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	# coding=utf-8
	# Copyright 2024 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.
	"""SegGpt model configuration"""

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


	logger = logging.get_logger(__name__)


	class SegGptConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`SegGptModel`]. It is used to instantiate a SegGPT
	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 SegGPT
	[BAAI/seggpt-vit-large](https://huggingface.co/BAAI/seggpt-vit-large) architecture.

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

	Args:
	hidden_size (`int`, optional, defaults to 1024):
	Dimensionality of the encoder layers and the pooler layer.
	num_hidden_layers (`int`, optional, defaults to 24):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 16):
	Number of attention heads for each attention 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.
	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-06):
	The epsilon used by the layer normalization layers.
	image_size (`List[int]`, optional, defaults to `[896, 448]`):
	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.
	qkv_bias (`bool`, optional, defaults to `True`):
	Whether to add a bias to the queries, keys and values.
	mlp_dim (`int`, optional):
	The dimensionality of the MLP layer in the Transformer encoder. If unset, defaults to
	`hidden_size` * 4.
	drop_path_rate (`float`, optional, defaults to 0.1):
	The drop path rate for the dropout layers.
	pretrain_image_size (`int`, optional, defaults to 224):
	The pretrained size of the absolute position embeddings.
	decoder_hidden_size (`int`, optional, defaults to 64):
	Hidden size for decoder.
	use_relative_position_embeddings (`bool`, optional, defaults to `True`):
	Whether to use relative position embeddings in the attention layers.
	merge_index (`int`, optional, defaults to 2):
	The index of the encoder layer to merge the embeddings.
	intermediate_hidden_state_indices (`List[int]`, optional, defaults to `[5, 11, 17, 23]`):
	The indices of the encoder layers which we store as features for the decoder.
	beta (`float`, optional, defaults to 0.01):
	Regularization factor for SegGptLoss (smooth-l1 loss).

	Example:

	```python
	>>> from transformers import SegGptConfig, SegGptModel

	>>> # Initializing a SegGPT seggpt-vit-large style configuration
	>>> configuration = SegGptConfig()

	>>> # Initializing a model (with random weights) from the seggpt-vit-large style configuration
	>>> model = SegGptModel(configuration)

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

	model_type = "seggpt"

	def __init__(
	self,
	hidden_size=1024,
	num_hidden_layers=24,
	num_attention_heads=16,
	hidden_act="gelu",
	hidden_dropout_prob=0.0,
	initializer_range=0.02,
	layer_norm_eps=1e-6,
	image_size=[896, 448],
	patch_size=16,
	num_channels=3,
	qkv_bias=True,
	mlp_dim=None,
	drop_path_rate=0.1,
	pretrain_image_size=224,
	decoder_hidden_size=64,
	use_relative_position_embeddings=True,
	merge_index=2,
	intermediate_hidden_state_indices=[5, 11, 17, 23],
	beta=0.01,
	**kwargs,
	):
	super().__init__(**kwargs)

	if merge_index > min(intermediate_hidden_state_indices):
	raise ValueError(
	f"Merge index must be less than the minimum encoder output index, but got {merge_index=} and {intermediate_hidden_state_indices=}"
	)
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.hidden_act = hidden_act
	self.hidden_dropout_prob = hidden_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.drop_path_rate = drop_path_rate
	self.pretrain_image_size = pretrain_image_size
	self.decoder_hidden_size = decoder_hidden_size
	self.use_relative_position_embeddings = use_relative_position_embeddings
	self.merge_index = merge_index
	self.intermediate_hidden_state_indices = intermediate_hidden_state_indices
	self.beta = beta
	self.mlp_dim = int(hidden_size * 4) if mlp_dim is None else mlp_dim