





"""simple docstring"""




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class 			_a						(				pl.LightningModule    ):






				def __init__(			self		:   Tuple,			lowerCAmelCase__		:   Optional[Any]							)     ->				Tuple:




										'''simple docstring'''



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






				def     snake_case     (			self		:   int							)     ->				Tuple:




										'''simple docstring'''



										pass





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):






						"""simple docstring"""




						_UpperCamelCase      : int         =   LongformerModel.from_pretrained(_lowercase       )
						_UpperCamelCase      : Union[str, Any]         =   LightningModel(_lowercase       )

						_UpperCamelCase      : Dict         =   torch.load(_lowercase	,						map_location=torch.device('''cpu'''       )       )
						lightning_model.load_state_dict(ckpt['''state_dict''']       )

						# init longformer question answering model
						_UpperCamelCase      : str         =   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__":
						UpperCamelCase_									=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_									=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

from random import choice





def 	a_    (							_lowercase       ):
						return choice(_lowercase       )





def 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   random_pivot(_lowercase       )

						# partition based on pivot
						# linear time
						_UpperCamelCase      : Union[str, Any]         =   [e for e in lst if e < pivot]
						_UpperCamelCase      : int         =   [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(_lowercase       ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(_lowercase       ) < k - 1:
												return kth_number(_lowercase	,						k - len(_lowercase       ) - 1       )
						# pivot is in elements smaller than k
						else:
												return kth_number(_lowercase	,						_lowercase       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



