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"""LiLT configuration"""

from huggingface_hub.dataclasses import strict

from ...configuration_utils import PreTrainedConfig
from ...utils import auto_docstring


@auto_docstring(checkpoint="SCUT-DLVCLab/lilt-roberta-en-base")
@strict
class LiltConfig(PreTrainedConfig):
    r"""
    channel_shrink_ratio (`int`, *optional*, defaults to 4):
        The shrink ratio compared to the `hidden_size` for the channel dimension of the layout embeddings.
    max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
        The maximum value that the 2D position embedding might ever be used with. Typically set this to something
        large just in case (e.g., 1024).

    Examples:

    ```python
    >>> from transformers import LiltConfig, LiltModel

    >>> # Initializing a LiLT SCUT-DLVCLab/lilt-roberta-en-base style configuration
    >>> configuration = LiltConfig()
    >>> # Randomly initializing a model from the SCUT-DLVCLab/lilt-roberta-en-base style configuration
    >>> model = LiltModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "lilt"

    vocab_size: int = 30522
    hidden_size: int = 768
    num_hidden_layers: int = 12
    num_attention_heads: int = 12
    intermediate_size: int = 3072
    hidden_act: str = "gelu"
    hidden_dropout_prob: float | int = 0.1
    attention_probs_dropout_prob: float | int = 0.1
    max_position_embeddings: int = 512
    type_vocab_size: int = 2
    initializer_range: float = 0.02
    layer_norm_eps: float = 1e-12
    pad_token_id: int | None = 0
    bos_token_id: int | None = None
    eos_token_id: int | list[int] | None = None
    classifier_dropout: float | int | None = None
    channel_shrink_ratio: int = 4
    max_2d_position_embeddings: int = 1024


__all__ = ["LiltConfig"]
