import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 							SCREAMING_SNAKE_CASE__						(    pl.LightningModule						):





					"""simple docstring"""






					def __init__( self					:  Any							,		__A					:  Any	):
						super().__init__()
						snake_case__			:				int       =  model
						snake_case__			:				int       =  2
						snake_case__			:				Optional[Any]       =  nn.Linear(self.model.config.hidden_size							,		self.num_labels	)






					def 	_lowercase					( self					:  Tuple	):
						pass







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Dict					,  snake_case_ :							Optional[Any]					,  snake_case_ :							str       ):
	snake_case__			:				str       =  LongformerModel.from_pretrained(a__       )
	snake_case__			:				Tuple       =  LightningModel(a__       )

	snake_case__			:				Tuple       =  torch.load(a__					,  map_location=torch.device("cpu"       )       )
	lightning_model.load_state_dict(ckpt["state_dict"]       )

	# init longformer question answering model
	snake_case__			:				List[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__":
	__lowerCamelCase :      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."""
	)
	__lowerCamelCase :      Optional[int]										=				parser.parse_args()
	convert_longformer_qa_checkpoint_to_pytorch(
	    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
	)











import sys

__lowerCamelCase :      List[str]										=				(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str = N       ):
	snake_case__			:				Any       =  -sys.maxsize - 1
	for i in range(len(snake_case_       ) - 12       ):
		snake_case__			:				Tuple       =  1
		for j in range(13       ):
			product *= int(n[i + j]       )
		if product > largest_product:
			snake_case__			:				Dict       =  product
	return largest_product


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



