






import math

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Dict )      ->					Any:



    if 0 not in (x, y):
        # We use the relation x^y = y*log10(x), where 10 is the base.
        return y * math.logaa(__A )
    else:
        if x == 0:  # 0 raised to any number is 0
            return 0
        elif y == 0:
            return 1  # any number raised to 0 is 1
    raise AssertionError('''This should never happen''' )


if __name__ == "__main__":  # Main function
  # Read two numbers from input and typecast them to int using map function.
  # Here x is the base and y is the power.
  SCREAMING_SNAKE_CASE     :   Optional[int] 							=						"Enter the base and the power separated by a comma: "
  SCREAMING_SNAKE_CASE     :   str 							=						map(int, input(prompt).split(','))
  SCREAMING_SNAKE_CASE     :   Dict 							=						map(int, input(prompt).split(','))

  # We find the log of each number, using the function res(), which takes two
  # arguments.
  SCREAMING_SNAKE_CASE     :   Optional[int] 							=						res(xa, ya)
  SCREAMING_SNAKE_CASE     :   List[str] 							=						res(xa, ya)

  # We check for the largest number
  if resa > resa:
    print('Largest number is', xa, '^', ya)
  elif resa > resa:
    print('Largest number is', xa, '^', ya)
  else:
    print('Both are equal')












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							,  _A) ->		List[str]:

     """simple docstring"""



     super().__init__()
     _UpperCAmelCase				:      Dict			  =				model
     _UpperCAmelCase				:      Union[str, Any]			  =				2
     _UpperCAmelCase				:      List[Any]			  =				nn.Linear(self.model.config.hidden_size							,  self.num_labels)


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     pass



def      _lowerCamelCase	(						__A    :							str		,		__A    :							str		,		__A    :							str )      ->					List[str]:
    # load longformer model from model identifier
    _UpperCAmelCase				:      Optional[Any]			  =				LongformerModel.from_pretrained(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				LightningModel(__A )

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

    # init longformer question answering model
    _UpperCAmelCase				:      int			  =				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__":
  SCREAMING_SNAKE_CASE 							=						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.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )




