




"""simple docstring"""

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class 				lowercase_       (     pl.LightningModule							):



 '''simple docstring'''

 def __init__( self       :       Optional[Any]    ,							_UpperCAmelCase       :       str		):
       super().__init__()
       _A					   =   model
       _A					   =   2
       _A					   =   nn.Linear(self.model.config.hidden_size    ,							self.num_labels		)



 def 			lowerCAmelCase_							( self       :       Optional[int]		):
       pass






def 						_snake_case     (					_snake_case							:					str				,  _snake_case							:					str				,  _snake_case							:					str							)    ->					str:






      '''simple docstring'''


      _A					   =   LongformerModel.from_pretrained(UpperCamelCase_							)
      _A					   =   LightningModel(UpperCamelCase_							)

      _A					   =   torch.load(UpperCamelCase_				,  map_location=torch.device('cpu'							)							)
      lightning_model.load_state_dict(ckpt['state_dict']							)

      # init longformer question answering model
      _A					   =   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					=			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					=			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 collections.abc import Sequence




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   return sum(c * (x**i) for i, c in enumerate(UpperCamelCase_))




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   __lowercase								=						0.0
   for coeff in reversed(UpperCamelCase_):
      __lowercase								=						result * x + coeff
   return result


if __name__ == "__main__":
   _a       =							(0.0, 0.0, 5.0, 9.3, 7.0)
   _a       =							10.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))




