from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE__		:					List[str]						=					logging.get_logger(__name__)

class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_						):
  lowercase_			=     'maskformer-swin'

  lowercase_			=     {
      'num_attention_heads': 'num_heads',
      'num_hidden_layers': 'num_layers',
  }




  def __init__(      self     :	Optional[Any]     , a_     :	List[str]=224     , a_     :	Tuple=4     , a_     :	List[Any]=3     , a_     :	Optional[int]=96     , a_     :	Any=[2, 2, 6, 2]     , a_     :	int=[3, 6, 12, 24]     , a_     :	Any=7     , a_     :	Any=4.0     , a_     :	Optional[int]=True     , a_     :	Optional[Any]=0.0     , a_     :	Optional[Any]=0.0     , a_     :	Optional[int]=0.1     , a_     :	Union[str, Any]="gelu"     , a_     :	Optional[Any]=False     , a_     :	Any=0.02     , a_     :	Optional[int]=1e-5     , a_     :	List[Any]=None     , a_     :	Dict=None     , **a_     :	Any     , )->       Any:




       """simple docstring"""
       super().__init__(**a_				)

       SCREAMING_SNAKE_CASE__				:				str			=		image_size
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		patch_size
       SCREAMING_SNAKE_CASE__				:				str			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Dict			=		embed_dim
       SCREAMING_SNAKE_CASE__				:				List[str]			=		depths
       SCREAMING_SNAKE_CASE__				:				int			=		len(a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		num_heads
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		window_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		mlp_ratio
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		qkv_bias
       SCREAMING_SNAKE_CASE__				:				str			=		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__				:				Tuple			=		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		drop_path_rate
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		hidden_act
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		use_absolute_embeddings
       SCREAMING_SNAKE_CASE__				:				Tuple			=		layer_norm_eps
       SCREAMING_SNAKE_CASE__				:				int			=		initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE__				:				Dict			=		int(embed_dim * 2 ** (len(a_				) - 1)				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		['stem'] + [F'''stage{idx}''' for idx in range(1     , len(a_				) + 1				)]
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Tuple			=		get_aligned_output_features_output_indices(
           out_features=a_     , out_indices=a_     , stage_names=self.stage_names				)




from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
      import accelerate







def     _a				(      lowercase__  :			List[str] ):







     '''simple docstring'''


     if not is_accelerate_available():
          return method
     SCREAMING_SNAKE_CASE__				:				str			=		version.parse(accelerate.__version__ ).base_version
     if version.parse(lowercase__ ) < version.parse('0.17.0' ):
          return method

     def wrapper(self  :			Optional[int]     ,  *lowercase__  :			int     ,  **lowercase__  :			Tuple ):
          if hasattr(self     ,  '_hf_hook' ) and hasattr(self._hf_hook     ,  'pre_forward' ):
               self._hf_hook.pre_forward(self )
          return method(self     ,  *lowercase__     ,  **lowercase__ )

     return wrapper



