:
            raise ValueError(
                f"probe_layer={self.probe_layer} must be in [0, num_hidden_layers="
                f"{self.num_hidden_layers})"
            )
        # Note: probe_feature_size == hidden_size is enforced by the model, not
        # here — `to_diff_dict()` default-constructs the config class, so this
        # class must stay constructible with pure defaults.


__all__ = ["Gemma4E2BItHybridConfig"]