


"""simple docstring"""


a_       							=    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"






def      a__ (				)	->	None:
				_A					  =			input("Enter message: "   )
				_A					  =			input("Enter key [alphanumeric]: "   )
				_A					  =			input("Encrypt/Decrypt [e/d]: "   )

				if mode.lower().startswith("e"   ):
								_A					  =			"encrypt"
								_A					  =			encrypt_message(__lowercase     ,			__lowercase   )
				elif mode.lower().startswith("d"   ):
								_A					  =			"decrypt"
								_A					  =			decrypt_message(__lowercase     ,			__lowercase   )

				print(f"""\n{mode.title()}ed message:"""   )
				print(__lowercase   )






def      a__ (				__lowercase     ,			__lowercase   )	->	str:
				return translate_message(__lowercase     ,			__lowercase     ,			"encrypt"   )






def      a__ (				__lowercase     ,			__lowercase   )	->	str:
				return translate_message(__lowercase     ,			__lowercase     ,			"decrypt"   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	str:
				_A					  =			[]
				_A					  =			0
				_A					  =			key.upper()

				for symbol in message:
								_A					  =			LETTERS.find(symbol.upper()   )
								if num != -1:
												if mode == "encrypt":
																num += LETTERS.find(key[key_index]   )
												elif mode == "decrypt":
																num -= LETTERS.find(key[key_index]   )

												num %= len(__lowercase   )

												if symbol.isupper():
																translated.append(LETTERS[num]   )
												elif symbol.islower():
																translated.append(LETTERS[num].lower()   )

												key_index += 1
												if key_index == len(__lowercase   ):
																_A					  =			0
								else:
												translated.append(__lowercase   )
				return "".join(__lowercase   )


if __name__ == "__main__":
		main()



"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      a__ (				__lowercase   )	->	Optional[int]:
				_A					  =			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				    "decoder.output_projection.weight",
				]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase="facebook/mbart-large-en-ro"     ,			__lowercase=False     ,			__lowercase=False   )	->	List[str]:
				_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )["model"]
				remove_ignore_keys_(__lowercase   )
				_A					  =			state_dict["encoder.embed_tokens.weight"].shape[0]

				_A					  =			MBartConfig.from_pretrained(__lowercase     ,			vocab_size=__lowercase   )
				if mbart_aa and finetuned:
								_A					  =			"relu"

				_A					  =			state_dict["decoder.embed_tokens.weight"]
				_A					  =			MBartForConditionalGeneration(__lowercase   )
				model.model.load_state_dict(__lowercase   )

				if finetuned:
								_A					  =			make_linear_from_emb(model.model.shared   )

				return model


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
		)
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument(
		    "--hf_config",
		    default="facebook/mbart-large-cc25",
		    type=str,
		    help="Which huggingface architecture to use: mbart-large",
		)
		parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
		parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
		a_       							=    parser.parse_args()
		a_       							=    convert_fairseq_mbart_checkpoint_from_disk(
		    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
		)
		model.save_pretrained(args.pytorch_dump_folder_path)