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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.
	"""VideoMAE model configuration"""

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


	logger = logging.get_logger(__name__)


	class VideoMAEConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`VideoMAEModel`]. It is used to instantiate a
	VideoMAE 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 VideoMAE
	[MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) architecture.

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

	Args:
	image_size (`int`, optional, defaults to 224):
	The size (resolution) of each image.
	patch_size (`int`, optional, defaults to 16):
	The size (resolution) of each patch.
	num_channels (`int`, optional, defaults to 3):
	The number of input channels.
	num_frames (`int`, optional, defaults to 16):
	The number of frames in each video.
	tubelet_size (`int`, optional, defaults to 2):
	The number of tubelets.
	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.
	qkv_bias (`bool`, optional, defaults to `True`):
	Whether to add a bias to the queries, keys and values.
	use_mean_pooling (`bool`, optional, defaults to `True`):
	Whether to mean pool the final hidden states instead of using the final hidden state of the [CLS] token.
	decoder_num_attention_heads (`int`, optional, defaults to 6):
	Number of attention heads for each attention layer in the decoder.
	decoder_hidden_size (`int`, optional, defaults to 384):
	Dimensionality of the decoder.
	decoder_num_hidden_layers (`int`, optional, defaults to 4):
	Number of hidden layers in the decoder.
	decoder_intermediate_size (`int`, optional, defaults to 1536):
	Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder.
	norm_pix_loss (`bool`, optional, defaults to `True`):
	Whether to normalize the target patch pixels.

	Example:

	```python
	>>> from transformers import VideoMAEConfig, VideoMAEModel

	>>> # Initializing a VideoMAE videomae-base style configuration
	>>> configuration = VideoMAEConfig()

	>>> # Randomly initializing a model from the configuration
	>>> model = VideoMAEModel(configuration)

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

	model_type = "videomae"

	def __init__(
	self,
	image_size=224,
	patch_size=16,
	num_channels=3,
	num_frames=16,
	tubelet_size=2,
	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,
	qkv_bias=True,
	use_mean_pooling=True,
	decoder_num_attention_heads=6,
	decoder_hidden_size=384,
	decoder_num_hidden_layers=4,
	decoder_intermediate_size=1536,
	norm_pix_loss=True,
	**kwargs,
	):
	super().__init__(**kwargs)

	self.image_size = image_size
	self.patch_size = patch_size
	self.num_channels = num_channels
	self.num_frames = num_frames
	self.tubelet_size = tubelet_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.initializer_range = initializer_range
	self.layer_norm_eps = layer_norm_eps
	self.qkv_bias = qkv_bias
	self.use_mean_pooling = use_mean_pooling

	self.decoder_num_attention_heads = decoder_num_attention_heads
	self.decoder_hidden_size = decoder_hidden_size
	self.decoder_num_hidden_layers = decoder_num_hidden_layers
	self.decoder_intermediate_size = decoder_intermediate_size
	self.norm_pix_loss = norm_pix_loss