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	""" Hiera model configuration"""

	import math

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

	logger = logging.get_logger(__name__)

	# HIERA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	# "hoge/hoge": ("/config.json"),
	# }


	class HieraConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`HieraModel`]. It is used to instantiate a Hiera
	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 Hiera
	[/]()
	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 (`list(int)`, optional, defaults to [7, 7]):
	The size (resolution) of each patch.
	stride_size (`list(int)`, optional, defaults to [4, 4]):
	The size (resolution) of each stride.
	padding_size (`list(int)`, optional, defaults to [3, 3]):
	The size (resolution) of each padding.
	num_channels (`int`, optional, defaults to 3):
	The number of input channels.
	embed_dim (`int`, optional, defaults to 96):
	Dimensionality of patch embedding.
	depths (`list(int)`, optional, defaults to `[2, 3, 16, 3]`):
	Depth of each layer in the Transformer encoder.
	num_heads (`list(int)`, optional, defaults to `[1, 2, 4, 8]`):
	Number of attention heads in each layer of the Transformer encoder.
	q_pool (`int`, optional, defaults to 3):
	Number of q_pool stages.
	q_stride (`list(int)`, optional, defaults to [2, 2]):
	Size of stride of q_pool,
	mask_unit_size (`list(int)`, optional, defaults to [8, 8]):
	Size of mask unit in attention.
	mask_unit_attention (`list(bool)`, optional, defaults to [True, True, False, False]):
	Whether or not to enable mask unit attention in each stage.
	separate_positional_embeds (`bool`, optional, defaults to False):
	Whether or not to use separeted positional embeddings.
	mlp_ratio (`float`, optional, defaults to 4.0):
	Ratio of MLP hidden dimensionality to embedding dimensionality.
	drop_path_rate (`float`, optional, defaults to 0.1):
	Stochastic depth rate.
	hidden_act (`str` or `function`, optional, defaults to `"gelu"`):
	The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
	`"selu"` and `"gelu_new"` are supported.
	layer_norm_eps (`float`, optional, defaults to 1e-05):
	The epsilon used by the layer normalization layers.
	hidden_dropout_prob (`float`, optional, defaults to 0.0):
	The dropout probability for all fully connected layers in the embeddings and encoder.
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	initializer_bias (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all bias matrices.

	Example:

	```python
	>>> from transformers import HieraConfig, HieraModel

	>>> # Initializing a Hiera / style configuration
	>>> configuration = HieraConfig()

	>>> # Initializing a model (with random weights) from the / style configuration
	>>> model = HieraModel(configuration)

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

	model_type = "hiera"

	attribute_map = {}

	def __init__(
	self,
	image_size=224,
	patch_size=[7, 7],
	stride_size=[4, 4],
	padding_size=[3, 3],
	num_channels=3,
	embed_dim=96,
	depths=[2, 3, 16, 3],
	num_heads=[1, 2, 4, 8],
	q_pool=3, # number of q_pool stages
	q_stride=[2, 2],
	mask_unit_size=[8, 8],
	mask_unit_attention=[True, True, False, False],
	separate_positional_embeds=False,
	mlp_ratio=4.0,
	drop_path_rate=0.0,
	hidden_act="gelu",
	layer_norm_eps=1e-6,
	hidden_dropout_prob=0.0,
	initializer_range=0.02,
	initializer_bias=0.02,
	**kwargs,
	):
	super().__init__(**kwargs)

	self.image_size = image_size
	self.patch_size = patch_size
	self.stride_size = stride_size
	self.padding_size = padding_size
	self.num_channels = num_channels
	self.embed_dim = embed_dim
	self.depths = depths
	self.num_layers = len(depths)
	self.num_heads = num_heads
	self.mlp_ratio = mlp_ratio
	self.hidden_dropout_prob = hidden_dropout_prob
	self.drop_path_rate = drop_path_rate
	self.hidden_act = hidden_act
	self.layer_norm_eps = layer_norm_eps

	assert q_pool < len(depths), "q_pool must be less than depths"

	self.mask_unit_size = mask_unit_size
	self.flat_mask_unit_size = int(math.prod(mask_unit_size))
	self.mask_unit_attention = mask_unit_attention
	self.q_pool = q_pool
	self.q_stride = q_stride
	self.flat_q_stride = int(math.prod(q_stride))
	self.separate_positional_embeds = separate_positional_embeds

	self.initializer_range = initializer_range
	self.initializer_bias = initializer_bias