



'''simple docstring'''






import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 							lowercase       (  pl.LightningModule				):


 """simple docstring"""


 def __init__(							self  ,a_							)		->   Optional[Any]:
        super().__init__()
        _UpperCAmelCase  :						Optional[int]			     = model
        _UpperCAmelCase  :						Tuple			     = 2
        _UpperCAmelCase  :						Dict			     = nn.Linear(self.model.config.hidden_size  ,self.num_labels							)







 def _snake_case (							self							)		->   Union[str, Any]:
        pass







def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = LongformerModel.from_pretrained(__lowerCamelCase		)
       _UpperCAmelCase  :						str			     = LightningModel(__lowerCamelCase		)

       _UpperCAmelCase  :						List[str]			     = torch.load(__lowerCamelCase       ,   map_location=torch.device("""cpu"""		)		)
       lightning_model.load_state_dict(ckpt["""state_dict"""]		)

       # init longformer question answering model
       _UpperCAmelCase  :						Dict			     = 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__":
   A_							: List[Any]						 =	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_							: List[Any]						 =	parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )









def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       str:
	if not isinstance(__lowerCamelCase							,			__lowerCamelCase    ):
		raise ValueError("""iterations must be defined as integers"""    )
	if not isinstance(__lowerCamelCase							,			__lowerCamelCase    ) or not number >= 1:
		raise ValueError(
		    """starting number must be
                         and integer and be more than 0"""    )
	if not iterations >= 1:
		raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz"""    )

	a										=							""""""
	while number <= iterations:
		if number % 3 == 0:
			out += "Fizz"
		if number % 5 == 0:
			out += "Buzz"
		if 0 not in (number % 3, number % 5):
			out += str(__lowerCamelCase    )

		# print(out)
		number += 1
		out += " "
	return out


if __name__ == "__main__":
		import doctest

		doctest.testmod()

