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""" Bros model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class BrosConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`BrosModel`] or a [`TFBrosModel`]. It is used to
    instantiate a Bros model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the Bros
    [jinho8345/bros-base-uncased](https://huggingface.co/jinho8345/bros-base-uncased) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 30522):
            Vocabulary size of the Bros model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`BrosModel`] or [`TFBrosModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`BrosModel`] or [`TFBrosModel`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        pad_token_id (`int`, *optional*, defaults to 0):
            The index of the padding token in the token vocabulary.
        dim_bbox (`int`, *optional*, defaults to 8):
            The dimension of the bounding box coordinates. (x0, y1, x1, y0, x1, y1, x0, y1)
        bbox_scale (`float`, *optional*, defaults to 100.0):
            The scale factor of the bounding box coordinates.
        n_relations (`int`, *optional*, defaults to 1):
            The number of relations for SpadeEE(entity extraction), SpadeEL(entity linking) head.
        classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the classifier head.


    Examples:

    ```python
    >>> from transformers import BrosConfig, BrosModel

    >>> # Initializing a BROS jinho8345/bros-base-uncased style configuration
    >>> configuration = BrosConfig()

    >>> # Initializing a model from the jinho8345/bros-base-uncased style configuration
    >>> model = BrosModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "bros"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=0,
        dim_bbox=8,
        bbox_scale=100.0,
        n_relations=1,
        classifier_dropout_prob=0.1,
        **kwargs,
    ):
        super().__init__(
            vocab_size=vocab_size,
            hidden_size=hidden_size,
            num_hidden_layers=num_hidden_layers,
            num_attention_heads=num_attention_heads,
            intermediate_size=intermediate_size,
            hidden_act=hidden_act,
            hidden_dropout_prob=hidden_dropout_prob,
            attention_probs_dropout_prob=attention_probs_dropout_prob,
            max_position_embeddings=max_position_embeddings,
            type_vocab_size=type_vocab_size,
            initializer_range=initializer_range,
            layer_norm_eps=layer_norm_eps,
            pad_token_id=pad_token_id,
            **kwargs,
        )

        self.dim_bbox = dim_bbox
        self.bbox_scale = bbox_scale
        self.n_relations = n_relations
        self.dim_bbox_sinusoid_emb_2d = self.hidden_size // 4
        self.dim_bbox_sinusoid_emb_1d = self.dim_bbox_sinusoid_emb_2d // self.dim_bbox
        self.dim_bbox_projection = self.hidden_size // self.num_attention_heads
        self.classifier_dropout_prob = classifier_dropout_prob
