

def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     list[int]:

	if length <= 0 or not isinstance(lowerCamelCase__	, lowerCamelCase__			):
		raise ValueError('Length must be a positive integer.'			)
	return [n * (2 * n - 1) for n in range(lowerCamelCase__			)]


if __name__ == "__main__":
					print(hexagonal_numbers(length=5))
					print(hexagonal_numbers(length=10))










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						:Union[str, Any]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[int]:
	# load longformer model from model identifier
	a										=							LongformerModel.from_pretrained(__lowerCamelCase    )
	a										=							LightningModel(__lowerCamelCase    )

	a										=							torch.load(__lowerCamelCase							,			map_location=torch.device("""cpu"""    )    )
	lightning_model.load_state_dict(ckpt["""state_dict"""]    )

	# init longformer question answering model
	a										=							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__":
		__UpperCamelCase					:       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."
		)
		__UpperCamelCase					:       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
		)

