



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


__UpperCAmelCase	       =	logging.get_logger(__name__)



class     lowerCAmelCase_		(  a__	,   a__							):
       UpperCAmelCase__    : List[str]       					=  "maskformer-swin"

       UpperCAmelCase__    : Any       					=  {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }
       def __init__(      self,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=96,      SCREAMING_SNAKE_CASE_=[2, 2, 6, 2],      SCREAMING_SNAKE_CASE_=[3, 6, 12, 24],      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=4.0,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.0,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-5,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_,      )					->		int:
          super().__init__(**SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     image_size
          UpperCamelCase   :  Dict						     =     patch_size
          UpperCamelCase   :  Optional[Any]						     =     num_channels
          UpperCamelCase   :  List[Any]						     =     embed_dim
          UpperCamelCase   :  Dict						     =     depths
          UpperCamelCase   :  Union[str, Any]						     =     len(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     num_heads
          UpperCamelCase   :  Dict						     =     window_size
          UpperCamelCase   :  Any						     =     mlp_ratio
          UpperCamelCase   :  List[str]						     =     qkv_bias
          UpperCamelCase   :  Optional[Any]						     =     hidden_dropout_prob
          UpperCamelCase   :  List[str]						     =     attention_probs_dropout_prob
          UpperCamelCase   :  Optional[int]						     =     drop_path_rate
          UpperCamelCase   :  Optional[int]						     =     hidden_act
          UpperCamelCase   :  int						     =     use_absolute_embeddings
          UpperCamelCase   :  List[str]						     =     layer_norm_eps
          UpperCamelCase   :  List[Any]						     =     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
          UpperCamelCase   :  List[str]						     =     int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_	) - 1)	)
          UpperCamelCase   :  Union[str, Any]						     =     ['stem'] + [F"""stage{idx}""" for idx in range(1,      len(SCREAMING_SNAKE_CASE_	) + 1	)]
          UpperCamelCase     ,					UpperCamelCase   :  Any						     =     get_aligned_output_features_output_indices(
              out_features=SCREAMING_SNAKE_CASE_,      out_indices=SCREAMING_SNAKE_CASE_,      stage_names=self.stage_names	)








from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     lowerCAmelCase_		(  a__							):
       @staticmethod
       @abstractmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		int:
          raise NotImplementedError()






       @abstractmethod
       def  snake_case_	(      self	)					->		str:
          raise NotImplementedError()



