



'''simple docstring'''





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


__a          =		logging.get_logger(__name__)







class     UpperCAmelCase_       (       _a		,		_a	):
    """simple docstring"""







    lowercase					=		"maskformer-swin"

    lowercase					=		{
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }




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

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





'''simple docstring'''





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> float:
      snake_case__  :    str    						=      (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
      # formula for sum of series
      return total





def 						__snake_case(				)		-> List[str]:
      print(sum_of_series(1     ,   1     ,   10		)		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
