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# coding=utf-8
# Copyright 2023-present NAVER Corp, The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""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
__all__ = ["BrosConfig"]