


"""simple docstring"""




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__magic_name__	:   Dict          =							logging.get_logger(__name__)

__magic_name__	:   str          =							{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class  __snake_case      (lowerCamelCase     ):
							__a				   =					'''oneformer'''
							__a				   =					{'''hidden_size''': '''hidden_dim'''}




							def __init__(    self:						List[Any]    ,       A_:						Optional[Dict] = None    ,       A_:						int = 2_55    ,       A_:						int = 1_50    ,       A_:						int = 0.1    ,       A_:						float = 2.0    ,       A_:						float = 5.0    ,       A_:						float = 5.0    ,       A_:						float = 0.5    ,       A_:						float = 0.07    ,       A_:						int = 1_25_44    ,       A_:						float = 3.0    ,       A_:						float = 0.75    ,       A_:						float = 0.02    ,       A_:						float = 1.0    ,       A_:						float = 1E-05    ,       A_:						bool = False    ,       A_:						bool = True    ,       A_:						bool = True    ,       A_:						Optional[list] = [4, 8, 16, 32]    ,       A_:						int = 77    ,       A_:						int = 2_56    ,       A_:						int = 77    ,       A_:						int = 6    ,       A_:						int = 4_94_08    ,       A_:						int = 2    ,       A_:						int = 16    ,       A_:						int = 2_56    ,       A_:						int = 2_56    ,       A_:						int = 2_56    ,       A_:						int = 10_24    ,       A_:						str = "GN"    ,       A_:						int = 6    ,       A_:						int = 10    ,       A_:						bool = True    ,       A_:						int = 8    ,       A_:						float = 0.1    ,       A_:						int = 20_48    ,       A_:						bool = False    ,       A_:						bool = False    ,       A_:						int = 2    ,       A_:						int = 4    ,       **A_:						List[Any]    ,       ):
										if backbone_config is None:
													logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""      )
													__lowerCamelCase        =			CONFIG_MAPPING["""swin"""](
													    image_size=2_24    ,       in_channels=3    ,       patch_size=4    ,       embed_dim=96    ,       depths=[2, 2, 6, 2]    ,       num_heads=[3, 6, 12, 24]    ,       window_size=7    ,       drop_path_rate=0.3    ,       use_absolute_embeddings=A_    ,       out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,       )
										elif isinstance(A_    ,       A_      ):
													__lowerCamelCase        =			backbone_config.get("""model_type"""      )
													__lowerCamelCase        =			CONFIG_MAPPING[backbone_model_type]
													__lowerCamelCase        =			config_class.from_dict(A_      )

										__lowerCamelCase        =			backbone_config

										__lowerCamelCase        =			ignore_value
										__lowerCamelCase        =			num_queries
										__lowerCamelCase        =			no_object_weight
										__lowerCamelCase        =			class_weight
										__lowerCamelCase        =			mask_weight
										__lowerCamelCase        =			dice_weight
										__lowerCamelCase        =			contrastive_weight
										__lowerCamelCase        =			contrastive_temperature
										__lowerCamelCase        =			train_num_points
										__lowerCamelCase        =			oversample_ratio
										__lowerCamelCase        =			importance_sample_ratio
										__lowerCamelCase        =			init_std
										__lowerCamelCase        =			init_xavier_std
										__lowerCamelCase        =			layer_norm_eps
										__lowerCamelCase        =			is_training
										__lowerCamelCase        =			use_auxiliary_loss
										__lowerCamelCase        =			output_auxiliary_logits
										__lowerCamelCase        =			strides
										__lowerCamelCase        =			task_seq_len
										__lowerCamelCase        =			text_encoder_width
										__lowerCamelCase        =			text_encoder_context_length
										__lowerCamelCase        =			text_encoder_num_layers
										__lowerCamelCase        =			text_encoder_vocab_size
										__lowerCamelCase        =			text_encoder_proj_layers
										__lowerCamelCase        =			text_encoder_n_ctx
										__lowerCamelCase        =			conv_dim
										__lowerCamelCase        =			mask_dim
										__lowerCamelCase        =			hidden_dim
										__lowerCamelCase        =			encoder_feedforward_dim
										__lowerCamelCase        =			norm
										__lowerCamelCase        =			encoder_layers
										__lowerCamelCase        =			decoder_layers
										__lowerCamelCase        =			use_task_norm
										__lowerCamelCase        =			num_attention_heads
										__lowerCamelCase        =			dropout
										__lowerCamelCase        =			dim_feedforward
										__lowerCamelCase        =			pre_norm
										__lowerCamelCase        =			enforce_input_proj
										__lowerCamelCase        =			query_dec_layers
										__lowerCamelCase        =			common_stride
										__lowerCamelCase        =			decoder_layers

										super().__init__(**A_      )




							def      __a      (    self:						List[str]      ):
										__lowerCamelCase        =			copy.deepcopy(self.__dict__      )
										__lowerCamelCase        =			self.backbone_config.to_dict()
										__lowerCamelCase        =			self.__class__.model_type
										return output








"""simple docstring"""




from __future__ import annotations

import re





def 			a_   (  lowercase__   :list[str]    ):

			def alphanum_key(lowercase__   :int    ):
						return [int(lowercase__    ) if s.isdigit() else s.lower() for s in re.split("""([0-9]+)""", lowercase__    )]

			return sorted(lowercase__, key=lowercase__    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




