195 lines
9.1 KiB
Python
195 lines
9.1 KiB
Python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Hiera model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
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logger = logging.get_logger(__name__)
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class HieraConfig(BackboneConfigMixin, PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`HieraModel`]. It is used to instantiate a Hiera
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Hiera
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[facebook/hiera-base-224](https://huggingface.co/facebook/hiera-base-224) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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embed_dim (`int`, *optional*, defaults to 96):
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Dimensionality of patch embedding.
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image_size (`list(int)`, *optional*, defaults to `[224, 224]`):
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The size (resolution) of input in the format (height, width) for images
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and (frames, height, width) for videos.
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patch_size (`list(int)`, *optional*, defaults to `[7, 7]`):
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The size (resolution) of each patch.
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patch_stride (`list(int)`, *optional*, defaults to `[4, 4]`):
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The stride of the patch.
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patch_padding (`list(int)`, *optional*, defaults to `[3, 3]`):
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The padding of the patch.
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mlp_ratio (`float`, *optional*, defaults to 4.0):
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The ratio of mlp hidden dim to embedding dim.
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depths (`list(int)`, *optional*, defaults to `[2, 3, 16, 3]`):
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Depth of each layer in the Transformer encoder.
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num_heads (`list(int)`, *optional*, defaults to `[1, 2, 4, 8]`):
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Number of attention heads in each layer of the Transformer encoder.
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embed_dim_multiplier (`float`, *optional*, defaults to 2.0):
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The multiplier to the dimensionality of patch embedding in each layer of the Transformer encoder.
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num_query_pool (`int`, *optional*, defaults to 3):
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The number of query pool stages.
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query_stride (`list(int)`, *optional*, defaults to `[2, 2]`):
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The stride of the query pool.
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masked_unit_size (`list(int)`, *optional*, defaults to `[8, 8]`):
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The size of the masked unit.
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masked_unit_attention (`list(bool)`, *optional*, defaults to `[True, True, False, False]`):
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Whether to use masked unit attention in each layer of the Transformer encoder.
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drop_path_rate (`float`, *optional*, defaults to 0.0):
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The drop path rate.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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hidden_act (`str`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
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`"selu"` and `"gelu_new"` are supported.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices and
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the zero_initializer for initializing all bias vectors.
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layer_norm_init (`float`, *optional*, defaults to 1.0):
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The initial weight value for layer normalization layers.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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decoder_hidden_size (`int`, *optional*):
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Dimensionality of decoder embeddings for MAE pretraining.
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decoder_depth (`int`, *optional*):
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Depth of the decoder for MAE pretraining.
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decoder_num_heads (`int`, *optional*):
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Number of attention heads in each layer of the decoder for MAE pretraining.
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normalize_pixel_loss (`bool`, *optional*, defaults to `True`):
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Whether to normalize the pixel loss by the number of pixels.
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mask_ratio (`float`, *optional*, defaults to 0.6):
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The ratio of masked tokens in the input.
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out_features (`list[str]`, *optional*):
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If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
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(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
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corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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out_indices (`list[int]`, *optional*):
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If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
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many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
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If unset and `out_features` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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Example:
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```python
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>>> from transformers import HieraConfig, HieraModel
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>>> # Initializing a Hiera hiera-base-patch16-224 style configuration
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>>> configuration = HieraConfig()
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>>> # Initializing a model (with random weights) from the hiera-base-patch16-224 style configuration
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>>> model = HieraModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "hiera"
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attribute_map = {"num_hidden_layers": "num_layers"}
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def __init__(
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self,
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embed_dim=96,
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image_size=[224, 224],
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patch_size=[7, 7],
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patch_stride=[4, 4],
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patch_padding=[3, 3],
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mlp_ratio=4.0,
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depths=[2, 3, 16, 3],
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num_heads=[1, 2, 4, 8],
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embed_dim_multiplier=2.0,
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num_query_pool=3,
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query_stride=[2, 2],
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masked_unit_size=[8, 8],
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masked_unit_attention=[True, True, False, False],
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drop_path_rate=0.0,
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num_channels=3,
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hidden_act="gelu",
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initializer_range=0.02,
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layer_norm_init=1.0,
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layer_norm_eps=1e-6,
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decoder_hidden_size=None,
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decoder_depth=None,
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decoder_num_heads=None,
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normalize_pixel_loss=True,
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mask_ratio=0.6,
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out_features=None,
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out_indices=None,
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**kwargs,
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):
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super().__init__(**kwargs)
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if masked_unit_size[0] % query_stride[0] ** (len(depths) - 1) != 0:
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raise ValueError(
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f"masked_unit_size[0] ({masked_unit_size[0]}) must be divisible by query_stride[0] ({query_stride[0]}) "
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f"raised to the power of the number of layers ({len(depths) - 1})"
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)
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if num_query_pool >= len(depths):
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raise ValueError(
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f"num_query_pool ({num_query_pool}) must be less than the number of layers ({len(depths)})"
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)
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self.embed_dim = embed_dim
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self.image_size = image_size
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self.patch_size = patch_size
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self.patch_stride = patch_stride
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self.patch_padding = patch_padding
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self.mlp_ratio = mlp_ratio
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self.depths = depths
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self.num_heads = num_heads
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self.num_layers = len(depths)
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self.embed_dim_multiplier = embed_dim_multiplier
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self.num_query_pool = num_query_pool
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self.query_stride = query_stride
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self.masked_unit_size = masked_unit_size
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self.masked_unit_attention = masked_unit_attention
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self.drop_path_rate = drop_path_rate
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self.num_channels = num_channels
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.layer_norm_init = layer_norm_init
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self.layer_norm_eps = layer_norm_eps
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self.decoder_hidden_size = decoder_hidden_size
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self.decoder_depth = decoder_depth
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self.decoder_num_heads = decoder_num_heads
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self.normalize_pixel_loss = normalize_pixel_loss
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self.mask_ratio = mask_ratio
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# we set the hidden_size attribute in order to make Hiera work with VisionEncoderDecoderModel
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# this indicates the channel dimension after the last stage of the model
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self.hidden_size = int(embed_dim * embed_dim_multiplier ** (len(depths) - 1))
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self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
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self._out_features, self._out_indices = get_aligned_output_features_output_indices(
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out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
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)
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__all__ = ["HieraConfig"]
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