



import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class 						lowercase		(					pl.LightningModule):






	"""simple docstring"""


	def __init__(      self      :		List[str]		,    __UpperCAmelCase      :		str	)     ->					List[Any]:
							super().__init__()
							UpperCAmelCase_=  model
							UpperCAmelCase_=  2
							UpperCAmelCase_=  nn.Linear(self.model.config.hidden_size		,    self.num_labels	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Dict	)     ->					Tuple:
							pass



def 			__a	(       lowerCAmelCase_  :   str     ,lowerCAmelCase_  :   str     ,lowerCAmelCase_  :   str							) ->					Dict:






						'''simple docstring'''



						UpperCAmelCase_=  LongformerModel.from_pretrained(__lowerCamelCase							)
						UpperCAmelCase_=  LightningModel(__lowerCamelCase							)

						UpperCAmelCase_=  torch.load(__lowerCamelCase     ,map_location=torch.device("""cpu"""							)							)
						lightning_model.load_state_dict(ckpt["""state_dict"""]							)

						# init longformer question answering model
						UpperCAmelCase_=  LongformerForQuestionAnswering.from_pretrained(__lowerCamelCase							)

						# transfer weights
						longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()							)
						longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()							)
						longformer_for_qa.eval()

						# save model
						longformer_for_qa.save_pretrained(__lowerCamelCase							)

						print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""							)


if __name__ == "__main__":
	__A							       =		argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--longformer_model''',
	    default=None,
	    type=str,
	    required=True,
	    help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
	)
	parser.add_argument(
	    '''--longformer_question_answering_ckpt_path''',
	    default=None,
	    type=str,
	    required=True,
	    help='''Path the official PyTorch Lightning Checkpoint.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	__A							       =		parser.parse_args()
	convert_longformer_qa_checkpoint_to_pytorch(
	    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
	)








'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =''
   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 lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =[chr(i + 6_5				) for i in range(2_6				)]
   # Remove duplicate characters from key
   _UpperCAmelCase  :  Any				       =remove_duplicates(key.upper()				)
   _UpperCAmelCase  :  Optional[Any]				       =len(__lowerCamelCase				)
   # First fill cipher with key characters
   _UpperCAmelCase  :  List[Any]				       ={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				)			,       2_6				):
      _UpperCAmelCase  :  Union[str, Any]				       =alphabet[i - offset]
      # Ensure we are not mapping letters to letters previously mapped
      while char in key:
         offset -= 1
         _UpperCAmelCase  :  str				       =alphabet[i - offset]
      _UpperCAmelCase  :  Any				       =char
   return cipher_alphabet





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   return "".join(cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       ={v: k for k, v in cipher_map.items()}
   return "".join(rev_cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =input('Enter message to encode or decode: '				).strip()
   _UpperCAmelCase  :  int				       =input('Enter keyword: '				).strip()
   _UpperCAmelCase  :  int				       =input('Encipher or decipher? E/D:'				).strip()[0].lower()
   try:
      _UpperCAmelCase  :  Tuple				       ={'e': encipher, 'd': decipher}[option]
   except KeyError:
      raise KeyError('invalid input option'				)
   _UpperCAmelCase  :  List[str]				       =create_cipher_map(__lowerCamelCase				)
   print(func(__lowerCamelCase			,       __lowerCamelCase				)				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

