






"""simple docstring"""







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel


class    __UpperCAmelCase(       pl.LightningModule							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												super().__init__()
												lowercase__    :				int=	model
												lowercase__    :				Any=	2
												lowercase__    :				Union[str, Any]=	nn.Linear(self.model.config.hidden_size	,  self.num_labels      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass





def 						lowercase__(A ,		A ,		A						)    ->Any:




						"""simple docstring"""



						lowercase__    :				Optional[Any]=	LongformerModel.from_pretrained(A						)
						lowercase__    :				Optional[Any]=	LightningModel(A						)

						lowercase__    :				str=	torch.load(A ,		map_location=torch.device("cpu"						)						)
						lightning_model.load_state_dict(ckpt["state_dict"]						)

						# init longformer question answering model
						lowercase__    :				str=	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__":
			a			:    Optional[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			:    int				 =		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


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__=None      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	data
												lowercase__    :				Optional[Any]=	None




						def __repr__(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	[]
												lowercase__    :				Tuple=	self
												while temp:
																		string_rep.append(F'''{temp.data}'''      )
																		lowercase__    :				Optional[int]=	temp.next
												return "->".join(snake_case__      )





def 						lowercase__(A						)    ->Dict:




						"""simple docstring"""



						if not elements_list:
												raise Exception("The Elements List is empty"						)

						lowercase__    :				Optional[int]=	Node(elements_list[0]						)
						for i in range(1 ,		len(A						)						):
												lowercase__    :				Optional[Any]=	Node(elements_list[i]						)
												lowercase__    :				str=	current.next
						return head





def 						lowercase__(A						)    ->None:




						"""simple docstring"""



						if head_node is not None and isinstance(A ,		A						):
												print_reverse(head_node.next						)
												print(head_node.data						)





def 						lowercase__()    ->str:




						"""simple docstring"""



						from doctest import testmod

						testmod()

						lowercase__    :				Optional[int]=	make_linked_list([14, 52, 14, 12, 43]						)
						print("Linked List:"						)
						print(A						)
						print("Elements in Reverse:"						)
						print_reverse(A						)


if __name__ == "__main__":
			main()






