

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class  __lowerCAmelCase     (					pl.LightningModule							):




  def __init__(				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =model
        _lowercase	       =2
        _lowercase	       =nn.Linear(self.model.config.hidden_size							,   self.num_labels					)




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        pass
def      a      (			A__	:   str      ,   A__	:   str      ,   A__	:   str     )    ->      Any:





      """simple docstring"""



      _lowercase	       =LongformerModel.from_pretrained(_lowerCamelCase     )
      _lowercase	       =LightningModel(_lowerCamelCase     )

      _lowercase	       =torch.load(_lowerCamelCase      ,   map_location=torch.device('cpu'     )     )
      lightning_model.load_state_dict(ckpt['state_dict']     )

      # init longformer question answering model
      _lowercase	       =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__":
  lowercase_									=   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_									=   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"""





from __future__ import annotations







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						[True] * limit
  lowerCamelCase_         =						False
  lowerCamelCase_         =						False
  lowerCamelCase_         =						True

  for i in range(3		,				int(limit**0.5 + 1 )		,				2 ):
    lowerCamelCase_         =						i * 2
    while index < limit:
      lowerCamelCase_         =						False
      lowerCamelCase_         =						index + i

  lowerCamelCase_         =						[2]

  for i in range(3		,				_lowerCamelCase		,				2 ):
    if is_prime[i]:
      primes.append(_lowerCamelCase )

  return primes







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 1000000 )      ->				int:
  lowerCamelCase_         =						prime_sieve(_lowerCamelCase )
  lowerCamelCase_         =						0
  lowerCamelCase_         =						0

  for i in range(len(_lowerCamelCase ) ):
    for j in range(i + length		,				len(_lowerCamelCase ) ):
      lowerCamelCase_         =						sum(primes[i:j] )
      if sol >= ceiling:
        break

      if sol in primes:
        lowerCamelCase_         =						j - i
        lowerCamelCase_         =						sol

  return largest


if __name__ == "__main__":
  print(F'''{solution() = }''')




