

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 						_A     (				pl.LightningModule				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      snake_case							:		Dict        =		model
      snake_case							:		Optional[int]        =		2
      snake_case							:		Optional[Any]        =		nn.Linear(self.model.config.hidden_size   ,self.num_labels    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      pass







def       lowercase					(	__A :					str ,    __A :					str ,    __A :					str		)				->			Optional[Any]:





    '''simple docstring'''




    snake_case							:		Optional[Any]        =		LongformerModel.from_pretrained(__A		)
    snake_case							:		Tuple        =		LightningModel(__A		)

    snake_case							:		Optional[int]        =		torch.load(__A ,    map_location=torch.device("""cpu"""		)		)
    lightning_model.load_state_dict(ckpt["""state_dict"""]		)

    # init longformer question answering model
    snake_case							:		Dict        =		LongformerForQuestionAnswering.from_pretrained(__A		)

    # 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(__A		)

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


if __name__ == "__main__":
     __lowercase     : str       =						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.'''
     )
     __lowercase     : List[str]       =						parser.parse_args()
     convert_longformer_qa_checkpoint_to_pytorch(
         args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
     )











UpperCAmelCase__      			=						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def     a_   ()				->						None:



					"""simple docstring"""
					__a					:						Dict      =					input("Enter message: "      )
					__a					:						Dict      =					input("Enter key [alphanumeric]: "      )
					__a					:						str      =					input("Encrypt/Decrypt [e/d]: "      )

					if mode.lower().startswith("e"      ):
										__a					:						List[str]      =					"encrypt"
										__a					:						Union[str, Any]      =					encrypt_message(__A				,       __A      )
					elif mode.lower().startswith("d"      ):
										__a					:						int      =					"decrypt"
										__a					:						List[str]      =					decrypt_message(__A				,       __A      )

					print(f'\n{mode.title()}ed message:'      )
					print(__A      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "encrypt"      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "decrypt"      )







def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					[]
					__a					:						Tuple      =					0
					__a					:						Dict      =					key.upper()

					for symbol in message:
										__a					:						Dict      =					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(__A      )

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

															key_index += 1
															if key_index == len(__A      ):
																				__a					:						List[str]      =					0
										else:
															translated.append(__A      )
					return "".join(__A      )


if __name__ == "__main__":
			main()





