


'''simple docstring'''
import random
import sys

__SCREAMING_SNAKE_CASE				:							str		       =		'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def 			a_							(  ):
							A_ 						=		input("Enter message: "   )
							A_ 						=		"LFWOAYUISVKMNXPBDCRJTQEGHZ"
							A_ 						=		input("Encrypt/Decrypt [e/d]: "   )

							check_valid_key(UpperCamelCase_   )

							if resp.lower().startswith("e"   ):
														A_ 						=		"encrypt"
														A_ 						=		encrypt_message(UpperCamelCase_	,     UpperCamelCase_   )
							elif resp.lower().startswith("d"   ):
														A_ 						=		"decrypt"
														A_ 						=		decrypt_message(UpperCamelCase_	,     UpperCamelCase_   )

							print(f"\n{mode.title()}ion: \n{translated}"   )







def 			a_							(  UpperCamelCase_   ):
							A_ 						=		list(UpperCamelCase_   )
							A_ 						=		list(UpperCamelCase_   )
							key_list.sort()
							letters_list.sort()

							if key_list != letters_list:
														sys.exit("Error in the key or symbol set."   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							return translate_message(UpperCamelCase_	,     UpperCamelCase_	,     "encrypt"   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							return translate_message(UpperCamelCase_	,     UpperCamelCase_	,     "decrypt"   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		""
							A_ 						=		LETTERS
							A_ 						=		key

							if mode == "decrypt":
														A_						,				A_ 						=		chars_b, chars_a

							for symbol in message:
														if symbol.upper() in chars_a:
																					A_ 						=		chars_a.find(symbol.upper()   )
																					if symbol.isupper():
																												translated += chars_b[sym_index].upper()
																					else:
																												translated += chars_b[sym_index].lower()
														else:
																					translated += symbol

							return translated







def 			a_							(  ):
							A_ 						=		list(UpperCamelCase_   )
							random.shuffle(UpperCamelCase_   )
							return "".join(UpperCamelCase_   )


if __name__ == "__main__":
							main()









'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					int      	=["image_processor", "tokenizer"]
						_UpperCAmelCase		:					Dict      	="EfficientNetImageProcessor"
						_UpperCAmelCase		:					Union[str, Any]      	=("BertTokenizer", "BertTokenizerFast")






						def __init__(     self       :			List[Any]   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			int  ):
													super().__init__(lowerCAmelCase   ,     lowerCAmelCase  )






						def __call__(     self       :			str   ,     lowerCAmelCase       :			Tuple=None   ,     lowerCAmelCase       :			List[Any]=None   ,     lowerCAmelCase       :			Optional[int]="max_length"   ,     lowerCAmelCase       :			Tuple=64   ,     lowerCAmelCase       :			Dict=None   ,     **lowerCAmelCase       :			Any  ):
													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(
																				    lowerCAmelCase   ,     padding=lowerCAmelCase   ,     max_length=lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													if images is not None:
																				A_ 						=		self.image_processor(lowerCAmelCase   ,     return_tensors=lowerCAmelCase   ,     **lowerCAmelCase  )

													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(**lowerCAmelCase  )   ,     tensor_type=lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     *lowerCAmelCase       :			Optional[int]   ,     **lowerCAmelCase       :			str  ):
													return self.tokenizer.batch_decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str   ,     *lowerCAmelCase       :			Any   ,     **lowerCAmelCase       :			Dict  ):
													return self.tokenizer.decode(*lowerCAmelCase   ,     **lowerCAmelCase  )






						@property
						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		self.tokenizer.model_input_names
													A_ 						=		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





