code: 
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  __A   :     int      =			logging.get_logger(__name__)
  
  __A   :     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				(							lowerCAmelCase ):
      lowerCAmelCase_							:		Dict						=							"oneformer"
      lowerCAmelCase_							:		Dict						=							{"hidden_size": "hidden_dim"}
  
  
  
  
  
  
      def __init__(	self			:    Tuple    ,				UpperCAmelCase_			:    Optional[Dict] = None    ,				UpperCAmelCase_			:    int = 255    ,				UpperCAmelCase_			:    int = 150    ,				UpperCAmelCase_			:    int = 0.1    ,				UpperCAmelCase_			:    float = 2.0    ,				UpperCAmelCase_			:    float = 5.0    ,				UpperCAmelCase_			:    float = 5.0    ,				UpperCAmelCase_			:    float = 0.5    ,				UpperCAmelCase_			:    float = 0.07    ,				UpperCAmelCase_			:    int = 12544    ,				UpperCAmelCase_			:    float = 3.0    ,				UpperCAmelCase_			:    float = 0.75    ,				UpperCAmelCase_			:    float = 0.02    ,				UpperCAmelCase_			:    float = 1.0    ,				UpperCAmelCase_			:    float = 1E-05    ,				UpperCAmelCase_			:    bool = False    ,				UpperCAmelCase_			:    bool = True    ,				UpperCAmelCase_			:    bool = True    ,				UpperCAmelCase_			:    Optional[list] = [4, 8, 16, 32]    ,				UpperCAmelCase_			:    int = 77    ,				UpperCAmelCase_			:    int = 256    ,				UpperCAmelCase_			:    int = 77    ,				UpperCAmelCase_			:    int = 6    ,				UpperCAmelCase_			:    int = 49408    ,				UpperCAmelCase_			:    int = 2    ,				UpperCAmelCase_			:    int = 16    ,				UpperCAmelCase_			:    int = 256    ,				UpperCAmelCase_			:    int = 256    ,				UpperCAmelCase_			:    int = 256    ,				UpperCAmelCase_			:    int = 1024    ,				UpperCAmelCase_			:    str = "GN"    ,				UpperCAmelCase_			:    int = 6    ,				UpperCAmelCase_			:    int = 10    ,				UpperCAmelCase_			:    bool = True    ,				UpperCAmelCase_			:    int = 8    ,				UpperCAmelCase_			:    float = 0.1    ,				UpperCAmelCase_			:    int = 2048    ,				UpperCAmelCase_			:    bool = False    ,				UpperCAmelCase_			:    bool = False    ,				UpperCAmelCase_			:    int = 2    ,				UpperCAmelCase_			:    int = 4    ,				**UpperCAmelCase_			:    List[str]    ,				):
          if backbone_config is None:
              logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
              lowerCAmelCase				:			Dict    			=      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_    ):
              lowerCAmelCase				:			List[Any]    			=      backbone_config.get('model_type'    )
              lowerCAmelCase				:			List[str]    			=      CONFIG_MAPPING[backbone_model_type]
              lowerCAmelCase				:			Any    			=      config_class.from_dict(UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[Any]    			=      backbone_config
  
          lowerCAmelCase				:			List[str]    			=      ignore_value
          lowerCAmelCase				:			str    			=      num_queries
          lowerCAmelCase				:			str    			=      no_object_weight
          lowerCAmelCase				:			List[str]    			=      class_weight
          lowerCAmelCase				:			Tuple    			=      mask_weight
          lowerCAmelCase				:			Dict    			=      dice_weight
          lowerCAmelCase				:			Optional[Any]    			=      contrastive_weight
          lowerCAmelCase				:			str    			=      contrastive_temperature
          lowerCAmelCase				:			Union[str, Any]    			=      train_num_points
          lowerCAmelCase				:			List[Any]    			=      oversample_ratio
          lowerCAmelCase				:			int    			=      importance_sample_ratio
          lowerCAmelCase				:			Tuple    			=      init_std
          lowerCAmelCase				:			str    			=      init_xavier_std
          lowerCAmelCase				:			str    			=      layer_norm_eps
          lowerCAmelCase				:			Dict    			=      is_training
          lowerCAmelCase				:			List[str]    			=      use_auxiliary_loss
          lowerCAmelCase				:			int    			=      output_auxiliary_logits
          lowerCAmelCase				:			Optional[int]    			=      strides
          lowerCAmelCase				:			Optional[Any]    			=      task_seq_len
          lowerCAmelCase				:			Tuple    			=      text_encoder_width
          lowerCAmelCase				:			int    			=      text_encoder_context_length
          lowerCAmelCase				:			Optional[int]    			=      text_encoder_num_layers
          lowerCAmelCase				:			List[Any]    			=      text_encoder_vocab_size
          lowerCAmelCase				:			str    			=      text_encoder_proj_layers
          lowerCAmelCase				:			Any    			=      text_encoder_n_ctx
          lowerCAmelCase				:			Optional[Any]    			=      conv_dim
          lowerCAmelCase				:			Union[str, Any]    			=      mask_dim
          lowerCAmelCase				:			Dict    			=      hidden_dim
          lowerCAmelCase				:			Any    			=      encoder_feedforward_dim
          lowerCAmelCase				:			int    			=      norm
          lowerCAmelCase				:			Any    			=      encoder_layers
          lowerCAmelCase				:			Any    			=      decoder_layers
          lowerCAmelCase				:			Any    			=      use_task_norm
          lowerCAmelCase				:			str    			=      num_attention_heads
          lowerCAmelCase				:			Optional[int]    			=      dropout
          lowerCAmelCase				:			List[str]    			=      dim_feedforward
          lowerCAmelCase				:			Union[str, Any]    			=      pre_norm
          lowerCAmelCase				:			Optional[int]    			=      enforce_input_proj
          lowerCAmelCase				:			Dict    			=      query_dec_layers
          lowerCAmelCase				:			Dict    			=      common_stride
          lowerCAmelCase				:			List[str]    			=      decoder_layers
  
          super().__init__(**UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			str    			=      copy.deepcopy(self.__dict__    )
          lowerCAmelCase				:			Union[str, Any]    			=      self.backbone_config.to_dict()
          lowerCAmelCase				:			List[str]    			=      self.__class__.model_type
          return output
  
  
  
  
  
  
style_context: 
  
  __A   :     List[Any]      =			'''
  # Installation de Transformers
  ! pip install transformers datasets
  # Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
  # ! pip install git+https://github.com/huggingface/transformers.git
  '''
  
  __A   :     Dict      =			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
  __A   :     Tuple      =			{
      '''{processor_class}''': '''FakeProcessorClass''',
      '''{model_class}''': '''FakeModelClass''',
      '''{object_class}''': '''FakeObjectClass''',
  }
  
  
  
  
  
  