code: 
  
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  __lowerCamelCase       :       Dict				   =			logging.get_logger(__name__)
  
  __lowerCamelCase       :       Tuple				   =			{
      "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      (      UpperCamelCase_   ):
  		__lowercase													=		"oneformer"
  		__lowercase													=		{"hidden_size": "hidden_dim"}
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase = None	,     __UpperCamelCase = 2_55	,     __UpperCamelCase = 1_50	,     __UpperCamelCase = 0.1	,     __UpperCamelCase = 2.0	,     __UpperCamelCase = 5.0	,     __UpperCamelCase = 5.0	,     __UpperCamelCase = 0.5	,     __UpperCamelCase = 0.07	,     __UpperCamelCase = 1_25_44	,     __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 = 2_56	,     __UpperCamelCase = 77	,     __UpperCamelCase = 6	,     __UpperCamelCase = 4_94_08	,     __UpperCamelCase = 2	,     __UpperCamelCase = 16	,     __UpperCamelCase = 2_56	,     __UpperCamelCase = 2_56	,     __UpperCamelCase = 2_56	,     __UpperCamelCase = 10_24	,     __UpperCamelCase = "GN"	,     __UpperCamelCase = 6	,     __UpperCamelCase = 10	,     __UpperCamelCase = True	,     __UpperCamelCase = 8	,     __UpperCamelCase = 0.1	,     __UpperCamelCase = 20_48	,     __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.'''							)
  										A__     :							Union[str, Any]   				=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=__UpperCamelCase	,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	,     )
  						elif isinstance(__UpperCamelCase	,     __UpperCamelCase							):
  										A__     :							Union[str, Any]   				=backbone_config.get('''model_type'''							)
  										A__     :							Optional[Any]   				=CONFIG_MAPPING[backbone_model_type]
  										A__     :							Any   				=config_class.from_dict(__UpperCamelCase							)
  
  						A__     :							List[Any]   				=backbone_config
  
  						A__     :							List[str]   				=ignore_value
  						A__     :							List[Any]   				=num_queries
  						A__     :							Any   				=no_object_weight
  						A__     :							Any   				=class_weight
  						A__     :							Dict   				=mask_weight
  						A__     :							Optional[Any]   				=dice_weight
  						A__     :							Tuple   				=contrastive_weight
  						A__     :							Union[str, Any]   				=contrastive_temperature
  						A__     :							List[Any]   				=train_num_points
  						A__     :							Any   				=oversample_ratio
  						A__     :							Optional[Any]   				=importance_sample_ratio
  						A__     :							List[Any]   				=init_std
  						A__     :							List[str]   				=init_xavier_std
  						A__     :							List[Any]   				=layer_norm_eps
  						A__     :							Optional[Any]   				=is_training
  						A__     :							List[Any]   				=use_auxiliary_loss
  						A__     :							List[Any]   				=output_auxiliary_logits
  						A__     :							List[str]   				=strides
  						A__     :							Union[str, Any]   				=task_seq_len
  						A__     :							List[Any]   				=text_encoder_width
  						A__     :							Dict   				=text_encoder_context_length
  						A__     :							Dict   				=text_encoder_num_layers
  						A__     :							List[str]   				=text_encoder_vocab_size
  						A__     :							List[Any]   				=text_encoder_proj_layers
  						A__     :							int   				=text_encoder_n_ctx
  						A__     :							str   				=conv_dim
  						A__     :							Optional[int]   				=mask_dim
  						A__     :							Dict   				=hidden_dim
  						A__     :							str   				=encoder_feedforward_dim
  						A__     :							Optional[int]   				=norm
  						A__     :							Optional[Any]   				=encoder_layers
  						A__     :							Dict   				=decoder_layers
  						A__     :							Union[str, Any]   				=use_task_norm
  						A__     :							Dict   				=num_attention_heads
  						A__     :							Optional[int]   				=dropout
  						A__     :							str   				=dim_feedforward
  						A__     :							Optional[Any]   				=pre_norm
  						A__     :							str   				=enforce_input_proj
  						A__     :							int   				=query_dec_layers
  						A__     :							str   				=common_stride
  						A__     :							List[Any]   				=decoder_layers
  
  						super().__init__(**__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict[str, any]:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=copy.deepcopy(self.__dict__							)
  						A__     :							Dict   				=self.backbone_config.to_dict()
  						A__     :							str   				=self.__class__.model_type
  						return output
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  __lowerCamelCase       :       str				   =			{
      "configuration_align": [
          "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "AlignConfig",
          "AlignTextConfig",
          "AlignVisionConfig",
      ],
      "processing_align": ["AlignProcessor"],
  }
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Dict				   =			[
  						    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "AlignModel",
  						    "AlignPreTrainedModel",
  						    "AlignTextModel",
  						    "AlignVisionModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_align import (
  						    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    AlignConfig,
  						    AlignTextConfig,
  						    AlignVisionConfig,
  						)
  						from .processing_align import AlignProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_align import (
  												    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    AlignModel,
  												    AlignPreTrainedModel,
  												    AlignTextModel,
  												    AlignVisionModel,
  												)
  
  else:
  						import sys
  
  						__lowerCamelCase       :       List[str]				   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  