



import warnings

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






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[str]				=	["""image_processor""", """tokenizer"""]
	lowerCAmelCase	:   str				=	"""CLIPImageProcessor"""
	lowerCAmelCase	:   str				=	("""CLIPTokenizer""", """CLIPTokenizerFast""")



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

							A__							=       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__(UpperCAmelCase__			,	UpperCAmelCase__		)



	def __call__(       self			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None			,	**UpperCAmelCase__		):

							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:
													A__							=       self.tokenizer(UpperCAmelCase__			,	return_tensors=UpperCAmelCase__			,	**UpperCAmelCase__		)

							if images is not None:
													A__							=       self.image_processor(UpperCAmelCase__			,	return_tensors=UpperCAmelCase__			,	**UpperCAmelCase__		)

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



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.batch_decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	@property
	def 			__A							(       self		):
							A__							=       self.tokenizer.model_input_names
							A__							=       self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



	@property
	def 			__A							(       self		):
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,	UpperCAmelCase__			,	)
							return self.image_processor_class



	@property
	def 			__A							(       self		):
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,	UpperCAmelCase__			,	)
							return self.image_processor










def  UpperCamelCase						(       _A			:				str    )->       str:





						"""simple docstring"""





						A__							=       ""
						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						(       _A			:				str    )->       dict[str, str]:





						"""simple docstring"""





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





						"""simple docstring"""





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





						"""simple docstring"""





						A__							=       {v: k for k, v in cipher_map.items()}
						return "".join(rev_cipher_map.get(_A			,							_A    ) for ch in message.upper()    )
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						A__							=       input("Enter message to encode or decode: "    ).strip()
						A__							=       input("Enter keyword: "    ).strip()
						A__							=       input("Encipher or decipher? E/D:"    ).strip()[0].lower()
						try:
												A__							=       {"e": encipher, "d": decipher}[option]
						except KeyError:
												raise KeyError("invalid input option"    )
						A__							=       create_cipher_map(_A    )
						print(func(_A			,							_A    )    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





