



'''simple docstring'''

def a_     (    __UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=''
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups







def a_     (    __UpperCAmelCase					)				->					List[Any]:





			"""simple docstring"""







			snake_case:    Any      							=[chr(i + 65					) for i in range(26					)]
			# Remove duplicate characters from key
			snake_case:    int      							=remove_duplicates(key.upper()					)
			snake_case:    Tuple      							=len(lowercase__					)
			# First fill cipher with key characters
			snake_case:    int      							={alphabet[i]: char for i, char in enumerate(lowercase__					)}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(lowercase__					)				,							26					):
						snake_case:    Tuple      							=alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									snake_case:    int      							=alphabet[i - offset]
						snake_case:    str      							=char
			return cipher_alphabet







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			return "".join(cipher_map.get(lowercase__				,							lowercase__					) for ch in message.upper()					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							={v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(lowercase__				,							lowercase__					) for ch in message.upper()					)







def a_     (    )				->					Tuple:





			"""simple docstring"""







			snake_case:    int      							=input('Enter message to encode or decode: '					).strip()
			snake_case:    Optional[Any]      							=input('Enter keyword: '					).strip()
			snake_case:    Dict      							=input('Encipher or decipher? E/D:'					).strip()[0].lower()
			try:
						snake_case:    Tuple      							={'e': encipher, 'd': decipher}[option]
			except KeyError:
						raise KeyError('invalid input option'					)
			snake_case:    int      							=create_cipher_map(lowercase__					)
			print(func(lowercase__				,							lowercase__					)					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




