



import math



def 					lowercase				(      )     ->   Optional[int]:
 _snake_case			:     Optional[Any] 		=    input("""Enter message: """ )
 _snake_case			:     List[Any] 		=    int(input(F'''Enter key [2-{len(a__ ) - 1}]: ''' ) )
 _snake_case			:     Dict 		=    input("""Encryption/Decryption [e/d]: """ )

 if mode.lower().startswith("""e""" ):
  _snake_case			:     Dict 		=    encrypt_message(a__ ,				a__ )
 elif mode.lower().startswith("""d""" ):
  _snake_case			:     Union[str, Any] 		=    decrypt_message(a__ ,				a__ )

 # Append pipe symbol (vertical bar) to identify spaces at the end.
 print(F'''Output:\n{text + '|'}''' )



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		Tuple )     ->   Optional[int]:
 _snake_case			:     List[Any] 		=    [""""""] * key
 for col in range(a__ ):
  _snake_case			:     List[str] 		=    col
  while pointer < len(a__ ):
   cipher_text[col] += message[pointer]
   pointer += key
 return "".join(a__ )



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Union[str, Any] ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any] )     ->   Optional[Any]:
 _snake_case			:     Optional[Any] 		=    math.ceil(len(a__ ) / key )
 _snake_case			:     Optional[int] 		=    key
 _snake_case			:     int 		=    (num_cols * num_rows) - len(a__ )
 _snake_case			:     List[str] 		=    [""""""] * num_cols
 _snake_case			:     Optional[Any] 		=    0
 _snake_case			:     Optional[int] 		=    0

 for symbol in message:
  plain_text[col] += symbol
  col += 1

  if (
      (col == num_cols)
      or (col == num_cols - 1)
      and (row >= num_rows - num_shaded_boxes)
  ):
   _snake_case			:     Tuple 		=    0
   row += 1

 return "".join(a__ )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



'''simple docstring'''



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


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







class 						lowerCAmelCase__   (      a ,					a					):



 """simple docstring"""



 lowerCAmelCase__  					=      "maskformer-swin"

 lowerCAmelCase__  					=      {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }
 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple=224					,							__SCREAMING_SNAKE_CASE					:       str=4					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=3					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=96					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=[2, 2, 6, 2]					,							__SCREAMING_SNAKE_CASE					:       Any=[3, 6, 12, 24]					,							__SCREAMING_SNAKE_CASE					:       Dict=7					,							__SCREAMING_SNAKE_CASE					:       Dict=4.0					,							__SCREAMING_SNAKE_CASE					:       Dict=True					,							__SCREAMING_SNAKE_CASE					:       str=0.0					,							__SCREAMING_SNAKE_CASE					:       int=0.0					,							__SCREAMING_SNAKE_CASE					:       str=0.1					,							__SCREAMING_SNAKE_CASE					:       List[Any]="gelu"					,							__SCREAMING_SNAKE_CASE					:       str=False					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=0.02					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=1E-5					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							__SCREAMING_SNAKE_CASE					:       Dict=None					,							**__SCREAMING_SNAKE_CASE					:       Tuple					,							)  -> Tuple:


      """simple docstring"""




      super().__init__(**__SCREAMING_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(__SCREAMING_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(__SCREAMING_SNAKE_CASE			) - 1)			)
      __SCREAMING_SNAKE_CASE     =      ["""stem"""] + [f'stage{idx}' for idx in range(1					,							len(__SCREAMING_SNAKE_CASE			) + 1			)]
      __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE					,							out_indices=__SCREAMING_SNAKE_CASE					,							stage_names=self.stage_names			)






