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	# 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