






"""simple docstring"""





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 ,				snake_case_      )		->       Dict:
        super().__init__()
        UpperCamelCase__         =    model
        UpperCamelCase__         =    2
        UpperCamelCase__         =    nn.Linear(self.model.config.hidden_size ,				self.num_labels      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
        pass







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
     """simple docstring"""

     UpperCamelCase__         =    LongformerModel.from_pretrained(__snake_case					)
     UpperCamelCase__         =    LightningModel(__snake_case					)

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

     # init longformer question answering model
     UpperCamelCase__         =    LongformerForQuestionAnswering.from_pretrained(__snake_case					)

     # 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(__snake_case					)

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


if __name__ == "__main__":
  A__					: Tuple=	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__					: Union[str, Any]=	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 collections import defaultdict
from math import ceil, sqrt







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 1_00_00_00 ,	SCREAMING_SNAKE_CASE = 10					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    defaultdict(SCREAMING_SNAKE_CASE					)

    for outer_width in range(3 ,	(t_limit // 4) + 2					):
        if outer_width * outer_width > t_limit:
            UpperCamelCase__         =    max(
                ceil(sqrt(outer_width * outer_width - t_limit					)					) ,	1					)
        else:
            UpperCamelCase__         =    1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(SCREAMING_SNAKE_CASE ,	outer_width - 1 ,	2					):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10					)


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




