
"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 				lowerCAmelCase_				(    pl.LightningModule    ):



						'''simple docstring'''


						def __init__( self     :      Any  ,A_     :      Any      )     ->   Dict:
							super().__init__()
							A						   =   model
							A						   =   2
							A						   =   nn.Linear(self.model.config.hidden_size  ,self.num_labels      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
							pass






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str	,      snake_case__   :     str     ):
	A						   =   LongformerModel.from_pretrained(__snake_case     )
	A						   =   LightningModel(__snake_case     )

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

	# init longformer question answering model
	A						   =   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__":
		_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"""


def       _snake_case				(		snake_case__   :     list	,      snake_case__   :     list	,      snake_case__   :     int     ):
	A						   =   len(snake_case__     )
	A						   =   [[0] * n for i in range(snake_case__     )]
	for i in range(snake_case__     ):
		A						   =   y_points[i]

	for i in range(2	,      snake_case__     ):
		for j in range(snake_case__	,      snake_case__     ):
			A						   =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()