'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return " ".join(input_str.split()[::-1]     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 			UpperCAmelCase  (						pl.LightningModule					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        lowercase__				:    List[str]  =					model
        lowercase__				:    Dict  =					2
        lowercase__				:    Any  =					nn.Linear(self.model.config.hidden_size	,   self.num_labels )


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

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

 # 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(UpperCAmelCase     )

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


if __name__ == "__main__":
  __a: 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."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

