# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # 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. """VilT model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class ViltConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ViLTModel`]. It is used to instantiate an ViLT 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 ViLT [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) 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 text part of the model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ViltModel`]. type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`ViltModel`]. This is used when encoding text. modality_type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the modalities passed when calling [`ViltModel`]. This is used after concatening the embeddings of the text and image modalities. max_position_embeddings (`int`, *optional*, defaults to 40): The maximum sequence length that this model might ever be used with. 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" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. 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. image_size (`int`, *optional*, defaults to 384): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. max_image_length (`int`, *optional*, defaults to -1): The maximum number of patches to take as input for the Transformer encoder. If set to a positive integer, the encoder will sample `max_image_length` patches at maximum. If set to -1, will not be taken into account. num_images (`int`, *optional*, defaults to -1): The number of images to use for natural language visual reasoning. If set to a positive integer, will be used by [`ViltForImagesAndTextClassification`] for defining the classifier head. Example: ```python >>> from transformers import ViLTModel, ViLTConfig >>> # Initializing a ViLT dandelin/vilt-b32-mlm style configuration >>> configuration = ViLTConfig() >>> # Initializing a model from the dandelin/vilt-b32-mlm style configuration >>> model = ViLTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "vilt" def __init__( self, vocab_size=30522, type_vocab_size=2, modality_type_vocab_size=2, max_position_embeddings=40, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-12, image_size=384, patch_size=32, num_channels=3, qkv_bias=True, max_image_length=-1, tie_word_embeddings=False, num_images=-1, **kwargs, ): super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) self.vocab_size = vocab_size self.type_vocab_size = type_vocab_size self.modality_type_vocab_size = modality_type_vocab_size self.max_position_embeddings = max_position_embeddings 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.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.qkv_bias = qkv_bias self.max_image_length = max_image_length self.num_images = num_images __all__ = ["ViltConfig"]