
import math




def 			_SCREAMING_SNAKE_CASE      (	)      ->					None:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      input("Enter message: ")
							__UpperCamelCase			:     Dict         =      int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
							__UpperCamelCase			:     List[str]         =      input("Encryption/Decryption [e/d]: ")

							if mode.lower().startswith("e"):
														__UpperCamelCase			:     Optional[Any]         =      encrypt_message(_lowerCamelCase		,						_lowerCamelCase)
							elif mode.lower().startswith("d"):
														__UpperCamelCase			:     Optional[int]         =      decrypt_message(_lowerCamelCase		,						_lowerCamelCase)

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int		,						_lowerCamelCase				:    str)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      [""] * key
							for col in range(_lowerCamelCase):
														__UpperCamelCase			:     Union[str, Any]         =      col
														while pointer < len(_lowerCamelCase):
																					cipher_text[col] += message[pointer]
																					pointer += key
							return "".join(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int		,						_lowerCamelCase				:    str)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      math.ceil(len(_lowerCamelCase) / key)
							__UpperCamelCase			:     List[str]         =      key
							__UpperCamelCase			:     Dict         =      (num_cols * num_rows) - len(_lowerCamelCase)
							__UpperCamelCase			:     Any         =      [""] * num_cols
							__UpperCamelCase			:     Optional[int]         =      0
							__UpperCamelCase			:     Dict         =      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)
														):
																					__UpperCamelCase			:     Any         =      0
																					row += 1

							return "".join(_lowerCamelCase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()

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


lowercase						:      Optional[Any]									= logging.get_logger(__name__)

lowercase						:      List[str]									= {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class 				lowerCamelCase__							(       __lowercase  ,		__lowercase):




	'''simple docstring'''




	_A			=  'nat'

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







	def __init__(      self	:Optional[Any]  ,     a	:Any=4  ,     a	:Any=3  ,     a	:int=6_4  ,     a	:Dict=[3, 4, 6, 5]  ,     a	:Dict=[2, 4, 8, 1_6]  ,     a	:Optional[Any]=7  ,     a	:Any=3.0  ,     a	:Optional[int]=True  ,     a	:int=0.0  ,     a	:Union[str, Any]=0.0  ,     a	:List[Any]=0.1  ,     a	:str="gelu"  ,     a	:Union[str, Any]=0.02  ,     a	:Tuple=1E-5  ,     a	:str=0.0  ,     a	:Optional[int]=None  ,     a	:Dict=None  ,     **a	:Optional[Any]  ,     )	->				int:
								super().__init__(**a		)

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