
'''simple docstring'''







import copy
from typing import Dict, List, Optional

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


__lowerCAmelCase   =       {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

__lowerCAmelCase   =       logging.get_logger(__name__)







class UpperCAmelCase__      (					UpperCamelCase__      ):





  """simple docstring"""

  __UpperCAmelCase							:							Union[str, Any]    					=   '''mask2former'''
  __UpperCAmelCase							:							int    					=   ['''swin''']
  __UpperCAmelCase							:							Dict    					=   {'''hidden_size''': '''hidden_dim'''}


  def __init__(						self :	List[str]				,_a :	Optional[Dict] = None				,_a :	int = 256				,_a :	int = 256				,_a :	int = 256				,_a :	int = 1024				,_a :	str = "relu"				,_a :	int = 6				,_a :	int = 10				,_a :	int = 8				,_a :	float = 0.0				,_a :	int = 2048				,_a :	bool = False				,_a :	bool = False				,_a :	int = 4				,_a :	int = 255				,_a :	int = 100				,_a :	float = 0.1				,_a :	float = 2.0				,_a :	float = 5.0				,_a :	float = 5.0				,_a :	int = 1_2544				,_a :	float = 3.0				,_a :	float = 0.75				,_a :	float = 0.02				,_a :	float = 1.0				,_a :	bool = True				,_a :	List[int] = [4, 8, 16, 32]				,_a :	bool = None				,**_a :	Tuple				,):


     '''simple docstring'''






     if backbone_config is None:
        logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'			)
        _a							:      Any     =  CONFIG_MAPPING['swin'](
            image_size=224				,in_channels=3				,patch_size=4				,embed_dim=96				,depths=[2, 2, 18, 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']				,)

     if isinstance(__A				,__A			):
        _a							:      str     =  backbone_config.pop('model_type'			)
        _a							:      Tuple     =  CONFIG_MAPPING[backbone_model_type]
        _a							:      Union[str, Any]     =  config_class.from_dict(__A			)

     # verify that the backbone is supported
     if backbone_config.model_type not in self.backbones_supported:
        logger.warning_once(
            F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
            F"""Supported model types: {','.join(self.backbones_supported			)}"""			)

     _a							:      Optional[int]     =  backbone_config
     _a							:      Optional[int]     =  feature_size
     _a							:      Tuple     =  mask_feature_size
     _a							:      Union[str, Any]     =  hidden_dim
     _a							:      Tuple     =  encoder_feedforward_dim
     _a							:      List[Any]     =  activation_function
     _a							:      Any     =  encoder_layers
     _a							:      List[str]     =  decoder_layers
     _a							:      Dict     =  num_attention_heads
     _a							:      Dict     =  dropout
     _a							:      Optional[int]     =  dim_feedforward
     _a							:      Dict     =  pre_norm
     _a							:      List[Any]     =  enforce_input_projection
     _a							:      List[str]     =  common_stride
     _a							:      Tuple     =  ignore_value
     _a							:      List[Any]     =  num_queries
     _a							:      str     =  no_object_weight
     _a							:      Union[str, Any]     =  class_weight
     _a							:      Union[str, Any]     =  mask_weight
     _a							:      List[str]     =  dice_weight
     _a							:      Union[str, Any]     =  train_num_points
     _a							:      Optional[Any]     =  oversample_ratio
     _a							:      int     =  importance_sample_ratio
     _a							:      List[Any]     =  init_std
     _a							:      str     =  init_xavier_std
     _a							:      Dict     =  use_auxiliary_loss
     _a							:      Dict     =  feature_strides
     _a							:      List[str]     =  output_auxiliary_logits
     _a							:      List[str]     =  decoder_layers

     super().__init__(**__A			)


  @classmethod
  def     __lowercase						(						cls :	str				,_a :	PretrainedConfig				,**_a :	int			):


     '''simple docstring'''






     return cls(
         backbone_config=__A				,**__A				,)


  def     __lowercase						(						self :	Tuple			):


     '''simple docstring'''






     _a							:      int     =  copy.deepcopy(self.__dict__			)
     _a							:      Union[str, Any]     =  self.backbone_config.to_dict()
     _a							:      int     =  self.__class__.model_type
     return output










from torch import nn






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		if act_fn in ["swish", "silu"]:
				return nn.SiLU()
		elif act_fn == "mish":
				return nn.Mish()
		elif act_fn == "gelu":
				return nn.GELU()
		else:
				raise ValueError(f'''Unsupported activation function: {act_fn}'''     )