

'''simple docstring'''

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					dict[str, str]   )      -> str:
							return "".join(cipher_map.get(lowerCAmelCase__		,lowerCAmelCase__   ) for ch in message.upper()   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					dict[str, str]   )      -> str:
							SCREAMING_SNAKE_CASE_				=				{v: k for k, v in cipher_map.items()}
							return "".join(rev_cipher_map.get(lowerCAmelCase__		,lowerCAmelCase__   ) for ch in message.upper()   )




def 							_UpperCamelCase				(   )      -> None:
							SCREAMING_SNAKE_CASE_				=				input('Enter message to encode or decode: '   ).strip()
							SCREAMING_SNAKE_CASE_				=				input('Enter keyword: '   ).strip()
							SCREAMING_SNAKE_CASE_				=				input('Encipher or decipher? E/D:'   ).strip()[0].lower()
							try:
														SCREAMING_SNAKE_CASE_				=				{'e': encipher, 'd': decipher}[option]
							except KeyError:
														raise KeyError('invalid input option'   )
							SCREAMING_SNAKE_CASE_				=				create_cipher_map(lowerCAmelCase__   )
							print(func(lowerCAmelCase__		,lowerCAmelCase__   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()








'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """CLIPImageProcessor"""
				_a    	=     ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



				def __init__(    self,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.',     _lowercase,     )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('feature_extractor'       )

											SCREAMING_SNAKE_CASE_				=				image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'       )
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'       )

											super().__init__(_lowercase,     _lowercase       )



				def __call__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							str:

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'       )

											if text is not None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if text is not None and images is not None:
																		SCREAMING_SNAKE_CASE_				=				image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_lowercase       ),     tensor_type=_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[Any]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )





				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.model_input_names
											SCREAMING_SNAKE_CASE_				=				self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





