






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
  )












import math

def      _lowerCamelCase	(						__A    :							int )      ->					int:

    if not isinstance(__A		,		__A ):
        _UpperCAmelCase				:      List[Any]			  =				f'''Input value of [number={number}] must be an integer'''
        raise TypeError(__A )

    if number < 1:
        _UpperCAmelCase				:      Optional[Any]			  =				f'''Input value of [number={number}] must be > 0'''
        raise ValueError(__A )
    elif number == 1:
        return 3
    elif number == 2:
        return 5
    else:
        _UpperCAmelCase				:      Any			  =				int(math.log(number // 3		,		2 ) ) + 2

        _UpperCAmelCase				:      Dict			  =				[3, 5]
        _UpperCAmelCase				:      Tuple			  =				2
        _UpperCAmelCase				:      Dict			  =				3
        for block in range(1		,		__A ):
            for _ in range(__A ):
                proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
                proth_index += 1
            increment *= 2

    return proth_list[number - 1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  for number in range(11):
    SCREAMING_SNAKE_CASE 							=						0
    try:
      SCREAMING_SNAKE_CASE 							=						proth(number)
    except ValueError:
      print(F'ValueError: there is no {number}th Proth number')
      continue

    print(F'The {number}th Proth number: {value}')




