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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.
	"""Hiera model configuration"""

	from ...configuration_utils import PretrainedConfig
	from ...utils import logging
	from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


	logger = logging.get_logger(__name__)


	class HieraConfig(BackboneConfigMixin, 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
	[facebook/hiera-base-224](https://huggingface.co/facebook/hiera-base-224) architecture.

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

	Args:
	embed_dim (`int`, optional, defaults to 96):
	Dimensionality of patch embedding.
	image_size (`list(int)`, optional, defaults to `[224, 224]`):
	The size (resolution) of input in the format (height, width) for images
	and (frames, height, width) for videos.
	patch_size (`list(int)`, optional, defaults to `[7, 7]`):
	The size (resolution) of each patch.
	patch_stride (`list(int)`, optional, defaults to `[4, 4]`):
	The stride of the patch.
	patch_padding (`list(int)`, optional, defaults to `[3, 3]`):
	The padding of the patch.
	mlp_ratio (`float`, optional, defaults to 4.0):
	The ratio of mlp hidden dim to embedding dim.
	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.
	embed_dim_multiplier (`float`, optional, defaults to 2.0):
	The multiplier to the dimensionality of patch embedding in each layer of the Transformer encoder.
	num_query_pool (`int`, optional, defaults to 3):
	The number of query pool stages.
	query_stride (`list(int)`, optional, defaults to `[2, 2]`):
	The stride of the query pool.
	masked_unit_size (`list(int)`, optional, defaults to `[8, 8]`):
	The size of the masked unit.
	masked_unit_attention (`list(bool)`, optional, defaults to `[True, True, False, False]`):
	Whether to use masked unit attention in each layer of the Transformer encoder.
	drop_path_rate (`float`, optional, defaults to 0.0):
	The drop path rate.
	num_channels (`int`, optional, defaults to 3):
	The number of input channels.
	hidden_act (`str`, 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.
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices and
	the zero_initializer for initializing all bias vectors.
	layer_norm_init (`float`, optional, defaults to 1.0):
	The initial weight value for layer normalization layers.
	layer_norm_eps (`float`, optional, defaults to 1e-06):
	The epsilon used by the layer normalization layers.
	decoder_hidden_size (`int`, optional):
	Dimensionality of decoder embeddings for MAE pretraining.
	decoder_depth (`int`, optional):
	Depth of the decoder for MAE pretraining.
	decoder_num_heads (`int`, optional):
	Number of attention heads in each layer of the decoder for MAE pretraining.
	normalize_pixel_loss (`bool`, optional, defaults to `True`):
	Whether to normalize the pixel loss by the number of pixels.
	mask_ratio (`float`, optional, defaults to 0.6):
	The ratio of masked tokens in the input.
	out_features (`List[str]`, optional):
	If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
	(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
	corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
	same order as defined in the `stage_names` attribute.
	out_indices (`List[int]`, optional):
	If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
	many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
	If unset and `out_features` is unset, will default to the last stage. Must be in the
	same order as defined in the `stage_names` attribute.


	Example:

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

	>>> # Initializing a Hiera hiera-base-patch16-224 style configuration
	>>> configuration = HieraConfig()

	>>> # Initializing a model (with random weights) from the hiera-base-patch16-224 style configuration
	>>> model = HieraModel(configuration)

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

	model_type = "hiera"

	attribute_map = {"num_hidden_layers": "num_layers"}

	def __init__(
	self,
	embed_dim=96,
	image_size=[224, 224],
	patch_size=[7, 7],
	patch_stride=[4, 4],
	patch_padding=[3, 3],
	mlp_ratio=4.0,
	depths=[2, 3, 16, 3],
	num_heads=[1, 2, 4, 8],
	embed_dim_multiplier=2.0,
	num_query_pool=3,
	query_stride=[2, 2],
	masked_unit_size=[8, 8],
	masked_unit_attention=[True, True, False, False],
	drop_path_rate=0.0,
	num_channels=3,
	hidden_act="gelu",
	initializer_range=0.02,
	layer_norm_init=1.0,
	layer_norm_eps=1e-6,
	decoder_hidden_size=None,
	decoder_depth=None,
	decoder_num_heads=None,
	normalize_pixel_loss=True,
	mask_ratio=0.6,
	out_features=None,
	out_indices=None,
	**kwargs,
	):
	super().__init__(**kwargs)
	if masked_unit_size[0] % query_stride[0] ** (len(depths) - 1) != 0:
	raise ValueError(
	f"masked_unit_size[0] ({masked_unit_size[0]}) must be divisible by query_stride[0] ({query_stride[0]}) "
	f"raised to the power of the number of layers ({len(depths) - 1})"
	)

	if num_query_pool >= len(depths):
	raise ValueError(
	f"num_query_pool ({num_query_pool}) must be less than the number of layers ({len(depths)})"
	)

	self.embed_dim = embed_dim
	self.image_size = image_size
	self.patch_size = patch_size
	self.patch_stride = patch_stride
	self.patch_padding = patch_padding
	self.mlp_ratio = mlp_ratio
	self.depths = depths
	self.num_heads = num_heads
	self.num_layers = len(depths)
	self.embed_dim_multiplier = embed_dim_multiplier
	self.num_query_pool = num_query_pool
	self.query_stride = query_stride
	self.masked_unit_size = masked_unit_size
	self.masked_unit_attention = masked_unit_attention
	self.drop_path_rate = drop_path_rate
	self.num_channels = num_channels
	self.hidden_act = hidden_act
	self.initializer_range = initializer_range
	self.layer_norm_init = layer_norm_init
	self.layer_norm_eps = layer_norm_eps
	self.decoder_hidden_size = decoder_hidden_size
	self.decoder_depth = decoder_depth
	self.decoder_num_heads = decoder_num_heads
	self.normalize_pixel_loss = normalize_pixel_loss
	self.mask_ratio = mask_ratio
	# we set the hidden_size attribute in order to make Hiera work with VisionEncoderDecoderModel
	# this indicates the channel dimension after the last stage of the model
	self.hidden_size = int(embed_dim * embed_dim_multiplier ** (len(depths) - 1))
	self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
	self._out_features, self._out_indices = get_aligned_output_features_output_indices(
	out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
	)