

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


a_     :    int     		=   logging.get_logger(__name__)




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    ="maskformer-swin"

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

    def __init__(      self			:   Dict				,     snake_case__			:   Optional[int]=2_2_4				,     snake_case__			:   Optional[int]=4				,     snake_case__			:   Dict=3				,     snake_case__			:   Optional[int]=9_6				,     snake_case__			:   Tuple=[2, 2, 6, 2]				,     snake_case__			:   Any=[3, 6, 1_2, 2_4]				,     snake_case__			:   str=7				,     snake_case__			:   Optional[int]=4.0				,     snake_case__			:   str=True				,     snake_case__			:   Any=0.0				,     snake_case__			:   Tuple=0.0				,     snake_case__			:   Tuple=0.1				,     snake_case__			:   Optional[int]="gelu"				,     snake_case__			:   str=False				,     snake_case__			:   List[Any]=0.02				,     snake_case__			:   Optional[Any]=1E-5				,     snake_case__			:   Union[str, Any]=None				,     snake_case__			:   List[Any]=None				,     **snake_case__			:   List[str]				,     ):

     """simple docstring"""






     super().__init__(**snake_case__     )

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




from collections.abc import Sequence


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Sequence[int] | None = None						)		->				int:
 '''simple docstring'''


 if nums is None or not nums:
  raise ValueError('Input sequence should not be empty'						)

 SCREAMING_SNAKE_CASE		    =      nums[0]
 for i in range(1		,				len(_UpperCamelCase						)						):
  SCREAMING_SNAKE_CASE		    =      nums[i]
  SCREAMING_SNAKE_CASE		    =      max(_UpperCamelCase		,				ans + num		,				_UpperCamelCase						)

 return ans


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Try on a sample input from the user
      a_     :    str     		=   int(input("Enter number of elements : ").strip())
      a_     :    Any     		=   list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
      print(max_subsequence_sum(array))

