

'''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       :						List[str]		,			lowerCAmelCase_       :						Optional[Any] )			->		Optional[int]:





									'''simple docstring'''
									super().__init__()
									A__     :   Any      =model
									A__     :   Any      =2
									A__     :   Optional[int]      =nn.Linear(self.model.config.hidden_size		,			self.num_labels )



			def lowercase__						(      self       :						List[str] )			->		Union[str, Any]:





									'''simple docstring'''
									pass


def 						__lowerCamelCase       (   __snake_case     :		Dict,		__snake_case     :		str,		__snake_case     :		str       )       ->							Any:






						"""simple docstring"""





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

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

						# init longformer question answering model
						A__     :   Optional[int]      =LongformerForQuestionAnswering.from_pretrained(lowercase__       )

						# 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(lowercase__       )

						print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}"       )


if __name__ == "__main__":
		__snake_case :	Optional[int]      						=			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.'
		)
		__snake_case :	List[str]      						=			parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)





from math import isqrt







def        _lowerCamelCase(	lowercase__	)						->	bool:

		'''simple docstring'''







		return all(number % divisor != 0 for divisor in range(2   , isqrt(lowercase__	) + 1	)	)







def        _lowerCamelCase(	lowercase__ = 1_0**6	)						->	int:

		'''simple docstring'''







		__lowercase=			0
		__lowercase=			1
		__lowercase=			7
		while prime_candidate < max_prime:
				primes_count += is_prime(lowercase__	)

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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






