



import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= [
					    "encoder.version",
					    "decoder.version",
					    "model.encoder.version",
					    "model.decoder.version",
					    "_float_tensor",
					    "decoder.output_projection.weight",
					]
					for k in ignore_keys:
										state_dict.pop(_lowerCAmelCase ,						_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_	,     UpperCamelCase_			= emb.weight.shape
					UpperCamelCase_			= nn.Linear(_lowerCAmelCase ,						_lowerCAmelCase ,						bias=_lowerCAmelCase)
					UpperCamelCase_			= emb.weight.data
					return lin_layer



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase="uclanlp/plbart-base" ,						_lowerCAmelCase=False ,						_lowerCAmelCase=False):
					UpperCamelCase_			= torch.load(_lowerCAmelCase ,						map_location="cpu")["model"]
					remove_ignore_keys_(_lowerCAmelCase)
					UpperCamelCase_			= state_dict["encoder.embed_tokens.weight"].shape[0]

					UpperCamelCase_			= PLBartConfig.from_pretrained(_lowerCAmelCase ,						vocab_size=_lowerCAmelCase)

					UpperCamelCase_			= state_dict["decoder.embed_tokens.weight"]
					if not classification:
										UpperCamelCase_			= PLBartForConditionalGeneration(_lowerCAmelCase)
										model.model.load_state_dict(_lowerCAmelCase)
										if finetuned:
															UpperCamelCase_			= make_linear_from_emb(model.model.shared)

					else:
										UpperCamelCase_			= {}
										for key, value in state_dict.copy().items():
															if key.startswith("classification_heads.sentence_classification_head"):
																				UpperCamelCase_			= value
																				state_dict.pop(_lowerCAmelCase)
										UpperCamelCase_			= PLBartForSequenceClassification(_lowerCAmelCase)
										model.model.load_state_dict(_lowerCAmelCase)
										model.classification_head.load_state_dict(_lowerCAmelCase)

					return model


if __name__ == "__main__":
	UpperCAmelCase :				int							=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""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="""uclanlp/plbart-base""",
	    type=str,
	    help="""Which huggingface architecture to use: plbart-base""",
	)
	parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
	parser.add_argument(
	    """--classification""", action="""store_true""", help="""whether the model is a classification checkpoint"""
	)
	UpperCAmelCase :				List[Any]							=parser.parse_args()
	UpperCAmelCase :				Optional[int]							=convert_fairseq_plbart_checkpoint_from_disk(
	    args.fairseq_path,
	    hf_config_path=args.hf_config,
	    finetuned=args.finetuned,
	    classification=args.classification,
	)
	model.save_pretrained(args.pytorch_dump_folder_path)







"""simple docstring"""


a__       :  Optional[Any]       		=					{
    """A""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """B""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """C""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """D""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """E""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """F""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """G""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """H""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """I""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """J""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """K""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """L""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """M""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """N""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """O""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """P""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """Q""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """R""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """S""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """T""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """U""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """V""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """W""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """X""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """Y""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
    """Z""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
}





def 				a__      (	_UpperCAmelCase						)     ->	list[tuple[str, str]]:

 '''simple docstring'''






 return [alphabet[char] for char in key.upper()]





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__     				=					""
 snake_case__     				=					0
 snake_case__     				=					generate_table(_UpperCAmelCase						)
 for char in words.upper():
  cipher += get_opponent(table[count]						,  _UpperCAmelCase						)
  snake_case__     				=					(count + 1) % len(_UpperCAmelCase						)
 return cipher





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 return encrypt(_UpperCAmelCase						,  _UpperCAmelCase						)





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	tuple[int, int]:

 '''simple docstring'''






 snake_case__     				=					0 if char in table[0] else 1
 snake_case__     				=					table[row].index(_UpperCAmelCase						)
 return row, col





def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	str:

 '''simple docstring'''






 snake_case__				, snake_case__     				=					get_position(_UpperCAmelCase						,  char.upper()						)
 if row == 1:
  return table[0][col]
 else:
  return table[1][col] if row == 0 else char


if __name__ == "__main__":
   import doctest

   doctest.testmod()  # Fist ensure that all our tests are passing...
   a__       :  int       		=					input("""Enter key: """).strip()
   a__       :  Optional[int]       		=					input("""Enter text to encrypt: """).strip()
   a__       :  Dict       		=					encrypt(key, text)

   print(f'''Encrypted: {cipher_text}''')
   print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')

