# 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`]. 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`]. 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`]. 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. is_decoder (`bool`, *optional*, defaults to `False`): Whether to only use the decoder in an encoder-decoder architecture, otherwise it has no effect on decoder-only or encoder-only architectures. add_cross_attention (`bool`, *optional*, defaults to `False`): Whether cross-attention layers should be added to the model. 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, is_decoder=False, add_cross_attention=False, **kwargs, ): super().__init__(**kwargs) self.is_decoder = is_decoder self.add_cross_attention = add_cross_attention self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.pad_token_id = pad_token_id 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"]