



'''simple docstring'''
import copy
from typing import Dict, Optional

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


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''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 _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Dict						      =			"oneformer"
		_SCREAMING_SNAKE_CASE					:    Dict						      =			{"hidden_size": "hidden_dim"}





		def __init__(     self ,       __UpperCAmelCase = None ,       __UpperCAmelCase = 255 ,       __UpperCAmelCase = 150 ,       __UpperCAmelCase = 0.1 ,       __UpperCAmelCase = 2.0 ,       __UpperCAmelCase = 5.0 ,       __UpperCAmelCase = 5.0 ,       __UpperCAmelCase = 0.5 ,       __UpperCAmelCase = 0.07 ,       __UpperCAmelCase = 12_544 ,       __UpperCAmelCase = 3.0 ,       __UpperCAmelCase = 0.75 ,       __UpperCAmelCase = 0.02 ,       __UpperCAmelCase = 1.0 ,       __UpperCAmelCase = 1E-05 ,       __UpperCAmelCase = False ,       __UpperCAmelCase = True ,       __UpperCAmelCase = True ,       __UpperCAmelCase = [4, 8, 16, 32] ,       __UpperCAmelCase = 77 ,       __UpperCAmelCase = 256 ,       __UpperCAmelCase = 77 ,       __UpperCAmelCase = 6 ,       __UpperCAmelCase = 49_408 ,       __UpperCAmelCase = 2 ,       __UpperCAmelCase = 16 ,       __UpperCAmelCase = 256 ,       __UpperCAmelCase = 256 ,       __UpperCAmelCase = 256 ,       __UpperCAmelCase = 1_024 ,       __UpperCAmelCase = "GN" ,       __UpperCAmelCase = 6 ,       __UpperCAmelCase = 10 ,       __UpperCAmelCase = True ,       __UpperCAmelCase = 8 ,       __UpperCAmelCase = 0.1 ,       __UpperCAmelCase = 2_048 ,       __UpperCAmelCase = False ,       __UpperCAmelCase = False ,       __UpperCAmelCase = 2 ,       __UpperCAmelCase = 4 ,       **__UpperCAmelCase ,       )       ->     str:

					'''simple docstring'''
					if backbone_config is None:
								logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""				)
								__UpperCAmelCase   :      str										=     CONFIG_MAPPING["""swin"""](
								    image_size=224 ,       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=__UpperCAmelCase ,       out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] ,       )
					elif isinstance(__UpperCAmelCase ,       __UpperCAmelCase				):
								__UpperCAmelCase   :      int										=     backbone_config.get("""model_type"""				)
								__UpperCAmelCase   :      List[Any]										=     CONFIG_MAPPING[backbone_model_type]
								__UpperCAmelCase   :      Optional[Any]										=     config_class.from_dict(__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[int]										=     backbone_config

					__UpperCAmelCase   :      Dict										=     ignore_value
					__UpperCAmelCase   :      List[str]										=     num_queries
					__UpperCAmelCase   :      str										=     no_object_weight
					__UpperCAmelCase   :      Any										=     class_weight
					__UpperCAmelCase   :      Optional[int]										=     mask_weight
					__UpperCAmelCase   :      Optional[int]										=     dice_weight
					__UpperCAmelCase   :      Dict										=     contrastive_weight
					__UpperCAmelCase   :      Union[str, Any]										=     contrastive_temperature
					__UpperCAmelCase   :      List[str]										=     train_num_points
					__UpperCAmelCase   :      Union[str, Any]										=     oversample_ratio
					__UpperCAmelCase   :      Dict										=     importance_sample_ratio
					__UpperCAmelCase   :      Dict										=     init_std
					__UpperCAmelCase   :      int										=     init_xavier_std
					__UpperCAmelCase   :      Tuple										=     layer_norm_eps
					__UpperCAmelCase   :      Any										=     is_training
					__UpperCAmelCase   :      Union[str, Any]										=     use_auxiliary_loss
					__UpperCAmelCase   :      str										=     output_auxiliary_logits
					__UpperCAmelCase   :      Optional[Any]										=     strides
					__UpperCAmelCase   :      Tuple										=     task_seq_len
					__UpperCAmelCase   :      int										=     text_encoder_width
					__UpperCAmelCase   :      Union[str, Any]										=     text_encoder_context_length
					__UpperCAmelCase   :      int										=     text_encoder_num_layers
					__UpperCAmelCase   :      Dict										=     text_encoder_vocab_size
					__UpperCAmelCase   :      Any										=     text_encoder_proj_layers
					__UpperCAmelCase   :      Any										=     text_encoder_n_ctx
					__UpperCAmelCase   :      List[str]										=     conv_dim
					__UpperCAmelCase   :      str										=     mask_dim
					__UpperCAmelCase   :      Tuple										=     hidden_dim
					__UpperCAmelCase   :      Union[str, Any]										=     encoder_feedforward_dim
					__UpperCAmelCase   :      Any										=     norm
					__UpperCAmelCase   :      Any										=     encoder_layers
					__UpperCAmelCase   :      List[Any]										=     decoder_layers
					__UpperCAmelCase   :      Any										=     use_task_norm
					__UpperCAmelCase   :      int										=     num_attention_heads
					__UpperCAmelCase   :      int										=     dropout
					__UpperCAmelCase   :      str										=     dim_feedforward
					__UpperCAmelCase   :      int										=     pre_norm
					__UpperCAmelCase   :      Optional[Any]										=     enforce_input_proj
					__UpperCAmelCase   :      Any										=     query_dec_layers
					__UpperCAmelCase   :      Tuple										=     common_stride
					__UpperCAmelCase   :      Dict										=     decoder_layers

					super().__init__(**__UpperCAmelCase				)





		def 							__A       (     self				)       ->     Dict[str, any]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     copy.deepcopy(self.__dict__				)
					__UpperCAmelCase   :      Optional[int]										=     self.backbone_config.to_dict()
					__UpperCAmelCase   :      int										=     self.__class__.model_type
					return output









'''simple docstring'''
from __future__ import annotations

from typing import Any



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     None:  # noqa: A002

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     item
					__UpperCAmelCase   :      str										=     next



class _A  :





		def __init__(     self				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Node | None										=     None
					__UpperCAmelCase   :      Optional[int]										=     0





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     Node(__UpperCAmelCase ,       self.head				)
					self.size += 1





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					# Switched 'self.is_empty()' to 'self.head is None'
					# because mypy was considering the possibility that 'self.head'
					# can be None in below else part and giving error
					if self.head is None:
								return None
					else:
								__UpperCAmelCase   :      str										=     self.head.item
								__UpperCAmelCase   :      Tuple										=     self.head.next
								self.size -= 1
								return item





		def 							__A       (     self				)       ->     bool:

					'''simple docstring'''
					return self.head is None





		def __str__(     self				)       ->     str:

					'''simple docstring'''
					if self.is_empty():
								return ""
					else:
								__UpperCAmelCase   :      Union[str, Any]										=     self.head
								__UpperCAmelCase   :      List[Any]										=     """"""
								__UpperCAmelCase   :      list[str]										=     []
								while iterate:
											item_list.append(str(iterate.item				)				)
											__UpperCAmelCase   :      Any										=     iterate.next

								__UpperCAmelCase   :      Dict										=     """ --> """.join(__UpperCAmelCase				)

								return item_str





		def __len__(     self				)       ->     int:

					'''simple docstring'''
					return self.size




