code: 
  
  
  
  
  
  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 				UpperCamelCase (    a_    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      str       ='oneformer'
        UpperCAmelCase_						:      Tuple       ={'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 = 12544				,						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 = 49408				,						UpperCAmelCase = 2				,						UpperCAmelCase = 16				,						UpperCAmelCase = 256				,						UpperCAmelCase = 256				,						UpperCAmelCase = 256				,						UpperCAmelCase = 1024				,						UpperCAmelCase = "GN"				,						UpperCAmelCase = 6				,						UpperCAmelCase = 10				,						UpperCAmelCase = True				,						UpperCAmelCase = 8				,						UpperCAmelCase = 0.1				,						UpperCAmelCase = 2048				,						UpperCAmelCase = False				,						UpperCAmelCase = False				,						UpperCAmelCase = 2				,						UpperCAmelCase = 4				,						**UpperCAmelCase				,						)       ->   List[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if backbone_config is None:
            logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."   )
            __snake_case   :				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   ):
            __snake_case   :				Any        =       backbone_config.get("model_type"   )
            __snake_case   :				Union[str, Any]        =       CONFIG_MAPPING[backbone_model_type]
            __snake_case   :				Optional[Any]        =       config_class.from_dict(UpperCAmelCase   )
  
          __snake_case   :				Union[str, Any]        =       backbone_config
  
          __snake_case   :				Any        =       ignore_value
          __snake_case   :				Union[str, Any]        =       num_queries
          __snake_case   :				str        =       no_object_weight
          __snake_case   :				Union[str, Any]        =       class_weight
          __snake_case   :				List[str]        =       mask_weight
          __snake_case   :				int        =       dice_weight
          __snake_case   :				Optional[int]        =       contrastive_weight
          __snake_case   :				Optional[Any]        =       contrastive_temperature
          __snake_case   :				int        =       train_num_points
          __snake_case   :				int        =       oversample_ratio
          __snake_case   :				List[Any]        =       importance_sample_ratio
          __snake_case   :				str        =       init_std
          __snake_case   :				Union[str, Any]        =       init_xavier_std
          __snake_case   :				Dict        =       layer_norm_eps
          __snake_case   :				str        =       is_training
          __snake_case   :				List[Any]        =       use_auxiliary_loss
          __snake_case   :				int        =       output_auxiliary_logits
          __snake_case   :				List[str]        =       strides
          __snake_case   :				Optional[int]        =       task_seq_len
          __snake_case   :				Union[str, Any]        =       text_encoder_width
          __snake_case   :				List[str]        =       text_encoder_context_length
          __snake_case   :				Tuple        =       text_encoder_num_layers
          __snake_case   :				Dict        =       text_encoder_vocab_size
          __snake_case   :				Optional[Any]        =       text_encoder_proj_layers
          __snake_case   :				Dict        =       text_encoder_n_ctx
          __snake_case   :				Tuple        =       conv_dim
          __snake_case   :				List[Any]        =       mask_dim
          __snake_case   :				List[Any]        =       hidden_dim
          __snake_case   :				Optional[Any]        =       encoder_feedforward_dim
          __snake_case   :				Optional[int]        =       norm
          __snake_case   :				Tuple        =       encoder_layers
          __snake_case   :				Optional[int]        =       decoder_layers
          __snake_case   :				Dict        =       use_task_norm
          __snake_case   :				int        =       num_attention_heads
          __snake_case   :				str        =       dropout
          __snake_case   :				Any        =       dim_feedforward
          __snake_case   :				str        =       pre_norm
          __snake_case   :				str        =       enforce_input_proj
          __snake_case   :				Dict        =       query_dec_layers
          __snake_case   :				Optional[int]        =       common_stride
          __snake_case   :				int        =       decoder_layers
  
          super().__init__(**UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self   )       ->   Dict[str, any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[str]        =       copy.deepcopy(self.__dict__   )
          __snake_case   :				Dict        =       self.backbone_config.to_dict()
          __snake_case   :				Any        =       self.__class__.model_type
          return output
  
  
style_context: 
  
  
  
  
  
  import torch
  from torch import autograd
  
  
  
  
  class 				_lowerCamelCase (    autograd.Function    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       inputs.numel()
          __snake_case   :				Optional[int]        =       int(0.005 * nb_elems   ) + 1
          if sigmoid:
            __snake_case   :				Any        =       (torch.sigmoid(UpperCAmelCase   ) > threshold).type(inputs.type()   )
          else:
            __snake_case   :				Optional[int]        =       (inputs > threshold).type(inputs.type()   )
          if mask.sum() < nb_min:
            # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
            __snake_case   :				List[str]        =       inputs.flatten().kthvalue(max(nb_elems - nb_min				,						1   )   ).values
            __snake_case   :				Union[str, Any]        =       (inputs > k_threshold).type(inputs.type()   )
          return mask
  
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return gradOutput, None, None
  
  
  
  
  class 				_lowerCamelCase (    autograd.Function    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[int]        =       inputs.clone()
          __snake_case    ,   __snake_case   :				int        =       inputs.flatten().sort(descending=UpperCAmelCase   )
          __snake_case   :				Tuple        =       int(threshold * inputs.numel()   )
  
          # flat_out and mask access the same memory.
          __snake_case   :				Optional[int]        =       mask.flatten()
          __snake_case   :				Union[str, Any]        =       0
          __snake_case   :				Any        =       1
          return mask
  
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return gradOutput, None
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        @staticmethod
        def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       inputs.clone()
          __snake_case    ,   __snake_case   :				Optional[Any]        =       inputs.abs().flatten().sort(descending=UpperCAmelCase   )
          __snake_case   :				List[Any]        =       int(threshold * inputs.numel()   )
  
          # flat_out and mask access the same memory.
          __snake_case   :				Optional[Any]        =       mask.flatten()
          __snake_case   :				Tuple        =       0
          __snake_case   :				Any        =       1
          return mask
  
  